3+1 Types of Digital Transformations and How to Prioritize Them

A former insurance CEO once said that if you want to understand risk in financial services, you should start by looking at the products you are offering. I have been exploring how incumbents in financial services, and specifically risk management, should change to embrace FinTech. Inevitably then the subject of ‘digital transformation’ comes up. I have been speaking with various colleagues and friends recently and I realised that there are rather different forms of digital transformations with different implications for risk management and the business.  Here is my take on the various types. 

1.       Data-driven

Someone in the business takes the initiative and starts collating, curating and using the many data sources in the business to address specific analytical issues and enhance the quality of decision making.  This represents a bottom-up transformation with potential transformational features. 

In this case, buy-in is unlikely to be an issue. The main risk management challenge may arise from the scaling up of this initiative. For example, scaling up may involve using external data rather than internal data or bringing new technology to store the data, e.g. a data lake, which needs to be integrated into existing systems. It is also important that the consideration of analytical issues in the business factors in the need to maintain (and enhance, where necessary) an understanding of the risk profile of the business. For example, if additional data allows the business to modify its underwriting approach in a significant way, you should also consider how the (different) exposures would be monitored. There are a couple of examples here.

2.       Enhancing Customer Journeys 

This can be about how customers are serviced, given their existing journeys, and might include enhancing the front-end applications or rolling out new IT equipment to service customers. Alternatively, the transformation may be about changing or enhancing aspects of customer journeys. This might include, for example, introducing chat-bots as part of customer journeys (e.g. claims management) or applying an artificial intelligence-based tool to a specific process (e.g. underwriting).

This type of transformation has become the most visible form of digital transformation thanks to the various accelerators that incumbents in financial services have created. The challenge of buy-in is typically addressed by specifying that the accelerator should partner external providers with business leaders for whom the technology may be relevant. The impact on the risk profile of the business is also dependent on the specific transformation and should be considered from the outset. 

3.       IT-enabler

There are cases where the legacy systems become the main challenge and where the adoption of cloud-based services can be part of the answer. There are several approaches here, ranging from incremental steps to a ‘big-bang’ approach. One interesting idea is focusing on reducing the functionality of the legacy system and replicating that outside using new technology. 

These transformations may be motivated by concerns about operational resilience in the short term but might also support the transformations outlined above and enable more effective risk management. 

4.       Digital ‘Non-transformation’

This involves applying new technologies in the context of a new product line where there is no transformation as such. This clearly avoids the transformation in the short term but it can also provide the business with the means to build confidence in specific technologies (AI, blockchain) and the capability to execute and bring on board new technologies.

These types of digital transformations are not mutually exclusive, but it is important to be clear that they are different. Equally, they are not substitutes for each other and the real challenge is prioritising between them. This will inevitably vary between businesses, though I believe that there are standard considerations shaping the priorities such as the need to change the culture in order to mobilize the business for the digital era and the state of the core IT infrastructure, including the need to leverage technology as an enabler.  

What do you think about these categories? 

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This Time is Different - The Digital Revolution

The August issue of Central Banking, a journal, includes my review of a book about the digital revolution by Chris Skinner.  It is a fascinating book that can change pre-determined views.  You can read the review here or below.

This Time Is Different: A book review of Digital Human by Chris Skinner, Marshall Cavendish (2018)

Mr Skinner has written two books on FinTech and banking (Digital Bank and ValueWeb), and now Digital Human is his third. This represents an opportunity to take a step back and consider some of the bigger questions about FinTech. How much of a change could this represent for banking? For financial services? For society?

His main argument is that digitalisation has reduced the cost associated with a minimum viable product beyond recognition for nearly anything in financial services. One way of looking at FinTech is as ‘one big bucket of finance and technology’ with a range of technologies from InsurTech (based on artificial intelligence) to digital currencies, with mobile wallets and peer-to-peer lending in between. Indeed, one could make the argument that it should be called ‘TechFin’ instead. However, it is possible to make overall sense of these technologies by distinguishing between those that challenge existing business structures and those that create new ones. 

One of the main aspects of the digital revolution with respect to banking is the differential effect between the (developed) West and developing world. Surprisingly, it is not in the direction you might expect. For the West overall, FinTech represents a challenge to existing business structures. Current IT systems took shape in the 1970s and 1980s at a time when now-ubiquitous ATMs were first introduced. While the front-ends of these systems have changed over time, the core architecture has not. As Mr Skinner points out, CEOs invest significantly in systems maintenance to pass on to the next CEO rather than overhauling technology. I was left wondering if this might also be a reflection of misplaced risk aversion that contributes to the relatively short tenure of CEOs.

There also seems to be a potentially systemic issue arising from the natural ageing process of the programmers who can still write code in the language of the legacy systems (COBOL). Mr Skinner observes that more than 50% of COBOL programmers are over 45 years old, so the challenge of maintaining legacy systems is not going to get any easier.

However, the real challenge does not seem to be adopting new technologies but the vertically integrated business model of banking or, as Mr Skinner puts it rather eloquently, being ‘control freaks in a proprietary operation building everything themselves’.  As usual, technology enables the challenge but does not help the incumbent figure out how the business model should evolve and how to remain profitable. Mr Skinner offers two suggestions. The first is leveraging on its capital, history and brands and repositioning the business as a trusted party that can select specialised providers, like Amazon Marketplace. The second is leveraging on the data and focusing on advice and data analytics.

Indeed, there seems to be a change in emphasis in FinTech. Between 2010 and 2014, the focus was on disrupting existing banking business models and unbundling. Since 2014, the focus has shifted to collaboration with more dynamic banks leveraging on their customers’ reach and capital.

Perhaps the key point to emphasise is that the regulatory framework has already adapted to some extent, at least in the EU where Open Banking is already a reality because of EU directives.

If you don’t work full time in FinTech, it is difficult to form an impression about how far these trends could go.  (Yes, I know there are forecasts, but they are merely forecasts.) This is where the other part of the book is particularly useful.  

In the developing world, banking tends to be restricted to affluent clients. . FinTech does not challenge major incumbents; rather, it represents more of a development opportunity. FinTech allows for servicing relatively small transactions (by Western standards) which is compensated through a relatively large number of transactions. In this way, financial inclusion becomes a business and stops being a form of charity.

Mr Skinner illustrates extensively how far and deep these trends are going. In sub-Saharan Africa, mobile banking and e-wallets lead with the overall number of accounts growing fivefold between 2011 and 2016, reaching around 275 million accounts out of 420 million mobile subscribers. Interestingly, use is not evenly spread. Institutional design continues to matter even in the age of FinTech. In some countries, these developments are led by mobile network operators and in others by banks. Some countries actively encourage partnership and agreements to enable domestic and cross-border money transfers cheaply.

This is not just a matter of convenience. If you cannot get paid reliably and must rely on cash, there is a limited number of business opportunities that can thrive. The case study of China’s Ant Financial is therefore fascinating. It starts with a problem of trust between buyers and sellers that limits the development of the e-commerce that evolved into what we call now electronic payments. One of the lessons of this is really about the central role that the consumer plays. The business scale is staggering: in 2016, the value of transactions in the peak day (called Singles’ Day) was double the amount transacted on the US’s Thanksgiving Day, Black Friday and Cyber Monday together. It’s not just payments, as there seems to be an emerging pattern that starts with electronic payments and moves to managing money, and Ant’s money market fund is already larger than JP Morgan’s US Government money market fund.

And what about society? Living longer, 3D printing, the Internet of Things and conquering space may well change how we live. I am sure you have heard before the old dictum that this time is different. Perhaps this time it is indeed, if only for banking because of FinTech.

Monitoring the Risk and Business Impact of AI-Based Solutions

AI-based solutions can shape how financial services businesses make money, whether the business model is the same or not. For an existing financial services business, the motivations may vary and range from efficiency to expanding the business. There would be project risk as with any development, but leaving that important consideration aside, it is worth bearing in mind that AI-based solutions would also impact the risk profile of the business. This may or may not be the original intention, but it becomes more likely. The key implication is that implementing an AI-based solution would require a radically different risk oversight approach by the business.

Standard computer algorithms which are not AI-based can—and do—solve complex problems. The main feature of such algorithms is that the problem is somehow defined and an algorithm developed to solve it which will produce the same answer as long as the same inputs are provided. So a credit-scoring mechanism calibrated to capture a certain type of client gives you just that.

The answers offered by an AI-based system may change over time. New data is used to reassess the underlying relationships and recalibrate the relationship between the target variable and the potential explanatory variables. This “learning” can also happen in a standard programme when there is a process of recalibration. The difference is that in the case of AI, learning would happen on a real-time basis—that’s the essence of AI.

Alternatively, with AI a target variable may not have been defined. That’s not as unusual as it might sound. For example, algorithms assessing a loan or credit card underwriting may fall in this category because there is no single rule to predict a borrower’s likelihood of repayment. New data can lead to a certain recalibration or can be used to identify new relationships between certain data. For example, over time an AI-based system might identify that outstanding debt is a better predictor of the likelihood of borrower repayment than repayment history and penalise someone with a relatively good track record of timely repayments.

The first type of AI-based solution is called “supervised machine learning” and the second one “un-supervised machine learning”. The key difference is the extent of autonomy that goes with the learning.

Consider the potential impact on conduct risk of AI-based tools. One of the expectations from Treating Customers Fairly (TCF) with respect to product governance is that they are designed to meet the needs of identified consumer groups and are targeted accordingly. This requires a clear business strategy, including identification of the target market through a combination of qualitative and quantitative research and oversight of the business to ensure that it is aligned with initial expectations of customers and business generated. Take the example of automated investment services covered in a recent FCA review. These providers would rely on some type of AI-based solution, whether supervised or unsupervised machine learning. The possibility of capturing different customers or the advice generated being different from what was envisaged cannot be ruled out. The challenge is how to put in place a monitoring approach which ensures that outcomes and risks which arise are consistent with the expectations in the business plan.

Something similar can apply from the perspective of credit risk, impacting the quality of the portfolio and performance. Suppose you have been targeting retail customers with a specific risk rating for a credit card business. If you roll out an AI-based solution to enhance the efficiency of product underwriting, you would need to have in place mechanisms to ensure that the credit quality of the portfolio is consistent with your expectations—or else change those expectations. Both options are fine. You may want to keep your target credit rating constant and seek more volume, or perhaps you see AI-based solutions as a more robust tool to support decision making and, in a controlled manner, can relax your target rating. Regardless of your choice, you would need to put in place a credit risk monitoring approach that is suited to the new AI-based solutions, as well as ensure that the business understands the portfolio implications of “learning” that is at the core of an AI-based solution system.

The salient point to take away is that the roll-out plan of AI-based tools may focus on the launch. However, the greatest challenge may well be the need to provide for the ongoing and timely monitoring of the AI-based tools and their integration in business governance and risk management, which I will cover in the next post.