Author: Abhideep Jain
Banks act as a backbone to any economy by efficiently regulating the flow of capital from those who have in surplus to those who have better use for it. The Coronavirus pandemic has been a shock not only to physical supply chains but also to the financial supply chain. A falling interest rate could lead to more activity on the lending side and enable growth in the post-pandemic era, but this would also mean lower interest rates on borrowings. Banks’ earnings could be impacted severely due to an ever-decreasing interest rate regime. European regulators have already instructed banks to forgo dividends to shore up capital. While the big banks in the US have defended their dividend payments, many have temporarily halted share buybacks. Many banks are offering forbearance on loans to small businesses and installment holidays on home loans and auto loans to their retail customers. This would test capital management in the short run. The adequately capitalized ones who can come up with purpose-driven lending will emerge as victorious. Others would take a hit. On the operations front, the work from home culture will drive numerous digital and cloud initiatives. It will also demand greater investments from the banks to ensure data privacy and adequate security.
Here are the various ways through which banks can leverage advanced analytics to function smoothly and mitigate risks during these uncertain times.
Managing Credit Quality
The current pandemic could rejig any bank’s loan book substantially. While banks expect some delinquencies from SME and consumer lending, there is going to be even more pressure to toughen the approval standards for new loans. Banks could see a cushion in their capital coming from the Fed’s interest rate cut and a massive stimulus package. Next, the credit risk teams have to do an exceptionally fine job of making these additional loans. Their capabilities could now be augmented by advanced analytics to help in the better determination of the creditworthiness of any individual or business. Using advanced algorithms, banks can proactively identify the potential delinquencies early in their lifecycle and take corrective actions. In these pressing times, there is even more need for banks to come up with a centralized analytics wing that could use data from across the organization and aid the RMs by creating a 360° profile of their customers.
Stress Testing for New Scenarios
The 2010 Dodd-Frank Act reforms and the CCAR after the 2008 sub-prime mortgage crisis have led to periodic monitoring of capital adequacy at US banking institutions. The bank regulatory bodies came up with stress tests designed to determine the ability of a bank or financial institution to deal with adverse economic scenarios. Some of the scenarios suggested by regulators include:
• What happens if the unemployment rate rises to x% in a specific year?
• What happens if oil prices rise by z%?
• What happens if there is a war outbreak?
As a fallout of this pandemic, banks should now also consider scenarios on worldwide pandemics such as COVID-19 or disruptions caused by climate change. The recent turn of events shows that the possibility of these scenarios can no longer be negated.
Banks’ risk management teams can quickly build multiple scenarios like climate change, pandemics, natural or man-made disasters into their calculations and predict its effect on banks’ capital and margins. As easy as it may sound, getting the right data for predicting such alternative futures is a daunting task. Banks need to normalize the data to retrain or redevelop, and have a defined process for model overlays to address the limitations of scorecards and forecasts used in such decisions. Banks who successfully transition to data-led governance and a strong data culture are better positioned to correctly incorporate such scenarios.
Changing Operations Mix at Banks
With many local and nationwide shutdowns, banks experienced a surge of calls and tickets at their contact centers. The preference of digital channels (like mobile payment, digital wallet, online banking, point of sale) over traditional ones crammed some channels leaving others with unused capacity. There were sudden cash withdrawals from ATMs with the withdrawal size being unusually large. Nevertheless, customers expected a seamless experience across all digital channels.
In such a scenario, banks could face a dearth of customer care representatives, CRM software licenses, or low availability of computing and networking resources. An AI engine can be used to predict and redistribute peak demand levels during an exigency. Sophisticated machine learning and deep learning techniques can be used to predict the channel occupancy levels based on variables like network downtime, marketing activities, new product launches, etc. Digital savvy banks could also explore AI-led chatbots to help decrease the traffic of tickets and calls at a contact center.
Banks could simulate such spikes and come up with flexible and scalable solutions to staffing and resource optimization. The key, especially during uncertain times, would be to establish a robust data pipeline to consume real-time data.
Adaptable Marketing Promotional Mix
Pandemics can lead to a significant change in the promotional mix strategy. Media consumption habits could change dramatically. Due to cancellations of sports events and concerts, marketing managers need to reallocate their marketing budgets to a different TV channel or even a different medium. It makes less sense for banks to put a lot of dollars on search marketing or social media marketing during pandemic times. It would be much wiser to withhold that budget until the situation becomes normal again. It is very difficult to get the mindshare of customers when the primary thing customers are interested in is content that is breaking the news at that instant.
One of the few ways in which banks can promote themselves could be through their social messaging. Purposeful lending by banks to pandemic hit businesses and individuals could spread a good word about the bank in the community.
Marketing attribution models with Bayesian models and VAR (vector autoregression) techniques working at the backend can help in forecasting the right channel mix. Scenarios for pandemics, war, natural disasters, global recessions built on top of these models can take into account the sudden movement of independent variables and tailor the marketing mix accordingly.
Increased Cyber Security due to Work from Home
Banks have always been very apprehensive about the Silicon Valley way of making people work from remote locations. This pandemic and subsequent lockdowns have been the largest sandbox for banks, a little forced one albeit, to analyze if they can pull all operations with a major workforce connecting remote. But this poses some challenges. Bloomberg has warned the banks to expect an increase in cyber-attacks during and post the Coronavirus outbreak. As business-critical data leaves the banks’ physical network, there have been increased reports of employee-led frauds in banks. Cases of phishing and social hacking are higher when an employee is working from unsecured networks. All these threats are not meant for banks to necessarily retreat to their older ways of working. AI and analytics could alert such transactions even when the miscreant is only starting to scheme things. Such systems could flag alerts by looking at anomalous behavior of users, unusual query logs, unauthenticated accesses, and flagged devices, etc. Scoring algorithms can bucket fraudulent activities with severity and priority indicators. Infosec teams can choose to intervene in the flagged cases as desired.
Finally, each bank should have their takeaways from the current crisis to build an even more financially and operationally resilient organization. Data is at the center of all this. Leading institutions are falling back on data as the fuel to help navigate through this crisis. Tiger Analytics has created a COVID-19 response playbook to assist financial institutions to tackle the current situation. As the quote goes, “never let a crisis go to waste”. Banks that can make a quick and decisive transformation reflecting the new environment will survive and thrive.