Banks have a responsibility to respond to their customers’ needs through the
coronavirus pandemic while, at the same time, preparing for a precipitous economic downturn and a different, post-COVID world. A poorly organized or ad hoc response could amplify the economic impact. Banks that take an empathic approach will help the community
at a time of extreme anxiety.
This is about dynamic data analysis with heart.
Start from the top
Institutions that haven’t already done so must move from business continuity to crisis management. Implement scenario modelling and contingency planning for the pandemic’s impact, the interrelated economic downturn and what we can say, with a level of certainty,
lays beyond. The situation is too unpredictable to look for greater accuracy, but firms can start here:
- Model based on what you’re already seeing – for example, the spike in financial distress, customers asking to change loan repayment plans and companies cutting near term spend.
- Calculate how to protect margins when demand and supply is disrupted and measure systemic risks such as the number of job losses and contraction of the economy.
- Run the numbers on revenue, potential credit losses, cash flow and liquidity for six months, 12 months and 12+ months, and be willing to go with the 80/20 rule.
Risk and finance need a seat at the table as banks divert resources from existing books to simulate the state of financial affairs on a dynamic, forward-looking basis. This will test the agility of your asset-liability management (ALM) infrastructure – in
terms of integration of risk and finance data and the efficacy of that data, as well as the adaptability of models and operationalizing them.
Coordinate input to scenarios with the federal/local government(s), regulators, the Fed, central banks, banking associations, SMEs, payments firms/associations, credit bureaus, data analytics firms, technologists, consumer associations and others.
Maintaining empathy means driving models through the lens of what will help the customer and, at the same time, be sustainable. Ultimately, you need to balance credit needs with your changing acceptable levels of risk tolerance.
React to your customers’ immediate needs, but with caution
An overnight switch to “life at home” is catalyzing digital banking, forcing those that do not bank online or by mobile to make the switch. Again, it’s about the analytics – the ability to quickly understand new behavioral patterns and respond and nudge
appropriately. For instance, consider raising the limits of contactless payments to enable your customers to buy what they need online without raising fraud alerts. This is also an opportune time for U.S. banking to gain traction on its payment’s evolution,
which considerably lags the rest of the world.
With the unprecedented numbers of newly unemployed and furloughed workers requesting bridging loans, you must quickly discern good credit risks from those most at risk of becoming delinquent post COVID. To do this, you’ll need to adapt models or build new
models that can calculate, in real time, changes to the customer’s or company’s credit risk based on your new risk tolerance level.
According to the Federal Reserve’s most recent Report on the Economic Well-Being of U.S. Households, released in May 2019, 40 percent of U.S. adults lack the funds to handle a $400 unexpected expense. It’s a circumstance that signals the need for
compassion in collections. This is a big ask of banks, but one they must embrace to support the financial wellness of communities. Success will require complete transparency in collection and recovery processes and a moderated level of forgiveness. Options
- Skip payments and overdraft waivers;
- Relaxed reporting on sudden non-mortgage payment to credit bureaus; and
- Adjusted payment terms that can be actioned after the pandemic lessens.
To help ease financial anxiety, banks should promote personal financial management and financial wellness tools. Such services can help customers plan for expected wage loss, financial hardships, medical expenses and other unexpected costs by offering early
wage access or loans, for example.
Virtual assistants – chatbots, bots, conversational interfaces – can help with customers’ growing online activities while helping alleviate the current staffing crisis as banks set up remote customer care offices. But creating a nuanced, fluent and intelligent
bot is difficult. The technology draws from many disciplines, including computer science, computational linguistics, AI, natural language processing, machine learning and deep learning in the contextual analysis of unstructured data. A frustrating bot experience
can lead to the customer to hang up or close the browser.
Fallout for banks
Experts predict the pandemic’s shockwaves will have a greater impact on the economy than the financial crisis of 2008. Early estimates suggest the world’s output could decline at least 25 percent. Still, banks find themselves in stronger position than most
other industries. The liquidity buffers regulated after the financial crisis, although the bane of the industry, should now sustain funding as we navigate the economy’s temporary closure. Furthermore, banks find themselves well-positioned to act as a conduit
to government aid.
But it will be far from plain sailing. The industry had already started job cuts, with more than 75,000 losses reported across industry giants like JPMC, HSBC, Deutsche Bank, Citigroup, Barclays and Société Générale in recent months. Cuts will undoubtedly
continue at a steeper trajectory. That makes analytics more important than ever. Incumbent banks that cannot quickly react to customer needs will be hardest hit, as customers find faster and cheaper alternatives from their sofas. Innovative, agile banks have
the best chance of surviving and thriving once this crisis has passed.
Data analysis with heart
The crisis at hand is no reason for fatalism. The banking industry is five times more regulated than other sectors and, therefore, better prepared to weather the storm. Banks have capital and liquidity buffers between 12-14 percent. That’s a lot of cash.
Nevertheless, banks risk being walloped by their own inability to respond and by lending to businesses and customers that fall into bankruptcy or emerge financially damaged. Calculating trade-offs, making informed decisions and underwriting those decisions
is dependent on advanced, data-driven technologies – a central analytics engine, access to vast amounts of data and understanding changing behavioral patterns, and the ability to rapidly deploy and monitor models and AI techniques.
There’s a lot to think about, and even more to do. While change is the constant, we need empathy and intelligence to guide our way: being empathic is the
right thing to do, and understanding the cost benefits of your decisions is the
smart thing to do. Embracing both will help pave the way to stronger financial footing and the trust of your customers.