By Diederick van Thiel
The damage of the COVID-19 lock down for businesses and economies is becoming more visible every day. Forecasting institutions and scenario planners are estimating significant contractions in global GDP. In the Eurozone, GDP contracted by -3,6% in the first quarter of 2020 and by -12,1% in the second! The United Kingdom even showed a 20,4% decrease in GDP due to the long lock down, while the USA reported a 9,5% decrease in the second quarter! As a consequence, unemployment rates rise fast. The severity of the outbreak and the response varies by country, factors which will affect the size of the contractions. The future scenarios are uncertain. Some say the economy is expected to recover slowly, with subdued consumer spending and business investment; the ECB foresees a Eurozone GDP contraction of –8,7% in 2020 overall, while a decrease of 3,8% is expected in the USA.
In the mid-year reports of the Western banks we see all of them at least double their costs of risk. That’s prudent, but bad news for the bank’s effectiveness. Especially as the net interest income from banking over the same period decreased with all leading western banks. Strategy consultants like Mc Kinsey and BCG dive into this area of post-COVID risk management. Digital strategy company Ecology Innovations, in combination with Cambridge University used data from the financial crisis to develop a set of metrics and scenarios to improve the effectiveness of banks’ business models in these times of high uncertainty. One important metric for the business model effectiveness is the currently exploding risk to income. The mid-term results of western banks show a dazzling ratio development of risk to income. More aggressive banks like HSBC, Barclays and for example ING traditionally show risk to income ratios between 4% and 9%. Now however they report a risk to income between 26% and 32%! BNP Paribas and Credit Agricole, who are usually less aggressive with risk to income ratios of around 20%, see this ratio double in the first half year of 2020. The exploding risk to income shows a double whammy hit for banks’ business models. On the one hand side it shows a decreasing net income and on the other hand side it shows an exploding cost of risk. As a digital banking non-executive director this keeps me, and many C-level colleagues, awake at night. The numbers show that our business models need urgent revisioning. In a poll that I did last week this picture was confirmed. NOW is the moment!
As also stated in my first blog in this series on banks effectiveness two weeks ago, this is the year in which banking C-level executives have to invest in their effectiveness. The year in which they first have to fix their core: re-evaluate their business models and re-asses their enterprise risk frameworks. And to do so, they fundamentally have to better understand their customers and digitize their value chains.
But, because things are easily said, I come up with some steps to drive successful business model transformations.
First of all, bank leaders have to understand how to better differentiate between customers in the same segment. It’s about going deeper into borrower’s financials, business model and psychometric profile to estimate resilience to COVID-19 effects. This brings in new and more granular requirements for the bank’s KYC for new and existing customers.
Secondly, the conventional sources of data typically used in credit-risk assessments became obsolete overnight. Financial resilience will be determined less by pre-COVID-19 profitability than by indebtedness, liquidity and a high level of financial self-determination —attributes that will establish a borrower’s ability to weather the crisis. Suddenly, the six- or 12-months-old data on which lenders relied in the past are no longer useful in evaluating the resilience of individual borrowers. Creative approaches to acquire and utilize high-frequency data are at the order of the day. At innovative risk decisioning provider AdviceRobo, we have insights in the performance of many groups of dynamic behavioral data. Open banking data, psychometric data, biometric data and mobile phone data show great predictive power for risk. We encourage banks to integrate these kind of new data groups in their KYC for smarter risk decisioning. Innovative high frequency data are in our opinion the solution for building deeper customer profiles for customer acquisition and smarter risk mitigation and collection strategies. Advanced modelling and analytics can seriously help find the way to more granular customer profiling and risk decisioning. Different for banks today is that many of their affected borrowers never imagined that they would be unable to pay their debts. The level of financial self-determination is a high predictive psychometric feature for the recovery of the debt. Traditional data and models do not detect this and still banks need to collect their debts. On top of that, traditional collections methods (calls, email, letters) are becoming less effective as customer preferences decisively shift toward digital interaction with their banks. Meanwhile, bank workout departments have shrunk to a fraction of the capacity that will be needed.
Effective banks therefore use this financial crisis to fundamentally transform their business models and enterprise risk frameworks with new and high frequent data and advanced modelling. It provides them deeper insight in customer value and better shapes their acquisition & retention strategies. But most of all, it will give them deeper insight in where credit, operational and market risks will come from as it will bring breakthroughs in their models type – and type 2- errors. It will also lead the way in smart data-driven digitization and robotization of their credit value chains driving personalized treatment solutions like dialogue programs and virtual banking assistants.
Beyond this horizon are approaches using more real-time business data in decision making and advanced analytics to review credit-underwriting processes and fuel digital dialogue programs and virtual banking assistants to prevent defaults or to collect them smarter. At Finovate 2019, I demoed JACQ as an example outlook to this new world of integrated high frequency data. I studied advanced models driving these kind of applications during my research on artificial intelligent credit risk prediction.
Today’s winners focus on digitization for fundamentally lowering their costs and collecting deep, granular data on their customers’ behavior and personality. Although still making significant losses, digital banks like Revolut, Monzo and N26 already have this kind of effective operations and advanced data methods in place. Revolut claims to have over 8 million digital customers. N26 5 million and Monzo 3 million. Effective traditional banks have a huge opportunity as they traditionally have much larger customer bases. On the other hand, they are stuck with the traditional business models and risk frameworks. Winners put digitization as the top priority in their strategies. Barclays is UK’s largest digital bank with over 10 million digital customers (50%) and a 68% growth in mobile customers. ING claims to have 13 million (34%) digital customers, including their ING Direct operations. Santander has 37 million digital customers (30%) and 3,6 million mobile customers. BNP Paribas claims to have over 8 million digital customers (24%), including their digital Hello bank.
The transition to new business models and enterprise risk frameworks will help banks cope with the present crisis but also serve as a rehearsal for the change that, in our view, credit-risk management will have to make. The best banks will keep and expand these practices after the crisis, to manage credit risk more effectively while better serving clients and helping them return to growth more quickly. But most important: don’t waste time! Post COVID-risk decisioning is about better detecting and treating! It starts NOW!
This article has also been published on Finextra (25 September 2020).