Author | Scienaptic Research
While demand for credit will probably grow, new challenges have emerged for lenders. As job losses and salary cuts grow, delinquencies have been mounting. Therefore, wary traditional lenders are tightening lending norms while restricting or suspending loans to certain categories of borrowers as a knee jerk reaction to the situation. This demands a rethink.
We can hardly determine when the economy will stabilize and show signs of recovery. The Deutsche Bank on Monday, 21st Sept 2020, said that "the world GDP will return to the level it was before COVID-19 by mid-2021 after a stronger-than-expected economic bounce in recent months but bloated debt levels and a shift in policy could heighten the risk of a financial crisis".
Credit Risk leaders are now firefighting on two fronts.
1. What needs to be done about originations?
2. What to do about existing accounts?
Bureau scores have been lagging and become extremely less reliable when used in isolation. Such is the case since COVID has had disproportionate impact on different sectors of the economy.
The role of a CRO in this pandemic
With BASEL accord constraints and the inevitable CECL introduction, the significance of forecasting/predicting your credit situation assumes at most importance. Models being through the cycle, it is incredibly had for CROs to account for macro changes and black swan events that puts enormous stresses on the portfolio.
We can't predict when and how the pandemic will be over, and it is now very unlikely to follow any particular pattern that we’ve observed over the years. Incidentally, the CRO's will need to focus on all the possible scenarios of safeguarding their portfolios. It's instinctive for some risk management teams to go back to the drawing board and re-calibrate the models. But deploying new risk management strategies at the modeling stage is no simple task. It requires significant time and effort. Some businesses spend up to 6 months implementing a change as the process comprises arriving at a business decision, defining business objectives, and implementing a change. That could be inappropriate considering the current economic environment. It might reduce the organization's nimbleness, decreasing their ability to perform insight-driven actions with agility.
A tactical and fast solution to this problem is using flexible rule engines in your predictive models.
Why Flexible Rule Engines?
Nowadays, all CRO's prefer to work with a credit decisioning software enabled with BRMS (Business Rule Management Systems). The business rules engine allows the development and rapid deployment of complex business rules so that organizations can make better decisions across the customer life cycle. But over time, we have seen how CRO always relies on models and their ability to solve a problem or achieve a business objective but often overlook the importance of using flexible rule engines. As a part of your models, flexible rule engines fully equip you to make fast, situation-driven, and dynamic decisions. Over-reliance on models can extend timelines, causing a delay in reacting to macro changes.
In current uncertain times, the creditworthiness of a borrower is dependent on various factors. The credit scores or current dues or the unemployment status, chances of re-hiring basing the skill sets, new childbirth, or the industry he/she is a part of or a county that a borrower belongs to. Macro events or situations such as these will be better deployed through the rule engines than the models. All of us are now forced to use the indicators that weren't available pre-COVID or not as obvious as now.
To summarize
The use of flexible rule engines can be considered as a tactical fix given the market's dynamism, whereas making changes in the models is more of a strategic fix. As mentioned before, a strategic fix requires significant time and effort, which the current situation does not allow. The near-term opportunity of using rule engines will help quicken the decision-making process, assisting financial institutions to make informed decisions and create growth opportunities.
Scienaptic’s business rule engine brings in the point-in-time view for your through the cycle models to account for macro changes and black swan events. You will be able to test-learn-deploy your models quickly so that there’s no compromise on your originations and growth goals. Line management too becomes easier.