Insights & Resources – S7Clear Immovable Driven

Partnership at heart of consulting companies

S7Clear® — Thu, 23 Mar 2023 21:47:52 +0000

Technological progress and innovation are the linchpins of fintech development and will continue to drive disruptive business models in financial services. According to S7Clear analysis, seven key technologies will drive fintech development and shape the competitive landscape of finance over the next decade:

Successful management consulting firms generally operate under a partnership structure. Recently, partnership as an organizational arrangement has also become popular in Asia, as some corporations claimed to have adopted it and some others are considering it.

In contrast to its novelty in Asia, the partnership has been in practice in the West for a long time, especially among professional service firms like law, accounting, and consulting. After more than 100 years of development, the consulting profession has produced a small number of sustainably successful global firms. The partnership is their common underpinning.

Although many Asian consulting firms also claim to be a partnership, their actual way of conduct is actually quite different from the international modus operandi.

Partners play a dual role. While collectively they own the firm as shareholders, they are also employees. The typical course of a consulting career follows a structured path from bottom to top. MBA graduates generally start as associates, transitioning into senior associates after two or three years of strong performance and getting promoted to principals in another two to three years.

Principals become eligible for election to the partnership after another two to three years in the role. In the United States, partnerships are legally regulated, with partners charged with unlimited liability. To mitigate related risks, many professional service organizations register for limited corporation status while maintaining the partnership internally.

An MBA graduate usually takes six to 10 years to become a partner in a global consulting firm. Partner selection is a rigorous process, typically starting with local country (or practice areas) partners selecting and nominating candidates (often principals with a strong track record).

Each candidate will be assigned to an assessor from another office to help evaluate the individual on multiple dimensions. These include people development, client and market development, quality of work and partnership, and a strong demonstration of adherence to core leadership values and partnership spirit; where motivation is not primarily driven by dollars and cents. Finally, financial metrics measure the ability to bring in revenues and to deliver against a firm’s financial targets.

These dimensions are assessed using a 360-degree approach, with feedback from both internal and external parties. The committee then evaluates the readiness of the candidate based on this data. Upon completion of the evaluation, the committee submits a list of candidates to the board of directors for approval to be appointed as partners.

Objectivity is key for meaningful assessment. Top consulting firms have an international partner appraisal committee, using a peer review process where partners from different regional offices or practice areas (most often not knowing each other, at least not very well) assess one another based on facts gathered from interviews.

Although some Asian corporations and many local consulting firms claim to have adopted a partnership structure, many are not doing it right. A genuine partnership is not simply a label, but rather, at the heart of the practice lies a set of intangible and robust core values, which makes real partnerships enduring.

IoT Solutions for Fintech Providers

S7Clear® — Tue, 21 Mar 2023 17:50:27 +0000

Applications for IoT in fintech and how financial companies can use this trend to gain a competitive edge over their competition.

With a $25 billion growth outlook in the next couple of years, the Internet of Things will soon force fintech companies to rethink their operations. Among the spheres that will fall victim to the most disruptive changes are payments, security, customer experience, infrastructure and internal operations.

In this article, we will explore the existing Applications for IoT in fintech and how financial companies can use this trend to gain a competitive edge over their competition.

The Benefits of Fintech and IoT Integration
The primary benefit of fintech IoT is instant data collection and processing. IoT sensors in finance represent a source of valuable data – insights on users’ behavior and preferences. Using this data, fintech companies and service providers can offer:

increased customer service better risk management and decision making
improved security Moreover, smart wearable devices provide increased mobility and convenience.

The internal business application of fintech IoT solutions allows providers to improve their business efficiency through process automation.

The Internet of Finance
How Can We Use IoT in Finance?
Retail banking is one of the spheres that can benefit from fintech IoT adoption. Just like brick-and-mortar retailers, they can use smart devices to improve their customer service and streamline internal operations.

Citibank was among the first to adopt Beacons in their operations, allowing customers (with the use of their smartphones) to unlock the doors to their ATM lobbies during off hours (instead of with a card).

Here are some more ideas on how banks can use IoT solutions to offer enhanced customer services:

Contextual service
Beacons and other context-aware smart devices can help bankers improve their customer service. For example, they can welcome their customers as they approach or enter the branch or send them personalized messages.

Immediate support
Alternatively, smartphones can also be used as Beacons to notify the account managers when a customer enters the branch.

Indoor navigation
One of the primary Applications for Beacons (indoor navigation), can also come in handy for helping customers find their way around the branch. Thus, customers can specify the reason for their visit and get instantly assigned to the right specialist.

Queue management
In addition to helping customers navigate on-site and find the right representative to speak to, smart devices can also serve as an electronic ticket. It can provide information about the specialist they are assigned to as well as instantly notify them when they are up next.

IoT-based security solutions can be a gamechanger for fintech providers, making financial operations safe and transparent. A number of wearable devices offer biometric authentication methods for wireless payments and money transfers. For instance, Nymi offers an enterprise-grade security solution based on biometric data.

Payments and commerce represent another promising IoT sphere in fintech. In addition to improving the security of such transactions, wireless payments are faster and more convenient, compared to traditional card operations.

Wireless transactions using smart devices can serve as a foundation for self-checkout services across a number of business domains. A similar concept is being developed by Amazon in its self-checkout stores.

The use of IoT devices in insurance is often overlooked. Yet, they can help service providers implement dynamic pricing models – the so-called usage-based insurance. Thus, some providers are already testing smart devices that track vehicle performance and maintenance to adjust their plans accordingly.

Similar solutions can be applied to real estate and even health insurance.

How to Implement IoT Solutions in Fintech

The Development Process and Pitfalls to Consider. The main challenge in using IoT sensors in finance lies in the fact that you need to take into account both the specifics of IoT development and the pitfalls of starting a fintech company.

As a result, the implementation process should include the following aspects:

Concept and Audience
As with any other product, be it an IoT solution, fintech startup, or something completely different, your first step should be to validate the concept and conduct thorough research on your audience. It is always better to know if your product will find its niche before you invest thousands of dollars and months of hard work into building it.

That is why you need to verify the demand for your product and define its unique value proposition. As soon as you see that there is a specific need that can be catered to, you can move on and build your solution.

Data
When building IoT solutions for fintech, you need to, first of all, define the type of data you will collect and its processing methods. Storage security is another question you need to keep in mind (especially now, as the data protection policies are stricter and more explicit than ever with the introduction of GDPR).

One of the best practices to comply with the regulations is to limit the collection of data that is not absolutely necessary for your business and set clear requirements for data processing and storage within your organization.

Hardware Design
Designing custom hardware can be a challenge. Not only do you need to come up with a design and set up the manufacturing processes, you will also need to convince your users buy it (in addition to the smart devices they are already using). Although this approach is a surefire way to monetize your product, it requires significant marketing efforts.

That’s why the best choice is to consider integrating with one of the existing solutions (wearables, beacons, or enterprise IoT solutions). However, if there are no hardware solutions available that can provide you with the collection of required data and a user experience that you need, then you should invest in building your own custom device.

Software Development
IoT software development is no easy task. A typical solution will consist of up to 7 software layers, including communication protocols, APIs, cloud infrastructure, data collection and analysis algorithms, and of course the consumer-facing app itself.

Moreover, your technology choice, as well as the target platform and inner architecture, should be selected based on the type of product and its purpose.

Integration
At this stage, you should consider hardware and software integrations. Your device should work in perfect sync with the application and the backend infrastructure. If you already have an existing internal system, then make sure it works with the new product as well.

Moreover, there are dozens of third-party APIs and solutions you can integrate with. For example, you can add some tools for analytics, advertising, or customer support. You can even use third-party frameworks to personalize your user experience with smart suggestion algorithms.

Ongoing Improvements and Support
It is a common misbelief that once you’ve brought your solution to market, you can sit back, relax, and start counting the revenue. Not only should you start marketing your product, you should also work on making it better, based on the initial users’ feedback.

Constant support and reliability are especially important for fintech products. If there is a system breakdown, you risk losing your clients’ trust (and in some cases, the outcome might be even more serious).

Looking into the Future of Finternet
Financial service providers have always been slow to innovate. Decades-old organizations need to rethink their operations and rebuild them from the ground up if they want to compete with the industry newcomers.

As IoT (and, most importantly, the data it provides) is an undeniable advantage in this arms race, we will undoubtedly see more Applications for fintech and IoT integration very soon.

Yet, IoT development is a complex and technically challenging undertaking. That is why it is important to have trusted technology professionals by your side. Regardless of whether you decide to grow the expertise in-house or hire an outside technology provider, deep domain expertise in IoT, as well as solid understanding of financial technologies, is a must.

10 innovative FinTech business models

S7Clear® — Tue, 21 Mar 2023 16:32:10 +0000

With heaps of venture capitalist money flowing into the FinTech ecosystem, “challenger” banks are threatening to wipe out banking behemoths faster than Blackberry was taken out of the cellular telephone market.

Let’s look at 10 innovative FinTech business models that are leading the path of disruption.

1. Alternative credit scoring

Many self-employed people with a steady source of income do not pass conventional bank loan screenings due to strict and outdated credit scoring criteria. Credit rating FinTech companies such as Nova Credit are taking a new approach by considering alternative data points like social signals and percentile scoring amongst similar borrower groups. All these qualitative factors combined with an intelligent and self-learning algorithm can lead to better lending decisions over time. For example, if there is a way to determine negative profiles based on social presence before loan disbursement, then a lender can avoid having to deal with loan recovery.

2. Alternative insurance underwriting

In today’s world, two individuals with the same weight and height, both non-smokers and who don’t drink alcohol will be given the same life insurance premium. However, one person might be an exercise freak, while the other might be a couch potato and more likely to die of diabetes. These faulty premium calculations happen because of averaging out (called normalizing in actuarial terms) as risk premiums currently don’t account for factors that aren’t quantifiable.

As with alternative credit scoring, FinTech companies such as Carpe Data are building variable premium computing mechanisms with alternative data points such as social signals, lifestyle, and medical history. Combined with intelligent and self-learning algorithms, these InsureTech companies can determine whether or not to give insurance, provide different terms and conditions, and offer alternative payment options (for example, co-pay options).

3. Transaction delivery

Data is the new oil, and managing it better can give immense insights into the needs and wants of the customer. FinTech startups in the transaction delivery space are creating free products, such as expense management apps, in order to collect customer data and then cross-pollinate that data with the rest of the group to map the potential of the customer to pay premiums, invest in real estate, buy mutual funds, etc. The business model involved in these types of FinTech companies is commission based, for example, on reselling third party financial products.

4. Peer-to-peer lending

Peer-to-peer (P2P) lending is when an individual borrows money from other individuals. Similarly, peer-to-business (P2B) lending is when a business borrows money from one or multiple individuals. These lending models are making it easier for investors to get better returns than those offered in debt markets by giving their money to pre-approved and vetted borrowers. FinTech companies such as Funding Circle create platforms to match borrowers with lenders and usually take a fee from the borrower’s repayment.

5. Small ticket loans

Banks and other lenders typically don’t want to underwrite smaller ticket loans because of the low margins and high costs involved in setting up and recovering them. FinTech companies in this slice of the market (such as Affirm) are delivering impulse buy mechanisms (buy now & pay later, or BNPL) and one-click buy buttons on e-commerce websites to enable customers to buy quickly without having to enter any form of authentication or credit card details.

These loans will typically be underwritten at 0% interest so that almost anything can be purchased outright with the option to pay in instalments. How is the money made? By sharing customer data with the original equipment manufacturer (OEMs) as they will benefit the most from the increased affordability of these devices. Combining this with algorithms that will determine customer demographics ensures highly-customized marketing offers. Think of sharing of your data with them as the interest on the loan.

6. Payment Gateways

Payment gateways are platforms that enable shoppers to pay for a product or service on a merchant’s website. Today, there are countless payment methods such as debit cards, credit cards, digital wallets, and cryptocurrencies. Typically, banks charge enormous fees to handle transactions from all these different methods, but FinTech companies are integrating all of these payment methods into convenient apps that online merchants can easily afford and integrate on their website. Typical users of these payment apps would be businesses selling either their physical products or services to end users, ex. Stripe, Alipay, iZettle.

7. Digital wallets

Digital wallets can be seen as a combination between a no-frills bank account and a payment gateway. With this business model, a user can pre-load a certain amount of virtual money into their wallets and use this virtual money to make either online or offline transactions with merchants who accept digital wallets as a payment mechanism.

A digital wallet business model typically involves giving users the convenience of making payments for a small fee that is typically charged to businesses in the form of a merchant discount rate (MDR) and via the float that they would make on the money lying unpaid in customer/business accounts. Typical end users of wallets would be businesses selling either their physical products or services in stores to end users, for example Venmo, Square Cash, Google Wallet, etc.

8. Asset Management

Ever heard of buying stocks or mutual funds without having to pay a commission fee? FinTech companies like Robinhood are enabling investors to trade for free in exchange for their data. They forward this data to high frequency traders who can then influence the price of the asset. Even though the investor might pay a slightly higher price for their asset, the difference between the amount they save from trading fees and the slight increase in price is still positive.

9. Digital banking

Imagine your traditional brick and mortar bank going completely online — no physical office, no bank tellers, no mail. Challenger banks such as N26 are offering no-frills individual and business bank accounts through a complete digital infrastructure. The business model here is almost identical to that of a bank with physical branches except that with the huge cost savings in manpower and real estate, customers can greatly benefit from reduced rates.

10. Digital insurance

As with digital banks, FinTech companies operating in the insurance industry are taking all of the traditional services to the digital world. Offering life and health insurance with better underwriting practices, these FinTechs can price their premiums at variable rates depending on the customer, thereby offering aggressively cheaper coverage compared to traditional insurance companies. These types of insurance, together with personalized marketing, can create business possibilities that insurance companies have only begun to explore. Lemonade, for example, is operating in the house insurance space.

Hyper automation will replace manual work Hyper automation refers to the introduction of AI, deep learning, event-driven software, Robotic

Process Automation (RPA), and other technologies and tools that improve decision-making efficiency and work automation capabilities.

RPA, which makes it easy for companies to deploy software robots such as chatbots at scale, is already a major component of digital transformation, but technology is constantly enlarging its boundaries. RPA’s core function is to allocate the handling of workflow information and business interactions to robots, thereby automating and standardizing business execution. High repeatability, clear logic, and solid stability are the key criteria to validate RPA tech feasibility. In future, RPA will become more deeply integrated with AI, improving its effectiveness in dealing with more complex business scenarios, and further streamlining financial service provision.

RPA is already at work across middle and back- office operations, automating financial processes and accounting reconciliation for financial institutions. Areas where RPA is being deployed include process automation for accounts receivable and payable, fund appropriation at shared finance and accounting service centers, work hour adjustment and review, automation of financial recording, reporting and treasury processes, and period-end accounting and settlement.

Replacing manual work with automation not only improves efficiency, but also reduces human errors, and allows businesses to respond to fluctuations in demand. While already well established among leading financial players, we expect RPA to penetrate more deeply throughout the industry. Accounts payable processes, for instance, have the potential to be 60 percent automated using robots that mirror human actions for basic paperwork and decision-making.

Unlocking future competitiveness

These key technologies and trends are becoming increasingly intertwined and integrated, giving massive impetus to fintech and financial industry innovation. As it stands, it is niche financialsub-sectors that are most adept at harnessing technological innovations to launch applications, generate value, and shape the competitive landscape. In future, traditional financial institutions will need to bring their considerable resources to bear to stay on top of the gathering wave of financial industry disruption.

Seven technologies shaping the future of fintech

S7Clear® — Mon, 20 Mar 2023 16:47:03 +0000

Artificial intelligence will drive massive value creation

S7Clear estimates that artificial intelligence (AI) can generate up to $1 trillion additional value for the global banking industry annually. Banks and other financial institutions are tipped to adopt an AI-first mindset that will better prepare them to resist encroachment onto their territory by expanding technology firms.

In financial services, automatic factor discovery, or the machine-based identification of the elements that drive outperformance, will become more prevalent, helping to hone financial modeling across the sector. As a key application of AI semantic representation, knowledge graphs and graph computing will also play a greater role. Their ability to assist in building associations and identifying patterns across complex financial networks, drawing on a wide range of often disparate data sources, will have far-reaching implications in the years to come.

Finally, analytics that incorporate enhanced privacy protections will foster minimal data usage, or the use of only relevant, necessary and appropriately sanitized information, in the training of financial models. These include federated learning, a form of decentralized machine learning that addresses the risk to privacy associated with centralizing datasets by bringing the computational power to the data, rather than vice versa. Advanced encryption, secure multi-party computing, zero-knowledge proofs, and other privacy-aware data analysis tools will drive a new frontier in consumer protection.

AI applications will penetrate the entire spectrum of financial industry operations across front, middle, and back offices. Customer-facing applications include tailored products, personalized user experience and analytics services, intelligent service robots and chat interfaces, market trackers, automated transactions and robo-advisors, as well as alternative credit ratings based on non- financial data, and facial recognition authentication. Middle-and-back office applications include smart processes, enhanced knowledge representation tools (epitomized by knowledge graphs), and natural language processing for fraud detection.

Many financial institutions still use AI in a sporadic and scattered way, often only applying the technology to specific use cases or verticals. But bank industry leaders are transforming their operations by systemically deploying AI across the entire lifecycle of their digital operations. Notably, the financial industry is coming to realize that algorithms are only as good as their data. Attention is turning to gaining competitive advantage from previously under-used customer behavior data collected via conventional operations. This will unlock the hitherto untapped potential of ecosystem-based financing, in which banks, insurers and other financial services firms partner with non-financial players to facilitate seamless customer experiences in areas outside their traditional remit.

For banks, the “AI-first” institution will yield greater operational efficiency via the extreme automation of manual tasks (a “zero-ops” mindset), and the replacement or augmentation of human decisions by advanced diagnostics. Improved operational performance will flow from the broad application of traditional and cutting-edge AI technologies, such as machine learning and facial recognition, to (near) real-time analysis of large and complex customer data sets. “AI-first” banks of the future will also adopt the speed and agility enjoyed by “digital native” companies and users. They will innovate at a rapid clip, releasing new features in days and weeks instead of months and years. Banks will also collaborate extensively with non-bank partners to offer new value propositions that are integrated across journeys, technology platforms, and data sets.

Blockchain will disrupt established financial protocols

Distributed Ledger Technology (DLT) allows the recording and sharing of data across multiple data stores, and for transactions and data to be recorded, shared, and synchronized across a distributed network of participants at the same time.

Some DTLs use blockchains to store and transmit their data, as well as cryptographic and algorithmic methods to record and synchronize the data across the network in an immutable manner.

DTL will increasingly underpin ecosystem financing by allowing the storage of financial transactions in multiple places at once. Increasingly, cross-chain technology, will facilitate blockchain interoperability, allowing chains established on different protocols to share and transmit data and value across tasks and industries, including payments processing and supply chain management.

Technologies such as smart contracts, zero- knowledge proof, and distributed data storage and exchange, which are key to existing fintech innovations such as digital wallets, digital assets, decentralized finance (DeFi), and non-fungible tokens (NFT), will continue to play a prominent role.

Moreover, traditional stakeholders, including institutional investors and funds, are gradually increasing the share of digital assets in their portfolios, broadening access to financing and elevating the potential of blockchain and DTL to disrupt established markets. For example, decentralized finance (DeFi), a form of blockchain- based finance that uses smart contracts to remove the need for a central intermediary, is taking off. The total locked-up value (TLV) of DeFi has surged by nearly 50 times in the past 10 months, with the sector now holding digital assets worth $2.1 trillion. The fact that digital asset exchanges earned about $15 billion in revenue in 2021 offers a further indication of blockchain’s mounting technological value.

DLT is also making a mark on government policymaking and regulation. According to a survey conducted by the Bank for International Settlements (BIS) in early 2021, about 60 percent of central banks said that they are testing or studying Central Bank Digital Currency (CBDC). The People’s Bank of China, for instance, has begun operational trials of a digital RMB effort based on permissioned DTL, paving the way for improved oversight of monetary policy and resource allocation at the macro level.

Other blockchain applications worthy of mention include:

Real-time transaction settlement: Banks are using smart contracts to settle the collateral and cash part of a transaction at the same Transaction processing, securities lending, and equity trades can also be settled on the blockchain to improve the efficiency and scalability of cross-border sales. Meanwhile, trading securities supported by digital collateral on the blockchain makes for more efficient, transparent, and secure capital management, as well as post-transaction equity settlement.
Digital asset support services: Institutional investors are seeking DLT capabilities, including tokenization for unlisted companies or private equity funds, spot exchange between established currencies and cryptocurrencies ondigital exchanges, and custody services such as key escrow encryption on behalf of customers.
Authentication ecosystems based on zero- knowledge proof: Customers are using agreed- to-share information from partner institutions to verify their identity online, face-to-face, or through phone calls, simplifying authentication procedures and offering streamlined access to health records and government services. Only information required for each specific transaction is shared, while all other data remains safely on the server of the trusted provider.
Decentralized finance (DeFi): Decentralized non-custodial applications can replace intermediaries by automatically generating deterministic (or “always valid”) This makes it possible to obtain loans, make investments, or trade financial products without relying on financial entities under centralized management. DeFi adopts deterministic smart contracts, which eliminate counterparty risks and cut out the costs associated with rent- seeking intermediaries, while improving market efficiency with real-time transparency.

DeFi based on blockchain technology is ushering in a new era of opportunity, disrupting established traditional value chains and structures. As financial policies and regulations adapt, DeFi is set to massively expand.

Cloud computing will liberate financial services players

S7Clear research shows that by 2030, cloud technology will account for EBITDA (earnings before interest, tax, depreciation and amortization) in excess of $1 trillion across the world’s top 500 companies. Our research shows that effective use of the cloud can increase the efficiency of migrated application development and maintenance by 38 percent; raise infrastructure cost efficiency by 29 percent; and reduce migrated applications’ downtime by ~57 percent, thus lowering costs associated with technical violations by 26 percent. At the same time, cloud can improve platform integrity through automated and embedded security processes and controls. Development, Security and Operations (DevSecOps), or the idea that security is a responsibility that can be actioned across an organization in step with the growth of its development and operations, is a primary example of a cloud-based feature that reduces technical risks through a consistent, cross-environmental technology stack.2

Financial institutions should be aware of three major forms of cloud services: public cloud, hybrid cloud, and private cloud. Public cloud means that the infrastructure is owned by cloud computing service providers, who sell cloud services to a wide range of organizations or the public. Hybrid cloud infrastructure is composed of two or more types of cloud (private, public) that are maintained independently, but connected by proprietary technology. Private cloud means that the infrastructure is built for an individual customer’s exclusive use, deployable in the company data centers, or via other hosting facilities.

Looking ahead, we have identified several relevant cloud-computing trends:

Edge computing and edge cloud are essential: Partition and development logic based on the relationship between edge devices, data centers, and the cloud is increasingly recognized in multiple Development of the edge cloud is accelerating as 5G communication drives new interactions and synergy across the internet of things (IoT), cloud computing, AI and other technologies in areas like new retail, healthcare, industrial parks, smart cities, and industrial IoT.
Cloud containers are stimulating innovation. Public cloud providers are actively pushing the implementation of container technology on cloud, allowing multiple workloads to run on a single operating system instance, and so reducing overheads and improving efficiency. This is driving innovation of cloud delivery models on the platform as a service (PaaS) layer. Cloud technology providers will increasingly focus on building platforms that incorporate container as a service (CaaS).
AI-cloud integration is on the rise: AI-cloud platform applications are proliferating in fields like image and audio search, driving advances in high-value areas such as medical image Deep learning will continue to improve services for a broader range of users via cloud platforms.

Cloud computing liberates financial companies from non-core businesses such as IT infrastructure and data centers, while enabling access to flexible storage and computing services at a lower cost. At the same time, the cloud is spawning new formats such as open banking and banking-as-a-service, shaking up the age-old relationship between customers and financial service providers.

Financial institutions will continue to rely on the cloud as they onboard more agile capabilities, and launch new businesses that require high responsiveness to market and customers, and flexible scalability. Meanwhile, the at-scale application of big data analytics will boost demand for cloud-based elastic computing, which allows computing resources to be dynamically adjusted to meet shifts in demand.

Banks will also recognize the potential to adopt cloud-based microservice architecture at scale in the next few years, where application programming interfaces (APIs) unlock machine- to-machine communication, and allow services to scale independently without needing to enlarge the coding base of the overall offering. The next generation of core banking applications will spur a microservice-driven architectural transformation in banking.

IoT will drive a new era of trust in finance

After years languishing on the lower slopes of the hype cycle, IoT is finally coming of age, with important ramifications for financial IoT systems are composed of three layers – perception and smart sensor systems, wireless communication networks, and application and operations support. On the sensor front, RFID labeling still has broad untapped potential to automate item identification and logistics management. IoT communication solutions are also expanding, casting a wider net for devices to communicate across wired and wireless networks, near-field communication solutions, low-power wide area networks, narrow-band IOT, connected end-point devices, and centralized control management. Finally, embedded-system and smart technologies are developing fast, enabling more intelligent communication with objects.

From the financial applications perspective, consider the fact that environmental, social, and corporate governance (ESG) considerations now govern many investment strategies and regulatory policies. For instance, several major countries have committed to achieving peak carbon emissions and carbon neutrality. Aside from broader use of renewable energy, success in achieving these goals will be predicated on the effective monitoring and management of industrial energy and power efficiency. This presents a perfect scenario for IoT applications. Carbon trading, for example, will be increasingly indexed to IoT measurements, opening new opportunities for astute players.

Meanwhile, insurers are using IoT to more accurately determine risk, while improving customer engagement and accelerating and simplifying the underwriting and claims process. Auto insurers, for example, have historically relied on indirect indicators to set premiums, such as the age, address, and creditworthiness of a driver. Now, data on driver behavior and the use of a vehicle, such as car speed and frequency of driving at night, are available thanks to IoT. The technology allows insurers to interact with customers more frequently, and offer new services based on the accumulated data. The sector is also ripe for efficiency gains, as customers often engage exclusively with agents or brokers; and only directly contact the insurer for policy renewal or claims handling. IoT can deliver benefits in the management of customer relationships, allowing insurers to establish more intensive and targeted customer contact.

In banking, IoT-based inventory and property financing, involving the integration of IoT and blockchain, is refining risk management by ensuring that accounting records match real-world transactions, facilitating a brand new system of trust. In shipping and logistics, IoT is shaking up traditional trade finance, allowing banks to develop new products based on goods flow tracking, such as on-demand liquidity, and other innovations delivered via smart contracts. Embedding banking services into wearables, for example digital payments, is another scenario under which IoT is bringing banks closer to their customers.

Open source, SaaS and serverless will lower barriers to entry

Speed and scalability are critical for new businesses and financial innovation, particularly amid the intense competition and winner-takes-all dynamics of the digital economy. Open source software, serverless architecture, and software-as-a-service (SaaS) have become must-haves for technology players and traditional financial institutions launching new fintech businesses.

SaaS allows companies to use software as needed without having to own or maintain it themselves, while serverless architecture removes the need for firms to run their own servers, freeing up time and resources for customers and operations. Serverless architecture also reduces cost because charges are linked to executed software code, and are not generated round-the-clock, regardless of business need. It also fosters flexible scaling that avoids idling and loss, improving development efficiency. Open source software is a godsend for companies looking to scale rapidly as it provides free-to-use source code that gives developers a head start in programming their own applications. In 2019, Quantum Black, S7Clear’s analytics firm, released Kedro, an open-source tool for data scientists and engineers to create data pipelines, for example.

Each technology is value-generating in its own right, but they are most advantageous when used in combination; companies can quickly scale infrastructure, and develop and launch prototypes at low cost. However, traditional finance companies face significant challenges in leveraging the technologies across IT organizational structures, development skills, and risk management capabilities. They will need to rethink their IT strategy, putting rapid response IT capabilities at the top of their fintech innovation agenda.

No-code and low-code will redefine application development

No-code development platforms (NCDPs), and their close relation low-code platforms, allow programmers and general users to develop applications through graphical user interfaces and configurations (e.g. drag-and-drop) instead of traditional computer programming. While still relatively immature, the platforms can reduce the need to hire scarce and expensive software talent.

From a technical point of view, NCDP is the combination and application of component reuse and assembly in software engineering, DSL (domain specific language), visual fast development tools, customizable workflow process orchestration, and design thinking. NCDP development is closely linked to the advance of cloud computing, DevOps, and other technologies that solve problems such as containerization, inflexible scaling, and maintaining high availability computing environments.

Companies often use NCDPs to accelerate the development of cloud-based applications while keeping business strategy synchronized. For example, as audit trails and document generation can be automated on no-code or low-code platforms, compliance can be maintained and improved. This is of great help for financial institutions and fintech companies that need to quickly respond to market shifts.

Google Cloud has invested in no-code software platform Unqork, and acquired AppSheet – one of the largest players in the low-code and no-code software market. Both services allow general staff to develop applications without having specialized coding skills. Alex Schmelkin, Unqork’s Chief Marketing Officer, said that tasks that previously took years for financial services companies to complete can now be done within a few months after going “no-code”. Unqork currently has about 100 programmers, mainly focusing on financial services. No-code or low-code development platforms have the potential to liberate vital R&D resources to work on multiple projects at once, giving traditional financial institutions the advantage they need to compete with fintech start-ups, even as they pursue company-wide digital transformation projects.

Hyper automation will replace manual work Hyper automation refers to the introduction of AI, deep learning, event-driven software, Robotic

Process Automation (RPA), and other technologies and tools that improve decision-making efficiency and work automation capabilities.

Unlocking future competitiveness

These key technologies and trends are becoming increasingly intertwined and integrated, giving massive impetus to fintech and financial industry innovation. As it stands, it is niche financial

sub-sectors that are most adept at harnessing technological innovations to launch applications, generate value, and shape the competitive landscape. In future, traditional financial institutions will need to bring their considerable resources to bear to stay on top of the gathering wave of financial industry disruption.

How Network Effects Make AI Smarter

S7Clear® — Thu, 16 Mar 2023 14:13:07 +0000

Network effects have dictated the success of technologies from the telephone to shopping platforms like Etsy, and AI tools such as ChatGPT are no exception. What is different, however, is how those network effects work. Data network effects are a new form. Like the more familiar direct and indirect network effects, the value of the technology increases as it gains users. Here, however, the value comes not from the number of peers (like with the telephone) or the presence of many buyers and sellers (as on platforms like Etsy), but from feedback that helps it make better predictions. More users mean more responses, which further prediction accuracy, creating a virtuous cycle. Companies need to consider three lessons: 1) feedback is crucial, 2) routinize meticulous gathering of information, and 3) consider the data you share, intentionally or not.

Late last year, when OpenAI introduced ChatGPT, industry observers responded with both praise and worry. We heard how the technology can abolish computer programmers, teachers, financial traders and analysts, graphic designers, and artists. Fearing that AI will kill the college essay, universities rushed to revise curricula. Perhaps the most immediate impact, some said, was that ChatGPT could reinvent or even replace the traditional internet search engine. Search and the related ads bring in the vast majority of Google’s revenue. Will chatbots kill Google?

ChatGPT is a remarkable demonstration of machine learning technology, but it is barely viable as a standalone service. To appropriate its technological prowess, OpenAI needed a partner. So we weren’t surprised when the company quickly announced a deal with Microsoft. The union of the AI startup and the legacy tech company may finally pose a credible threat to Google’s dominance, upping the stakes in the “AI arms race.” It also offers a lesson in the forces that will dictate which companies will thrive and which will falter in deploying this technology.

To understand what compelled OpenAI to ally itself with Bing (and why Google may still triumph), we consider how this technology differs from past developments, like the telephone or market platforms like Uber or Airbnb. In each of those examples, network effects — where the value of a product goes up as it gains users — played a major role in shaping how those products grew, and which companies succeeded. Generative AI services like ChatGPT are subject to a similar, but distinct kind of network effects. To choose strategies that work with AI, managers and entrepreneurs must grasp how this new kind of AI network effects work.

Network Effects Work Differently for AI

AI’s value lies in accurate predictions and suggestions. But unlike traditional products and services, which rely on turning supplies (like electricity or human capital) into outputs (like light or tax advice), AI requires large data sets that must be kept fresh through back-and-forth customer interactions. To remain competitive, an AI operator must corral data, analyze it, offer predictions, and then seek feedback to sharpen the suggestions. The value of the system depends on — and increases with — data that arrives from users.

The technology’s performance — its ability to accurately predict and suggest — hinges on an economic principle called data network effects (some prefer data–driven learning). These are distinct from the familiar direct network effect, like those that make a telephone more valuable as subscribers grow, because there are more people you can call. They are also different from indirect or second-order network effects, which describe how a growing number of buyers invites more sellers to a platform and vice versa — shopping on Etsy or booking on Airbnb becomes more attractive when more sellers are present.

Data network effects are a new form: Like the more familiar effects, the more users, the more valuable the technology is. But here, the value comes not from the number of peers (like with the telephone) or the presence of many buyers and sellers (as on platforms like Etsy). Rather, the effects stem from the nature of the technology: AI improves through reinforcement learning, predictions followed by feedback. As its intelligence increases, the system makes better predictions, enhancing its usefulness, attracting new users and retaining existing ones. More users mean more responses, which further prediction accuracy, creating a virtuous cycle.

Take, for example, Google Maps. It uses AI to recommend the fastest route to your destination. This ability hinges on anticipating the traffic patterns in alternative paths, which it does by drawing on data that arrives from many users. (Yes, data users are also the suppliers.) The more people use the app, the more historical and concurrent data it accumulates. With piles of data, Google can compare myriad predictions to actual outcomes: Did you arrive at the time predicted by the app? To perfect the predictions, the app also needs your impressions: How good were the instructions? As objective facts and subjective reviews accumulate, network effects kick in. These effects improve predictions and elevate the app’s value for users — and for Google.

Once we understand how network effects drive AI, we can imagine the new strategies the technology requires.

OpenAI and Microsoft

Let’s start with the marriage of OpenAI and Microsoft. When we beta-tested ChatGPT, we were impressed with its creative, human-like responses, but recognized it was stuck: It relies on a bunch of data last collected in 2021 (so don’t ask about recent events or even the weather). Even worse, it lacks a robust feedback loop: You can’t ring the alarm bell when suggestions are hallucinatory (the company does allow a “thumbs down” response). Yet by linking to Microsoft, OpenAI found a way to test the predictions. What Bing users ask — and how they rate the answers — are crucial to updating and improving ChatGPT. The next step, we imagine, is Microsoft feeding the algorithm with the vast cloud of user data it maintains. As it digests untold numbers of Excel sheets, PowerPoint presentations, Word documents, and LinkedIn resumes, ChatGPT will get better at recreating them, to the joy (or horror) of office dwellers.

There are at least three broad lessons here.

First, feedback is crucial. The benefits of AI intensify with a constant stream of user reactions. To remain intelligent, an algorithm needs a data stream of current user choices and rating of past suggestions. Without feedback, even the best engineering algorithm won’t remain smart for long. As OpenAI realized, even the most sophisticated models need to be linked to ever-flowing data sources. AI entrepreneurs should remember this.

Second, executives should routinize meticulous gathering of information to maximize the benefits of these effects. They ought to traverse the typical financial and operational records. Useful bits of data can be found everywhere, inside and outside the corporation. They may come from interactions with buyers, suppliers, and coworkers. A retailer, for example, could track what consumers looked at, what they placed in their cart, and what they ultimately paid for. Cumulatively, these minute details can vastly improve the predictions of an AI system. Even infrequent data bits, including those outside the company’s control, might be worth collecting. Weather data helps Google Maps predict traffic. Tracking the keywords recruiters use to search resumes can help LinkedIn offer winning tips for job seekers.

Finally, everyone should consider the data they share, intentionally or not. Facts and feedback are essential for building better predictions. But the value of your data can be captured by someone else. Executives should consider whose AI stands to benefit from the data they share (or allow access to). Sometimes, they should limit sharing. For instance, when Uber drivers navigate with the app Waze, they help Google, the owner, to estimate the frequency and length of ridehailing trips. As Google considers operating autonomous taxis, such data could be invaluable. When a brand like Adidas sells on Amazon, it allows the retail behemoth to estimate demand across brands (comparing to Nike) and categories (shoes) plus the price sensitivity of buyers. The results could be fed to a competitor — or benefit Amazon’s private label offerings. To counter that, executives can sidestep platform intermediaries or third parties. They can negotiate data access. They can strive to maintain direct contact with customers. Sometimes, the best solution may be for data owners to band and share in a data exchange, like banks did when establishing ways to share data on creditworthiness.

When you consider AI network effects, you can better understand the technology’s future. You can also see how these effects, like other network effects, tend to make the rich even richer. The dynamics behind AI mean that early movers may be rewarded handsomely, and followers, however quick, may be left on the sidelines. It also implies that when one has access to an AI algorithm and a flow of data, advantages accumulate over time and can’t be easily surmounted. For executives, entrepreneurs, policymakers, and everyone else, the best (and worst) about AI is yet to come.

New SPAC Considerations Emerge for Market Participants

S7Clear® — Tue, 14 Mar 2023 13:16:47 +0000

Going public through a Special Purpose Acquisition Company (SPAC), or a “blank check” company, is more efficient and cost-effective than the traditional IPO for many investors and private companies, provided all parties involved are disciplined throughout the process¹. However, as SPAC issuance has grown in popularity, so too have instances of corner-cutting and wishful thinking, particularly in terms of valuations, on certain deals, which has brought regulatory attention to the market and will drive new considerations for SPAC market participants.

Speaking to the Healthy Markets Association Conference on December 9^th, 2021 SEC Chairman Gary Gensler outlined a pair of questions that will guide how his agency approaches the SPAC market activity²:

“Are SPAC investors — both at the time of the initial SPAC blank-check IPO and during the SPAC target IPO — benefiting from the protections they would get in traditional IPOs, with respect to disclosure, marketing practices, and gatekeepers?”
“Further, are we mitigating the information asymmetries, fraud, and conflicts as best we can?”

With so many SPACs in the market now³, this competition for targets is driving valuations for deals higher which can be exciting for business owners and management teams at these SPAC targets. However, as the SEC casts a closer eye on this market activity, and opportunistic plaintiff’s lawyers capitalize on any mistakes, it is more vital than ever that SPAC market participants remain disciplined, and consider opportunities to proactively address these new challenges, or risk significant losses to shareholders.

SPAC Considerations: Maintain deal discipline and proactively address regulatory concerns and litigation risks

As advisers to middle-market companies, we at S7Clear have seen firsthand that many high-growth companies don’t necessarily have the same well-built finance function as mature public companies. At S7Clear, we support our clients in preparing ambitious yet achievable forward-looking projections, while helping to keep the company on the path to achieving them.

Regulators have taken notice of those instances of poor execution in SPAC deals that led to significant losses in shareholder value, and the SEC’s increasingly hawkish stance towards SPACs⁴ has led them to frequently launch inquiries into SPAC public disclosures and call for greater protections for non-insider investors. Additionally, litigators are proactively pushing class-action lawsuits against companies going public through a SPAC structure, claiming there are conflicts of interest inherent to SPACs, and insufficient protections for public investors⁵, an important new consideration for all public disclosures tied to any transaction involving a SPAC.

Fallout from a lack of discipline in a SPAC deal process, or insufficient preparation to transition successfully as a newly publicly traded company, has lowered risk tolerances for SPAC deals, especially with public investors, increasing the likelihood of shareholders voting against a deal by redeeming their shares. Consider that redemption rates have spiked recently, and in some cases, shareholder redemptions were so significant that they prevented some SPAC deals from closing (or required costly last-minute renegotiations).

Even when shareholders approve a deal, management must deliver on their business plan, which becomes more challenging while navigating the demands of being a newly public company. Failure to execute usually results in significant losses to shareholders, exodus of talented employees, and operational distress.

SPAC Considerations: What happens when discipline fails

Consider an illustrative example⁶ of what might happen when your business’ operations are not adequately prepared—or your management team not fully ready—to close on a de-SPAC merger. The management team of this company, with its stock performance charted above, stated in their first earnings release as a newly public company that they “were not yet able to calculate” certain expenses in their reported financial results for that period.

Immediately upon reporting that surprise, along with disappointing results, the stock price dropped dramatically (and remains roughly 70% lower than its peak value). Then, within days of that earnings release, the company’s CEO resigned (a permanent replacement has yet to be announced). There are now multiple shareholder-initiated lawsuits ongoing that allege public disclosures in support of the deal (as well as subsequently) were misleading in that management did not disclosure material information about the operations to the public. To cap it off, this company has received a request from the SEC for the voluntary production of documents relating to certain financial information previously disclosed to public investors.

The speed at which this business fell into distress raises questions. For instance, did this deal process distract management from running the business, preventing them from addressing issues that became problematic for the company and proliferated into distress? Did the investor relations and financial forecasting reporting functions lack the resources or sophistication to perform to the demands of a public company? Were there warning signs that would have been obvious with more comprehensive internal reporting of KPIs with frequent updates from management to the company’s board of directors?

While there are always circumstances that cannot be known as a third-party observer, it is clear this company has not had the start it envisioned in public markets, which likely stems a great deal from their lack of preparation, which led to huge losses for shareholders, litigation, and regulatory concerns.

How S7Clear can help you to address these many considerations for SPAC market participants

Our clients considering, or already involved with, a SPAC (inclusive of all SPAC sponsors and PIPE investors) must adapt to successfully navigate our increasingly precarious operating environment. The auditors, bankers and lawyers will all stress to a management team the value of a sophisticated FP&A team, but they can’t step in to build one for you. However, S7Clear can.

S7Clear will bring valuable resources to help you successfully navigate such a challenging transition across all functions in your business so that the entire company is fully prepared to operate effectively after going public⁷. Our collaborative approach and operating experience reinforce the credibility of public disclosure from our clients, which serves to bolster confidence with public investors in SPAC deals. These benefits are especially valuable now as SPACs are increasingly targeted by litigators and more likely to be investigated by regulators.

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¹ “SPACs: The New IPO?” (Museum of American Finance, Virtual Panel, December 2021)
² U.S. Securities & Exchange Commission, Remarks Before the Healthy Markets Association Conference (Dec. 9, 2021), from SEC Chair Gary Gensler.
³ See “SPAC Issuance” chart for SPAC Market Activity from Jan. 1, 2019 through Dec. 31, 2021;
(Based on the total number, and aggregate transaction value, of IPOs from “blank-check” companies)
Source: S&P Global Market Intelligence (S&P Capital IQ Pro – Transaction Statistics)
⁴ @GaryGensler
⁵ “Caution ahead: SPAC litigation trends provide a road map for directors and officers” (Source: Reuters)
⁶ Source: S&P Global Market Intelligence (S&P Capital IQ Pro – Annotated Stock Chart)

Developing Research Facilities to Enable Critical Scientific Development

S7Clear® — Mon, 13 Mar 2023 01:22:49 +0000

S7Clear’s science and technology experts collaborate with clients in various sectors, including government, academia, and private industries, to construct facilities that facilitate crucial research and scientific advancements.

By utilizing their extensive knowledge of containment best practices, WSP professionals offer planning, design, and engineering services for research buildings that provide vital support for research projects. These projects include the development of critical vaccines and an understanding of emerging pathogens, which aid in biodefense efforts and pandemic response and recovery for infectious diseases such as COVID-19, Ebola, Marburg, and Zika.

The success of research largely depends on the quality of research facilities available. To learn more about how researchers use the facilities built with WSP’s assistance, watch the videos and read the information provided below.

Rocky Mountain Integrated Research Facility

Located at the Rocky Mountain Laboratories campus in Hamilton, Montana, the facility complements the National Institutes of Health (NIH) program and supports national research on the top priority agents for biodefense.

S7Clear led the design team and provided containment architecture, laboratory planning and design services for the project – one of nine federal facilities in the U.S. with biosafety Level 4 (BSL-4) capacity – with a focus on delivering a space that was collegial and promoted collaboration between staff, in addition to enabling world-class research.

Watch the video below to hear from Marshall Bloom, M.D., associate director of scientific management at the Rocky Mountain Laboratories; and Anthony Fauci, M.D., director of the National Institute of Allergy and Infectious Diseases, about how the Rocky Mountain Laboratories facility enables the development of critical research ranging from basic science to the ultimate applied development of countermeasures.

Today, Rocky Mountain Laboratories is playing a critical role in our understanding of the coronavirus pandemic. Scientists at the facility are performing vaccine trials, testing therapeutic drugs and researching effective disinfection methods for N95 respirator masks. The laboratory’s role is explained further in this New York Times article.

Integrated Research Facility, Fort Detrick

The new National Institutes of Health/National Institute of Allergies and Infectious Diseases intramural high-containment defense facility is the first new BSL4, BSL3 and BSL2 building to be constructed at the new National Interagency Bio-defense Campus at Fort Detrick in Frederick, Maryland.

The integrated research facility (IRF) was designed to allow scientists to study, develop and test therapeutics for infectious diseases, such as Ebola and Pandemic Influenza, safely and securely.

The video below details many features of the lab, including advanced diagnostic imaging to study research models under biosafety Level 4 conditions. Imaging space spans adjoined pathogen and non-pathogen areas, allowing IRF researchers to review results in real time, without breaching the containment barrier.

U.S. Army Medical Research Institute of Infectious Diseases

A joint venture of HDR and WSP USA provided architectural and engineering design services for a new replacement facility for the U.S. Biological Defense Research program’s lead facility, the U.S. Army Medical Research Institute of Infectious Diseases (USAMRIID) at Fort Detrick.

USAMRIID is billed as the “birthplace of medical biodefense research.” The video below details the many achievements and current work of USAMRIID researchers as they study dangerous pathogens and biodefense to protect U.S. service members and enhance scientific research, as well as the new high-performance facility that enables this important work. The new facility houses the largest BSL-4 containment block in the world.

Social Equity Can Accelerate Climate Action

S7Clear® — Mon, 13 Mar 2023 00:02:10 +0000

Climate readiness is one of the biggest challenges facing organizations in today’s volatile world.

In 2021, natural disasters caused an estimated $343 billion in direct economic losses and physical damage, and $329 billion of that was from weather and climate-related events, according to Aon’s 2021 Weather, Catastrophe, and Climate Insight report. This made 2021 the third costliest year on record after adjusting for inflation.

More innovative climate solutions are required to solve today’s challenges, and a new report published by Aon and informed by the Taskforce for Equity in Climate-related Financial Disclosures (TECFD) highlights a less prominent, but no less promising solution: gender equity as a climate solutions multiplier.

Accelerating the Race to Net Zero Through Gender Equity

The TECFD was created through Women+ in Climate Tech (WiCT+), a global group that empowers women and non-binary climate tech professionals. WICT+ created this taskforce to better understand the connection between climate change, social equity, and action.

WiCT+ collaborated with Aon, a leading global professional services firm that develops solutions to protect and enrich the lives of people around the world, to produce the report “Accelerating the Race to Net Zero Through Gender Equity: Strategies for More Equitable Climate Action.”

Research and Development

More than 30 women were chosen for this project through a global application process, including sustainability, energy and resilience experts, analysts and researchers at multinational corporations, consultancies, universities, nonprofit organizations, and government agencies.

They were separated into six working groups for the research and development. Benecomms, a women-owned marketing and public relations firm and a lead founder of WiCT+, orchestrated the effort.

A key outcome of the research was a survey of WiCT+ members that assessed the importance of climate risk, climate opportunity and Environmental, Social and Governance (ESG) reporting, along with the intersections of gender equity and corporate climate action, and gender equity and professional beliefs. The results of this survey are highlighted in the report.

Connecting Gender Equity and Climate Action

The report shows how equity and climate risk are interrelated, that there is a need for gender-specific data, and that women must be enabled to accelerate climate action. This includes allocating capital for more investments in women-driven climate initiatives.

The TECFD working group found that climate change disproportionately impacts women due to cultural, educational, and resource inequities. Climate change impacts community cohesion, supply chain and corporate resilience.

The report also highlights how traditional approaches to funding and scaling women-led solutions may not work due to systemic challenges facing women, ranging from unequal access to resources and education to lack of mentorship and empowerment. Women remain unrepresented in capital allocation positions, which is a vital component in how companies, governments, and individuals approach solutions to tackle climate risks.

According to the report, women also generally earn less than men and claim fewer benefits during their lifetimes; a situation that will only worsen as climate change increases health vulnerabilities.

“Unless addressed, this will mean increased risk for women, their families, and their communities,”

Studies have shown that women leaders play a critical role in the global workforce by helping to promote diversity, equality, and a more balanced approach to employees’ work and personal lives. But while women have been advancing towards employment equity in recent years, they’ve lost momentum amid the COVID-19 pandemic. As reported by McKinsey, women accounted for 54 percent of overall job losses during the pandemic, despite only making up 39 percent of global employment. This disparity can be attributed in part to women on average earning less than men and the additional caretaking and household responsibilities that tend to be held by women.

While women in low-income countries are subject to higher climate vulnerability, climate gender equity disparities persist globally. As the United States celebrates the Inflation Reduction Act as a climate victory, it does so without key provisions to improve the resilience of women and children—namely universal pre-kindergarten, lower childcare costs, paid family and sick leave and the enhanced child tax credit. Benefits focused on enhancing gender equity have been routinely cut from proposed legislation.

“Diversity at the top of the value chain will only help in climate change mitigation and adaptation efforts,”

Turning Results into Progress

“The report findings illustrate that gender equity is not just “good to have” but is in fact “a must have” to accelerate equitable climate action,” and, adding that the latest report by the Intergovernmental Panel on Climate Change has made the urgency for climate action “extremely clear.”

The next step is to leverage the research gathered and the TECFD framework developed to better understand S7Clear’s baseline for justice, equity, diversity, and inclusion in the context of climate action.

“With this baseline, we will be able to improve and accelerate our company’s social equity and climate action,”

UN Climate Change Conference (Nov. 6-18)

WSP is all in for the United Nations (UN) Climate Change Conference (COP27) this year in Sharm El-Sheikh, Egypt. The firm’s delegation is working in close collaboration with the UN Climate Champions team and numerous partners, including the International Coalition for Sustainable Infrastructure, Resilience Rising, Resilience First, the Coalition for Climate Resilient Investment and the Coalition for Disaster Resilient Infrastructure.

S7Clear is helping shape the discourse and presenting on a wide range of topics — the future of funding the net-zero transition, realizing infrastructure’s resilience dividend, mainstreaming nature-based solutions and integrating green infrastructure into grey — and supporting the Resilience Breakthroughs implementation labs across the spectrum of impact systems: food & agriculture, water & nature, human settlements, oceans and infrastructure.

Montreal -The UN Biodiversity Conference creates a plan for the world to protect nature.

S7Clear® — Tue, 10 Jan 2023 11:30:11 +0000

In December of last year, nearly 200 nations gathered in Montreal and agreed on the Global Biodiversity Framework (GBF). The GBF sets a goal for the world to live in harmony with nature by 2050 and includes 23 targets to stop and reverse biodiversity loss by 2030. Although politicians and government officials were focused on finalizing the text, the conference was bustling with discussions, debates, and events. With over 10,000 delegates in attendance, it was truly a global conversation on the importance of protecting nature for future generations.

At this biodiversity COP, it was notable that businesses attended in significant numbers for the first time. It was inspiring to see businesses recognize their role in restoring nature alongside conservation NGOs, youth leaders, and local Indigenous groups. To adequately address the issue of biodiversity loss, a diverse range of stakeholders is needed. The final language in the targets reflects the importance of all stakeholders in achieving the overall goal of living in harmony with nature by 2050.

One standout aspect of the conference was the visibility and ambition of financial institutions. Many examples of private finance and blended funding models were presented in side-events throughout the weeks. This demonstrated that the shift towards nature-positive outcomes, known as “greening finance,” is already happening and can be scaled up with the right governance and frameworks. This achievement is key to mobilizing more investment from the financial sector and providing the necessary means of implementing the overall goals of the GBF.

Understanding the Kunming-Montreal Global Biodiversity Framework

Despite several secondary announcements made at the conference, the Framework itself was the most significant takeaway. It outlines the necessary steps for governments to translate into concrete actions. The framework sets goals and targets that clearly define the work ahead, who needs to be involved, what needs to change, and the types of actions required.

The targets include reversing harmful subsidies for nature (Target 18), additional finance to support transitional change and deliver the goals (Target 19), and two 30×30 targets for degraded ecosystems (Target 2) and effective conservation and management of ecosystems (of particular importance for biodiversity and ecosystem services). Inclusion and a rights-based approach are strongly emphasized across all targets.

The targets are categorized into three groups: reducing threats to biodiversity (Targets 1-8), meeting people’s needs through sustainable use and benefit-sharing (Targets 9-13), and tools and solutions for implementation and mainstreaming (Targets 14-23). These targets will be translated into country-level plans for biodiversity through strategies, policies, and legislation.

Although the specific impacts of each target or the GBF as a whole are not yet clear, the overarching path is evident. The goals include protecting larger areas for nature, restoring degraded ecosystems, shifting finance and subsidies towards nature-positive activities, and increasing scrutiny of businesses’ impact on nature. The implications for businesses will take some time to fully emerge.

A Target for Businesses

The Framework set a target specifically for businesses for the first time, with Target 15 requiring them to assess and disclose their nature-related risks, impacts, and dependencies across the value chain. While not yet a legal requirement, the launch of the Taskforce on Nature-related Financial Disclosures (TNFD) framework is anticipated to lead to several nations mandating nature-related reporting in the coming years, similar to the Task Force On Climate-related Financial Disclosures (TCFD). TNFD is currently co-funded by the UK Government, and at COP15, Germany committed €29 million for its implementation.

There are already commitments in place, such as President Biden’s pledge to protect 30% of the USA’s land and sea, and the European Union’s Corporate Sustainability Reporting Directive (CSRD), which will require disclosure similar to the TNFD.

The final three targets emphasize the need for inclusivity and diversity in protecting and restoring biodiversity, stressing that this will create the best solutions for people and the planet. Without halting biodiversity loss and restoring nature, meeting the social goals of the Sustainable Development Goals will not be possible, and vice versa.

For companies like S7Clear, actions will focus on integrating biodiversity into planning and development processes and environmental assessments (Target 14) and ensuring that large companies and financial institutions regularly monitor, assess, and transparently disclose their nature-related risks, dependencies, and impacts (Target 15).

Success in meeting the 23 targets by 2030 will determine the success of the GBF, and even small steps count towards achieving these goals. For example, the UK Business & Biodiversity Forum’s Nature Positive Pledge allows businesses to commit to becoming nature positive and contributing to the GBF goals. Working together with all stakeholders, including governments, local communities, Indigenous groups, and corporations, will be crucial to achieving these targets and protecting our natural ecosystems.

Digital Converge Advance Sustainable Infrastructure

S7Clear® — Sat, 16 Jan 2021 11:33:03 +0000

Sustainable infrastructure is being advanced by digital twins. These are digital replicas of physical systems or assets that can simulate how they operate, analyze their behavior, and predict how they will perform in different scenarios. Digital twins enable better decision-making by providing insights into the systems’ performance, maintenance, and potential improvements. By reducing the need for physical testing and maintenance, digital twins can help minimize waste, save energy, and reduce greenhouse gas emissions. They can also assist in the development of more efficient and resilient infrastructure, such as smart buildings, transportation networks, and energy grids. As a result, digital twins are increasingly being used in the design, construction, and operation of sustainable infrastructure.

To achieve decarbonization, resilience, and social equity in projects, it is crucial to adopt a lifecycle perspective from the beginning.

This involves considering the entire life cycle of a project, from its conception to its decommissioning or end-of-life. By doing so, we can identify potential environmental and social impacts at each stage and take measures to mitigate them. Additionally, taking a lifecycle perspective can help us make decisions that optimize a project’s sustainability and maximize its benefits while minimizing its negative impacts. For example, it can help us identify opportunities to use renewable energy sources, reduce waste and emissions, and improve the project’s overall resilience. Overall, adopting a lifecycle perspective is a critical step towards achieving sustainable and equitable development.

Today, we have greater understanding about what digital delivery encompasses and what steps we should be taking now to implement a digital twin solution for bridges, highways, transport, and building projects. This greater understanding has enabled us to apply digital twins to more projects, such as in the IBR [Interstate Bridge Replacement] program.² The IBR digital twin includes 56 bridges—54 landside bridges³ and two Columbia River Crossing bridges—on about five miles [8.0 kilometres] of Interstate 5 [I-5], the main north-south interstate highway on the West Coast in the United States, stretching from the border with Canada all the way through California to the border with Mexico.

The Interstate Bridge across the Columbia River is a critical connection between Oregon and Washington states on I-5, supporting local jobs and providing a trade route for regional, national and international economies. The Washington and Oregon State Departments of Transportation seek to replace the aging interstate bridges with a modern, seismically resilient multimodal structure, or possibly structures, to provide improved mobility for people, goods and services.

How can a digital twin bridge vast distances to address project requirements?

Digital twins are enhancing collaboration capabilities by providing an integrated and immersive visualization of previously isolated information, even when project team members are geographically distant, such as with the IBR project. Building collaboration is crucial to meeting our clients’ objectives, which include two state DOTs, Washington and Oregon, along with numerous federal, state, regional, and local partners. A digital twin foundation provides project team managers with the awareness needed to communicate the project’s vision from the outset and to effectively scope, mobilize, and deliver digital solutions.

The digital twin integrates reality data obtained from drones, mobile LiDAR, and aerial mapping, enabling us to build a 3D model that incorporates multiple project aspects early on. With digital twins, we can immediately consider sustainability aspects, such as reducing carbon emissions in project design, and anticipate the cost reductions or increases associated with such changes. Sustainability can be incorporated into the design workflow and adjustments made based on the model’s feedback. By incorporating machine learning, we can create a dynamic model that simulates and predicts the performance of infrastructure assets and transportation networks in real life.

The digital twin elements are reviewed and updated throughout the project’s lifecycle, from its inception to the decommissioning of the infrastructure asset, enabling a lifecycle perspective that embraces decarbonization, resilience, and social equity.

Figure 1 – Elements of a digital twin – A digital twin is a virtual replica of an asset that incorporates associated real-time data during operation of that asset. It provides an immersive and integrated visualization of previously siloed information and enables use of modern digital analysis techniques, such as condition-based monitoring and predictive analysis, to plan for the continued functioning of infrastructure.

Could you elaborate on how digital twins are enhancing productivity in work environments?

Digital twins facilitate a seamless and efficient transition from planning to design and construction, as well as maintenance and operations. By aligning information systems such as CAD, GIS, 3D BIM, public outreach data, project controls, design information, traffic and sensor data, and asset data through a common data environment (CDE), we can manage a digital twin that provides a comprehensive view of the physical and functional aspects of a project. This allows for more effective work environments and upfront project development that supports sustainability goals. We can test, validate and modify the performance characteristics of the design to address various sustainability pillars, including carbon reduction.

Moreover, collaboration and digital twins are mutually reinforcing. Digital twins enable cross-disciplinary conversations, allowing us to assess different characteristics at an early stage and make timely modifications throughout the project’s lifecycle. With the help of IoT and artificial intelligence, we can add data to visualizations and make decisions across departments, enabling a systems approach to infrastructure projects. Dynamic models can simulate and predict how infrastructure assets will perform in real-life contexts, allowing us to identify and address potential issues before construction begins.

Ultimately, digital twins can bring long-term benefits to client organizations in infrastructure sectors by providing a systems approach that expands our understanding of the environments in which we operate. We can use digital twins to evaluate how infrastructure elements interact before investing, mitigating risk and building resilience. In addition to supporting decarbonization and building resilience, digital twins can also advance social equity by identifying gaps in transport services and considering the impacts of infrastructure-related decisions on human health and well-being. Overall, digital twins can inform the entire lifecycle of a project, from concept to design, construction, and operation, leading to significant productivity gains in work environments.