Financial Services: Preparing for the Looming Credit Crisis
The COVID-19 pandemic has significantly affected financial institutions – slowing the growth of loan originations, increasing credit costs, contracting economic activity, and causing record levels of unemployment claims. It has also impacted the banking industry’s exposure to the hard-hit travel, entertainment, and energy industries. While no one knows the severity or longevity of the impact on the global economy, banks have significantly increased the provisions for Allowance for Credit Loss (ACL) to build significant loan loss reserves.
In response to the pandemic, on March 28, 2020, President Trump signed into law the Coronavirus Aid, Relief, and Economic Security (CARES) Act. This monumental $2.2 trillion stimulus package is intended to provide financial relief to businesses, individuals, and public institutions affected by the coronavirus pandemic. Government stimulus and forbearance for pandemic relief have attempted to help to mitigate some of these challenges.
Banks must provide forbearance for up to 180 days and it can be extended for up to an additional 180 days, at the request of the borrower (Ref: Section 4022: Foreclosure Moratorium and Consumer Right to Request Forbearance in CARES Act). No extra fees, penalties, or interest may be charged to the borrower. Forbearance is primarily in the form of short-term payment deferrals and fee waivers – up to 3 months for card and auto loans, and up to 12 months for mortgage and student loan portfolios. Banks can expect to see higher enrollment for auto, student loans and mortgages compared to credit card portfolios. These forbearances would delay delinquencies and charge-offs to the latter part of 3Q/beginning of 4Q 2020, when the forbearance period ends. Customers will have to pay the accrued/deferred interest and principal, either as a lump sum or in up to 12 installments, which are added to regular monthly payments. If this additional risk is not managed well, the bank balance sheet will suffer badly. Disciplined underwriting and account management (such as timely credit line increase/decrease) would not only improve credit quality, but also effectively manage the tsunami of delinquencies and net charge-off at the end of the forbearance period.
For the next couple of quarters, banks also expect to face lower purchase volume and lower demand for credit, as consumer behavior tends to be conservative in economic uncertainty. At the same time, lockdowns have elevated digital as an urgent priority across the entire value chain. As we come out of this crisis, these changes in customer behavior, spend, and payment trends require a massive transformation of online digital capabilities, contactless payments, and automated underwriting processes. The COVID-19 pandemic has resulted in mortgage lenders revisiting and, in many cases, adopting measures to digitize the mortgage process.
With all these additional challenges caused by the COVID-19 crisis—forbearances, delinquency risks, changes in customer and digital trends, increased fraud—how can we ensure that banks can handle the tsunami of expected losses? Here are some of the best practices that financial institutions can adopt during the crisis:
- Make strategic investments to enhance digital capabilities in order to provide better customer experience and engagement.
- Manage reserves and profit/loss proactively through timely identification of risk. FICO may not provide accurate risk posture, since banks are not required to report the delinquencies/losses for the accounts enrolled for forbearance. Early warning indicators, regional level macro-economic indicators (such as unemployment claims, Industry/Sector, which type of employment is impacted by COVID) can help to identify the trends for enrollments, impact on reserves, and P&L.
- Stress test underwriting and account management decisions frequently to gauge the resilience of loan portfolios—from origination throughout the life of the loans—and determine the impact on reserves. Reverse stress testing can help to identify the vulnerabilities in the portfolio and mitigate the risks in a timely manner.
- Improve the operational efficiency to execute ad-hoc/what-if scenarios with speed and agility and conduct timely impact assessment on P&L.
- Close inactive accounts where there is no activity in the last 6 to 12 months as per the bank’s credit policy to minimize fraud losses.
- Continuously monitor forbearance enrollment volume trends, Credit Line Increase (CLI), Credit Line Decrease (CLD): ramping up the CLD/stopping the CLI (one-size-fits-all) across the board may not be a strategic alternative. Reducing or canceling unused credit lines where it’s appropriate (once the account enrolls for a loan modification or troubled debt restructuring) can be considered.
- Identify and mitigate the concentration risk dynamically and reevaluate the customer creditworthiness to manage credit exposure.
- Leverage ensemble of AI/ML models to identify and rank-order the opportunities intelligently while keeping humans in the loop for speed and agility. Delayed actions can have adverse impacts on reserves and P&L.
- Continuously assess macroeconomic assumptions and monitor limits, particularly in a rapidly changing environment.
- Have a robust strategic plan, strong execution with agility to respond to the crisis and come out stronger post-crisis. Financial institutions must be prepared to exit less resilient businesses and segments if the potential risk exposure is beyond the risk tolerance levels.
Continuous monitoring of portfolio performance, prudent credit decisions, and understanding of the context are critical. With these best practices, banks can mitigate their losses during these uncertain times.
About the Author:
Raj Gangavarapu is Head of Data Science at diwo, your intelligent advisor to turn AI into action. He has two decades of leadership experience helping companies to solve complex business problems by leveraging data and analytics. He is a speaker at various academic and industry conferences on data science, risk, Artificial Intelligence (AI) and Machine Learning (ML).
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