Aligning Innovation and Regulation in a DeFi World

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It’s been a while since I lived in Zurich. It was about this time of year when I first visited the city that I instantly fell in love with. Beautiful blue skies and if you’re lucky enough, you can see the snow-capped mountains from Lake Zurich. It’s hard not to be instantly drawn to this small city of approximately 1.4 million people, which punches well above its weight class. One out of every eleven jobs in Switzerland is in Zurich. The financial sector generates around a quarter of the city’s economic output and provides approximately 59,000 full time equivalent jobs – accounting for 16% of all employment in the city.

Between 21-23 June, Zurich will also be home to the Point Zero Forum – an exclusive invite-only, in-person gathering of select global leaders, founders and investors with the purpose of developing new ideas on emerging concepts such as decentralised finance (DeFi), Web 3.0, embedded finance and sustainable finance; driving investment activity; and bringing together public and private sector leaders to brainstorm on regulatory requirements.

The Future of DeFi

Zug is a little canton outside of Zurich and is famously known as “Crypto Valley”. When I lived in Zurich, Zug was the home of many of the country’s leading hedge funds as Zug’s low tax, business friendly environment and fantastic quality of life attracted many of the world’s leading fund managers and companies. Today the same can be said about crypto companies setting up shop in Zug. And crypto ecosystems are expanding exponentially.

However, with the increase in the global adoption of cryptocurrency, what role will the regulators play in aligning regulation without stifling innovation? How can Crypto Valley and Singapore play a role in defining the role regulation will play in a DeFi world?

DeFi is moving fast and we are seeing an explosion of new ideas and positive outcomes. So, what can we expect from all of this? Well, that is what I will discuss with a group of regulators and industry players in a round table discussion on How an Adaptive and Centralised Regulatory Approach can Shape a Protected Future of Finance at the Point Zero Forum. We will explore the role of regulators in a fast-moving industry that has recently seen some horror stories and how industry participants are willing to work with regulators to meet in the middle to build an exciting and sometimes unpredictable future. How do we regulate something in the future? I am personally looking forward to the knowledge sharing.

For the industry to strive and innovate, we need both regulators and industry players to work together and agree to a working framework that helps deliver innovation and growth by creating new technology and jobs. But we also need to keep an eye out on the increasing number of scams in the industry. It is true to say that we have seen our fair share of them in recent months. The total collapse of TerraUSD and Luna and the collapse of the wider crypto market that saw an estimated loss of USD 500 billion has really spooked global markets.

So is cryptocurrency here for good and will it be widely adopted globally? How will regulators see the recent collapse of Luna and view regulations moving forward? We have reached an interesting point with cryptocurrencies and digital assets in general. Is it time to reflect on the current market or should we push forward and try to find a workable middle ground?

Let’s find out. Watch this space for my follow-up post after the Point Zero Forum event!

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How Useful is Synthetic Data?

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When non-organic (man-made) fabric was introduced into fashion, there were a number of harsh warnings about using polyester and man-made synthetic fibres, including their flammability.

In creating non-organic data sets, should we also be creating warnings on their use and flammability? Let’s look at why synthetic data is used in industries such as Financial Services, Automotive as well as for new product development in Manufacturing.

Synthetic Data Defined

Synthetic data can be defined as data that is artificially developed rather than being generated by actual interactions. It is often created with the help of algorithms and is used for a wide range of activities, including as test data for new products and tools, for model validation, and in AI model training. Synthetic data is a type of data augmentation which involves creating new and representative data.

Why is it used?

The main reasons why synthetic data is used instead of real data are cost, privacy, and testing. Let’s look at more specifics on this:

  • Data privacy. When privacy requirements limit data availability or how it can be used. For example, in Financial Services where restrictions around data usage and customer privacy are particularly limiting, companies are starting to use synthetic data to help them identify and eliminate bias in how they treat customers – without contravening data privacy regulations.
  • Data availability. When the data needed for testing a product does not exist or is not available to the testers. This is often the case for new releases.
  • Data for testing. When training data is needed for machine learning algorithms. However, in many instances, such as in the case of autonomous vehicles, the data is expensive to generate in real life.
  • Training across third parties using cloud. When moving private data to cloud infrastructures involves security and compliance risks. Moving synthetic versions of sensitive data to the cloud can enable organisations to share data sets with third parties for training across cloud infrastructures.
  • Data cost. Producing synthetic data through a generative model is significantly more cost-effective and efficient than collecting real-world data. With synthetic data, it becomes cheaper and faster to produce new data once the generative model is set up.

Why should it cause concern?

If real dataset contains biases, data augmented from it will contain biases, too. So, identification of optimal data augmentation strategy is important.

If the synthetic set doesn’t truly represent the original customer data set, it might contain the wrong buying signals regarding what customers are interested in or are inclined to buy.

Synthetic data also requires some form of output/quality control and internal regulation, specifically in highly regulated industries such as the Financial Services.

Creating incorrect synthetic data also can get a company in hot water with external regulators. For example, if a company created a product that harmed someone or didn’t work as advertised, it could lead to substantial financial penalties and, possibly, closer scrutiny in the future.

Conclusion

Synthetic data allows us to continue developing new and innovative products and solutions when the data necessary to do so wouldn’t otherwise be present or available due to volume, data sensitivity or user privacy challenges. Generating synthetic data comes with the flexibility to adjust its nature and environment as and when required in order to improve the performance of the model to create opportunities to check for outliers and extreme conditions.

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Cloudification of India’s Banking Industry

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In this Insight, guest author Anupam Verma talks about the technology-led evolution of the Banking industry in India and offers Cloud Service Providers guidance on how to partner with banks and financial institutions. “It is well understood that the banks that were early adopters of cloud have clearly gained market share during COVID-19. Banks are keen to adopt cloud but need a partnership approach balancing innovation with risk management so that it is ‘not one step forward and two steps back’ for them.”

India has been witnessing a digital revolution. Rapidly rising mobile and internet penetration has created an estimated 1 billion mobile users and more than 600 million internet users. It has been reported that 99% of India’s adult population now has a digital identity in the form of Aadhar and a large proportion of the adult Indians have a bank account.

Indians are adapting to consume multiple services on the smartphone and are demanding the same from their financial services providers. COVID-19 has accelerated this digital trend beyond imagination and is transforming India from a data-poor to a data-rich nation. This data from various alternate sources coupled with traditional sources is the inflection point to the road to financial inclusion. Strong digital infrastructure and digital footprints will create a world of opportunities for incumbent banks, non-banks as well as new-age fintechs.

The Cloud Imperative for Banks

Banks today have an urgent need to stay relevant in the era of digitally savvy customers and rising fintechs. This journey for banks to survive and thrive will put Data Analytics and Cloud at the front and centre of their digital transformation.

A couple of years ago, banks viewed cloud as an outsourcing infrastructure to improve the cost curve. Today, banks are convinced that cloud provides many more advantages (Figure 1).

Why banks adopt cloud

Banks are also increasingly partnering with fintechs for applications such as KYC, UI/UX and customer service. Fintechs are cloud-native and understand that cloud provides exponential innovation, speed to market, scalability, resilience, a better cost curve and security. They understand their business will not exist or reach scale if not for cloud. These bank-fintech partnerships are also making banks understand the cloud imperative.

Traditionally, banks in India have had concerns around data privacy and data sovereignty. There are also risks around migrating legacy systems, which are made of monolithic applications and do not have a service-oriented architecture. As a result, banks are now working on complete re-architecture of the core legacy systems. Banks are creating web services on top of legacy systems, which can talk to the new technologies. New applications being built are cloud ready. In fact, many applications may not connect to the core legacy systems. They are exploring moving customer interfaces, CRM applications and internal workflows to the cloud. Still early days, but banks are using cloud analytics for marketing campaigns, risk modelling and regulatory reporting.

The remote working world is irreversible, and banks also understand that cloud will form the backbone for internal communication, virtual desktops, and virtual collaboration.

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Strategy for Cloud Service Providers (CSPs)

It is estimated that India’s public cloud services market is likely to become the largest market in the Asia Pacific behind only China, Australia, and Japan. Ecosystm research shows that 70% of banking organisations in India are looking to increase their cloud spending. Whichever way one looks at it, cloud is likely to remain a large and growing market. The Financial Services industry will be one of the prominent segments and should remain a focus for cloud service providers (CSPs).  

I believe CSPs targeting India’s Banking industry should bucket their strategy under four key themes:

  1. Partnering to Innovate and co-create solutions. CSPs must work with each business within the bank and re-imagine customer journeys and process workflow. This would mean banking domain experts and engineering teams of CSPs working with relevant teams within the bank. For some customer journeys, the teams have to go back to first principles and start from scratch i.e the financial need of the customer and how it is being re-imagined and fulfilled in a digital world.
    CSPs should also continue to engage with all ecosystem partners of banks to co-create cloud-native solutions. These partners could range from fintechs to vendors for HR, Finance, business reporting, regulatory reporting, data providers (which feeds into analytics engine).
    CSPs should partner with banks for experimentation by providing test environments. Some of the themes that are critical for banks right now are CRM, workspace virtualisation and collaboration tools. CSPs could leverage these themes to open the doors. API banking is another area for co-creating solutions. Core systems cannot be ‘lifted & shifted’ to the cloud. That would be the last mile in the digital transformation journey.
  2. Partnering to mitigate ‘fear of the unknown’. As in the case of any key strategic shift, the tone of the executive management is important. A lot of engagement is required with the entire senior management team to build the ‘trust quotient’ of cloud. Understanding the benefits, risks, controls and the concept of ‘shared responsibility’ is important. I am an AWS Certified Cloud Practitioner and I realise how granular the security in the cloud can be (which is the responsibility of the bank and not of the CSP). This knowledge gap can be massive for smaller banks due to the non-availability of talent. If security in the cloud is not managed well, there is an immense risk to the banks.
  3. Partnering for Risk Mitigation. Regulators will expect banks to treat CSPs like any other outsourcing service providers. CSPs should work with banks to create robust cloud governance frameworks for mitigating cloud-related risks such as resiliency, cybersecurity etc. Adequate communication is required to showcase the controls around data privacy (data at rest and transit), data sovereignty, geographic diversity of Availability Zones (to mitigate risks around natural calamities like floods) and Disaster Recovery (DR) site.
  4. Partnering with Regulators. Building regulatory comfort is an equally important factor for the pace and extent of technology adoption in Financial Services. The regulators expect the banks to have a governance framework, detailed policies and operating guidelines covering assessment, contractual consideration, audit, inspection, change management, cybersecurity, exit plan etc. While partnering with regulators on creating the framework is important, it is equally important to demonstrate that banks have the skill sets to run the cloud and manage the risks. Engagement should also be linked to specific use cases which allow banks to effectively compete with fintech’s in the digital world (and expand financial access) and use cases for risk mitigation and fraud management. This would meet the regulator’s dual objective of market development as well as market stability.

Financial Services is a large and growing market for CSPs. Fintechs are cloud-native and certain sectors in the industry (like non-banks and insurance companies) have made progress in cloud adoption. It is well understood that the banks that were early adopters of cloud have clearly gained market share during COVID-19. Banks are keen to adopt cloud but need a partnership approach balancing innovation with risk management so that it is ‘not one step forward and two steps back’ for them.

The views and opinions mentioned in the article are personal.
Anupam Verma is part of the Leadership team at ICICI Bank and his responsibilities have included leading the Bank’s strategy in South East Asia to play a significant role in capturing Investment, NRI remittance, and trade flows between SEA and India.

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Industries of the Future – Ecosystm Bytes

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Industries continue to innovate and disrupt to create and maintain a competitive edge – and their technology partners evolve their solution offerings to empower them.

We bring to you latest industry news from the Healthcare, Financial Services, Retail, Travel & Hospitality and Entertainment & Media industries to show you how organisations are leveraging technology. Find out more about organisations such as Services Australia, Paypal, Walmart, Zara and Amex – and how tech providers such as IBM, Oracle, Google and Uplift are supporting organisations across industries.

View the latest Ecosystm Bytes on Industries of the Future below, and reach out to our experts if you have questions.


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Fueling Asia’s Innovation Ecosystem

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Since the start of this millennium, no region has transformed as much as Asia. There has been significant paradigm shifts in the region and the perception that innovation starts in the US or in Europe and percolates through to Asia after a time lag, has been shattered. Asia is constantly demonstrating how dynamic, and technology-focused it is. This is getting fueled by the impact of the growing middle class on consumerism and the spirit of innovation across the region. The region has also seen a surge in new and upcoming business leaders who are embracing change and looking beyond success to creating impact.

What is Driving Innovation in Asia?

The “If you ain’t got it, build it” attitude. One of the key drivers of this shift is the age of the average population in Asia. According to the UN the Asia Pacific region has nearly 60% of the world’s youth population (between the age of 17-24). With youth comes dynamism, a desire to change the world, and innovation. As this age group enters the workforce, they will transform their lives and the companies they work in. They are already showing a spirit of agility when it comes to solving challenges – they will build what they do not have.

The Need to enable Foundational Shifts. The younger generation is more aware of environmental, social and governance issues that the world continues to face. Many of the countries in the region are emerging economies, where these issues become more apparent. COVID-19 has also inculcated an empathy in people and they are thinking of future success in terms of impact. The desire to enable foundational shifts is giving direction to the transformation journey in the region. The wonderful new paradigm that is the Digital Economy allows us to cut across all segments; and technology and its advancements has immense potential to create a more sustainable and inclusive future for the world. 

Realising the Power of Momentum. The pandemic has caused major disruptions in the region. But every crisis also presents an opportunity to perhaps re-imagine a brighter world through a digital lens.  The other thing that the pandemic has done is made people and organisations realise that to succeed they need to be open to change – and that momentum is important. As organisations had to pivot fast, they realised what I have been saying for years – we shouldn’t “let perfect get in the way of better”. This adaptability and the readiness to fail fast and learn from the mistakes early for eventual success, is leading to faster and more agile transformation journeys.     

Where are we seeing the most impact?

Industries are Transforming. There are industries such as Healthcare and Education that had to transform out of a necessity and urgency brought about by the COVID-19 pandemic. This has led to a greater impetus for change and optimism in these industries. These industries will continue to transform as governments focus significantly on creating “Social Safety Nets” and technology plays a key role in enabling critical services across Health, Education and Food Security. Then there are industries, such as the Financial Services and Retail, that had a strong customer focus and were well on their digital journeys before the pandemic. The pandemic boosted these efforts.

Ecosystm Industry Optimism Index

But these are not the only industries that are transforming. There are industries that have been impacted more than others. There are several instances of how organisations in these industries are demonstrating not only resilience but innovation. The Travel & Hospitality industry has had several such instances. As business models evolve the industry will see significant changes in digital channels to market, booking engines, corporate service offerings and others, as the overall Digital Strategy is overhauled.

Technologies are Evolving. Organisations depended on their tech partners to help them make the fast pivot required to survive and succeed in the last year – and tech companies have not disappointed. They have evolved their capabilities and continue to offer innovative solutions that can solve many of the ongoing business challenges that organisations face in their innovation journey. More and more technologies such as AI, machine learning, robotics, and digital twins are getting enmeshed together to offer better options for business growth, process efficiency and customer engagement. And the 5G rollouts will only accelerate that. The initial benefits being realized from early adoption of 5G has been for consumers. But there is a much bigger impact that is waiting to be realised as 5G empowers governments and businesses to make critical decisions at the edge.

Tech Start-ups are Flourishing.  There are immense opportunities for technology start-ups to grow their market presence through innovative products and services. To succeed these companies need to have a strong investment roadmap; maintain a strong focus on customer engagement; and offer technology solutions that can fulfil the global needs of their customers. Technologies that promote efficiency and eliminate mundane tasks for humans are the need of the hour. However, as the reliance on technology-led transformation increases, tech vendors are becoming acutely aware that they cannot be best-in-class across the different technologies that an organisation will require to transform. Here is where having a robust partner ecosystem helps. Partnerships are bringing innovation to scale in Asia.

We can expect Asia to emerge as a powerhouse as businesses continue to innovate, embed technology in their product and service offerings – and as tech start-ups continue to support their innovation journeys.


Ecosystm CEO Amit Gupta gets face to face with Garrett Ilg, President Asia Pacific & Japan, Oracle to discuss the rise of the Asia Digital economies, the impact of the growing middle class on consumerism and the spirit of innovation across the region.

Designed for change in a rising digital economy
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Building Trust in your AI Solutions

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In this blog, our guest author Shameek Kundu talks about the importance of making AI/ machine learning models reliable and safe. “Getting data and algorithms right has always been important, particularly in regulated industries such as banking, insurance, life sciences and healthcare. But the bar is much higher now: more data, from more sources, in more formats, feeding more algorithms, with higher stakes.”

Building trust in algorithms is essential. Not (just) because regulators want it, but because it is good for customers and business. The good news is that with the right approach and tooling, it is also achievable.

Getting data and algorithms right has always been important, particularly in regulated industries such as banking, insurance, life sciences and healthcare. But the bar is much higher now: more data, from more sources, in more formats, feeding more algorithms, with higher stakes. With the increased use of Artificial Intelligence/ Machine Learning (AI/ML), today’s algorithms are also more powerful and difficult to understand.

A false dichotomy

At this point in the conversation, I get one of two reactions. One is of distrust in AI/ML and a belief that it should have little role to play in regulated industries. Another is of nonchalance; after all, most of us feel comfortable using ‘black-boxes’ (e.g., airplanes, smartphones) in our daily lives without being able to explain how they work. Why hold AI/ML to special standards?

Both make valid points. But the skeptics miss out on the very real opportunity cost of not using AI/ML – whether it is living with historical biases in human decision-making or simply not being able to do things that are too complex for a human to do, at scale. For example, the use of alternative data and AI/ML has helped bring financial services to many who have never had access before.

On the other hand, cheerleaders for unfettered use of AI/ML might be overlooking the fact that a human being (often with a limited understanding of AI/ML) is always accountable for and/ or impacted by the algorithm. And fairly or otherwise, AI/ML models do elicit concerns around their opacity – among regulators, senior managers, customers and the broader society. In many situations, ensuring that the human can understand the basis of algorithmic decisions is a necessity, not a luxury.

A way forward

Reconciling these seemingly conflicting requirements is possible. But it requires serious commitment from business and data/ analytics leaders – not (just) because regulators demand it, but because it is good for their customers and their business, and the only way to start capturing the full value from AI/ML.

1. ‘Heart’, not just ‘Head’

It is relatively easy to get people excited about experimenting with AI/ML. But when it comes to actually trusting the model to make decisions for us, we humans are likely to put up our defences. Convincing a loan approver, insurance under-writer, medical doctor or front-line sales-person to trust an AI/ML model – over their own knowledge or intuition – is as much about the ‘heart’ as the ‘head’. Helping them understand, on their own terms, how the alternative is at least as good as their current way of doing things, is crucial.

2. A Broad Church

Even in industries/ organisations that recognise the importance of governing AI/ML, there is a tendency to define it narrowly. For example, in Financial Services, one might argue that “an ML model is just another model” and expect existing Model Risk teams to deal with any incremental risks from AI/ML.

There are two issues with this approach:

First, AI/ML models tend to require a greater focus on model quality (e.g., with respect to stability, overfitting and unjust bias) than their traditional alternatives. The pace at which such models are expected to be introduced and re-calibrated is also much higher, stretching traditional model risk management approaches.

Second, poorly designed AI/ML models create second order risks. While not unique to AI/ML, these risks become accentuated due to model complexity, greater dependence on (high-volume, often non-traditional) data and ubiquitous adoption. One example is poor customer experience (e.g., badly communicated decisions) and unfair treatment (e.g., unfair denial of service, discrimination, misselling, inappropriate investment recommendations). Another is around the stability, integrity and competitiveness of financial markets (e.g., unintended collusion with other market players). Obligations under data privacy, sovereignty and security requirements could also become more challenging.

The only way to respond holistically is to bring together a broad coalition – of data managers and scientists, technologists, specialists from risk, compliance, operations and cyber-security, and business leaders.

3. Automate, Automate, Automate

A key driver for the adoption and effectiveness of AI/ ML is scalability. The techniques used to manage traditional models are often inadequate in the face of more data-hungry, widely used and rapidly refreshed AI/ML models. Whether it is during the development and testing phase, formal assessment/ validation or ongoing post-production monitoring,  it is impossible to govern AI/ML at scale using manual processes alone.

o, somewhat counter-intuitively, we need more automation if we are to build and sustain trust in AI/ML. As humans are accountable for the outcomes of AI/ ML models, we can only be ‘in charge’ if we have the tools to provide us reliable intelligence on them – before and after they go into production. As the recent experience with model performance during COVID-19 suggests, maintaining trust in AI/ML models is an ongoing task.

***

I have heard people say “AI is too important to be left to the experts”. Perhaps. But I am yet to come across an AI/ML practitioner who is not keenly aware of the importance of making their models reliable and safe. What I have noticed is that they often lack suitable tools – to support them in analysing and monitoring models, and to enable conversations to build trust with stakeholders. If AI is to be adopted at scale, that must change.

Shameek Kundu is Chief Strategy Officer and Head of Financial Services at TruEra Inc. TruEra helps enterprises analyse, improve and monitor quality of machine


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Consumers at the Core of the Digital Financial Ecosystem

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The disruption that we faced in 2020 has created a new appetite for adoption of technology and digital in a shorter period. Crises often present opportunities – and the FinTech and Financial Services industries benefitted from the high adoption of digital financial services and eCommerce. In 2021, there will be several drivers to the transformation of the Financial Services industry – the rise of the gig economy will give access to a larger talent pool; the challenges of government aid disbursement will be mitigated through tech adoption; compliance will come sharply back into focus after a year of ad-hoc technology deployments; and social and environmental awareness will create a greater appetite for green financing. However, the overarching driver will be the heightened focus on the individual consumer (Figure 1).

2021 will finally see consumers at the core of the digital financial ecosystem.

Ecosystm Advisors Dr. Alea Fairchild, Amit Gupta and Dheeraj Chowdhry present the top 5 Ecosystm predictions for FinTech in 2021 – written in collaboration with the Singapore FinTech Festival. This is a summary of the predictions; the full report (including the implications) is available to download for free on the Ecosystm platform.

The Top 5 FinTech Trends for 2021

 #1 The New Decade of the ‘Empowered’ Consumer Will Propel Green Finance and Sustainability Considerations Beyond Regulators and Corporates

We have seen multiple countries set regulations and implement Emissions Trading Systems (ETS) and 2021 will see Environmental, Social and Governance (ESG) considerations growing in importance in the investment decisions for asset managers and hedge funds. Efforts for ESG standards for risk measurement will benefit and support that effort.

The primary driver will not only be regulatory frameworks – rather it will be further propelled by consumer preferences. The increased interest in climate change, sustainable business investments and ESG metrics will be an integral part of the reaction of the society to assist in the global transition to a greener and more humane economy in the post-COVID era. Individuals and consumers will demand FinTech solutions that empower them to be more environmentally and socially responsible. The performance of companies on their ESG ratings will become a key consideration for consumers making investment decisions. We will see corporate focus on ESG become a mainstay as a result – driven by regulatory frameworks and the consumer’s desire to place significant important on ESG as an investment criterion.

#2 Consumers Will Truly Be ‘Front and Centre’ in Reshaping the Financial Services Digital Ecosystems  

Consumers will also shape the market because of the way they exercise their choices when it comes to transactional finance. They will opt for more discrete solutions – like microfinance, micro-insurances, multiple digital wallets and so on. Even long-standing customers will no longer be completely loyal to their main financial institutions. This will in effect take away traditional business from established financial institutions. Digital transformation will need to go beyond just a digital Customer Experience and will go hand-in-hand with digital offerings driven by consumer choice.

As a result, we will see the emergence of stronger digital ecosystems and partnerships between traditional financial institutions and like-minded FinTechs. As an example, platforms such as the API Exchange (APIX) will get a significant boost and play a crucial role in this emerging collaborative ecosystem. APIX was launched by AFIN, a non-profit organisation established in 2018 by the ASEAN Bankers Association (ABA), International Finance Corporation (IFC), a member of the World Bank Group, and the Monetary Authority of Singapore (MAS). Such platforms will create a level playing field across all tiers of the Financial Services innovation ecosystem by allowing industry participants to Discover, Design and rapidly Deploy innovative digital solutions and offerings.

#3 APIfication of Banking Will Become Mainstream

2020 was the year when banks accepted FinTechs into their product and services offerings – 2021 will see FinTech more established and their technology offerings becoming more sophisticated and consumer-led. These cutting-edge apps will have financial institutions seeking to establish partnerships with them, licensing their technologies and leveraging them to benefit and expand their customer base. This is already being called the “APIficiation” of banking. There will be more emphasis on the partnerships with regulated licensed banking entities in 2021, to gain access to the underlying financial products and services for a seamless customer experience.

This will see the growth of financial institutions’ dependence on third-party developers that have access to – and knowledge of – the financial institutions’ business models and data. But this also gives them an opportunity to leverage the existent Fintech innovations especially for enhanced customer engagement capabilities (Prediction #2).   

#4 AI & Automation Will Proliferate in Back-Office Operations

From quicker loan origination to heightened surveillance against fraud and money laundering, financial institutions will push their focus on back-office automation using machine learning, AI and RPA tools (Figure 3). This is not only to improve efficiency and lower risks, but to further enhance the customer experience. AI is already being rolled out in customer-facing operations, but banks will actively be consolidating and automating their mid and back-office procedures for efficiency and automation transition in the post COVID-19 environment. This includes using AI for automating credit operations, policy making and data audits and using RPA for reducing the introduction of errors in datasets and processes.

There is enormous economic pressure to deliver cost savings and reduce risks through the adoption of technology. Financial Services leaders believe that insights gathered from compliance should help other areas of the business, and this requires a completely different mindset. Given the manual and semi-automated nature of current AML compliance, human-only efforts slow down processing timelines and impact business productivity. KYC will leverage AI and real-time environmental data (current accounts, mortgage payment status) and integration of third-party data to make the knowledge richer and timelier in this adaptive economic environment. This will make lending risk assessment more relevant.

#5 Driven by Post Pandemic Recovery, Collaboration Will Shape FinTech Regulation

Travel corridors across border controls have started to push the boundaries. Just as countries develop new processes and policies based on shared learning from other countries, FinTech regulators will collaborate to harmonise regulations that are similar in nature. These collaborative regulators will accelerate FinTech proliferation and osmosis i.e. proliferation of FinTechs into geographies with lower digital adoption.

Data corridors between countries will be the other outcome of this collaboration of FinTech regulators. Sharing of data in a regulated environment will advance data science and machine learning to new heights assisting credit models, AI, and innovations in general. The resulting ‘borderless nature’ of FinTech and the acceleration of policy convergence across several previously siloed regulators will result in new digital innovations. These Trusted Data Corridors between economies will be further driven by the desire for progressive governments to boost the Digital Economy in order to help the post-pandemic recovery.


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