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Optimizing Lending Operations in a Time of Extreme Uncertainty

This is the first to a series of blog posts highlighting optimization, artificial intelligence, predictive analytics, and decisioning for lending operations in times of extreme uncertainty.

Like all businesses, lenders are facing tremendous change and uncertainty in the face of the COVID-19 crisis.  While focusing first on how to keep their employees and customers safe during the new normal, they are asking how to make data-driven decisions in this new environment.  It’s only natural that business people are skeptical about whether analytics will work in a situation like today’s – in which the data deviate from all historical precedents.  Certainly, nobody predicted, for example, that the number of loans with forbearance requests would increase by over 1000% during each two-week period in March. Can anyone possibly make an optimized decision when things are changing so quickly and when so many things are unknown?

Prescriptive analytics – also known as mathematical optimization – is the practice of developing a business strategy to achieve a business objective subject to capacity and other constraints, often using a demand forecast. For example, banks use optimization software to develop marketing and debt management strategies to run their lending operations.  But what happens when the demand forecast might be wrong, when the constraints change quickly, and when decision-makers cannot agree on a single objective? The reality is that decisionmakers have to balance multiple competing objectives related to many different stakeholders. And, especially during the COVID-19 crisis and the period of change that will certainly follow, they have to do so in the face of uncertainty.

Let’s discuss some of the methods that analysts use to control risk while optimizing lending practices during times like these. These techniques, collectively known as robust optimization and robust statistics, help lenders and other business people deal with the uncomfortable reality that we do not know what the future holds.  

Consider a hypothetical bank or other lender servicing a portfolio of consumer loans and forecasting its loss performance in this environment. Management probably has several competing objectives: they want to improve service levels on their digital channel, they want to minimize credit and fraud losses, they’re facing a reduced operating budget, and they’re not certain how many employees they will have and which vendors will be able to provide adequate service levels. Furthermore, they anticipate new and unpredicted changes, and they need to be able to update their strategies quickly.

The mathematics can be quite technical, but Experian’s Marketswitch Optimization is user-friendly software to help businesspeople–not engineers–design and deploy optimal strategies for practices such as Account Management and Loan Originations while facing such a dynamic and uncertain environment. The bank’s business analysts (not computer specialists or mathematicians) will use techniques such as these:

  • With Sensitivity Analysis, the analysts will explore the performance of their optimized Account Management, Collections, and Loan Originations strategies while considering possible changes in input variables.
  • Optimization Scenarios with Uncertainty (technically known as Stochastic Optimization) allow the managers and analysts to design operational strategies that control risk, particularly the bank’s exposure to probabilistic and worst-case scenarios.
  • Using Scenario Performance Analysis, the lender’s team will validate and test their optimization scenarios against a variety of different data sets to understand how their strategies would perform in each case.
  • Model Quality Evaluation techniques help the credit risk managers compare model predictions against actual performance during a quickly changing economy.
  • Model impact analysis (related to Model Risk Management) helps senior leadership assess when it is time to invest in improving its statistical models.
  • Robust Model Calibration Analysis removes unjustifiable variations in the lender’s predictive models to make their predictions more valid as things change over time.

These six advanced analytics techniques are especially helpful when developing business strategies for a time in which some values are unknown—including future unemployment levels, staffing budgets, data reporting practices, interest rates, and customer demands.  Business decisions can—and arguably must—be optimized during times of uncertainty. But during times like these, it is especially important that the analysts understand how and why to account for the uncertainty in both the data and the models.

Lenders, are you optimizing your servicing and debt management strategies? It has never been more important than now to do so–using the advanced techniques available to manage uncertainty mathematically.

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