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The secret to better credit-risk management: economically calibrated models

May/June 2011

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Andrew Jennings  Wang

By Andrew Jennings and Carolyn Wang

As the banking sector gradually rebounds from the global recession, many bank executives and boards are focused on incorporating the painful lessons learned during the past three years into their business operations. Chief among those lessons learned is the need to strengthen the management of credit risk as economic conditions fluctuate.

It is clear that many banks weren’t prepared for the economic and financial storms that struck in 2008. Not only were the analytic models employed by banks ill-prepared for the depth and length of the recession, bank executives were caught off guard by their inability to do more to manage through the onslaught of consumer defaults.

The good news is that these hard times spurred analytic innovation and produced useful data to strengthen risk management going forward. Our post-crisis research has revealed three important lessons regarding credit risk that can be instructive for banks everywhere:

  1. Risk is dynamic. A bank’s risk-management strategy must be agile enough to keep pace with a risk environment that is evolving continuously.
  2. Rapid and significant changes in economic and market forces can render traditional risk-management approaches less reliable.
  3. Credit providers need better economic forecasting relative to risk management for loan origination and portfolio management.

Economically Calibrated Risk Models

Risk models that are used to originate loans or make credit decisions on existing customers need to take an economically sensitive approach that offers the guidance and insight banks require for effective risk management. Such an approach will enable models to provide decision makers with more reliable and actionable information. While most of today’s credit-risk models continue to rank-order risk properly during turbulent times, we now know that immediate past default experience can be a weak indicator of future payment performance when economic conditions change significantly and unexpectedly.

Empirical evidence shows that default rates can shift substantially even when credit scores stay the same (see Figure 1). For example, in 2005 and 2006, a 2 percent default rate was associated with a FICO Score of 650-660. By 2007, a 2 percent default rate was associated with a score of about 710 as rapidly worsening economic conditions (and the impact of prior weak underwriting standards) affected loan performance.

figure 1

Although most banks already incorporate some type of economic forecasting into their policies, our research and experience indicates a substantial portion of this input is static and may not provide useful guidance for risk managers. As a result, there is a tendency to over-correct and miss key revenue opportunities, or under-correct and retain more portfolio risk than desired. Fortunately, progress in predictive analytics over the past three years now allows forecasting based on a more empirical foundation that is far more adept at managing risk in a dynamic environment.

Such forecasts can augment existing credit-risk predictions in two ways:

  1. They can improve predictions for payment performance. These improved predictions can be incorporated into individual lending decisions, and they can be used at the aggregate level to predict portfolio performance.
  2. They can be used to predict the migration of assets between tranches of risk grades. When used in conjunction with aggregate portfolio default probabilities, this can form the basis of forecasting risk-weighted assets for the purpose of Basel capital calculations (and other types of regulatory compliance).

Risk Shifts as Economy Shifts

During economic downturns, many lower-risk consumers may refinance their debt obligations, leaving their previous lenders with portfolios full of riskier consumers. Other borrowers who were lower-risk in the past may reach their breaking points through job loss or increased payment requirements. And higher-risk consumers may get stretched further, resulting in more frequent and severe delinquencies and defaults.

Economically calibrated analytics give lenders a way to understand the complex dynamics at work during unstable economic times. The resulting models provide an additional dimension to risk prediction that enables lenders to:

  1. Grow portfolios in a less risky and more sustainable manner by identifying more profitable customers and extending more appropriate offers.
  2. Limit losses by tightening credit policies sooner and targeting appropriate customer segments more precisely for early-stage collections.
  3. Prepare for the future with improvements in long-term strategy and stress testing.
  4. Achieve compliance with capital regulations more efficiently. (Improved accuracy in reserving will also reduce the cost of capital.)

At the simplest level, next-generation analytics provides lenders with an understanding of how the future risk level associated with a given credit score will change under current and projected economic conditions. These sophisticated analytic models are able to derive the relationship between historical changes in economic conditions and the default rates at different score ranges (i.e., the odds-to-score relationship) in a lender’s portfolio.

Using this derived relationship, lenders can input current and anticipated economic conditions into their models to project the expected odds-to-score outcome under those conditions. They can model their portfolio performance under a variety of scenarios utilizing economic indicators such as the unemployment rate, key interest rates, Gross Domestic Product (GDP), housing price changes and many others variables. These models can be constructed regionally or locally to account for the fact that economic conditions may not be homogenous across an entire country.

The odds-to-score relationship can be studied at an overall portfolio level or it can be scrutinized more finely for key customer segments that may behave differently under varying economic conditions. And, it can be applied to a variety of score types, such as origination scores, behavior scores, broad-based bureau scores like the FICO Score and Basel II risk metrics.

Economically calibrated analytics can be particularly valuable when examining the behavior scores that lenders utilize to manage accounts already on their books for actions such as credit line increases/decreases, authorizations, loan re-pricing and cross selling. An economically calibrated behavior score could be used in place of, or along with, the traditional behavior score across the full range of account-management actions.

Significant Value Add for Compliance

In addition to operational risk management, the incorporation of economic factors into portfolio performance modeling can be valuable for regulatory compliance. When lenders set aside provisions and capital reserves, it is important that they understand the risks in their portfolios under stressed economic conditions because that is when the reserves are likely to be needed most. In fact, forward-looking risk prediction is explicitly mandated in Basel II regulations, and such predictive analytics should be part of any lender’s best practices for risk management.

FICO has been working for some time now with European lenders to add economic projections into Basel II Probability of Default (PD) models. Using the derived odds-to-score relationship between a lender’s PD score and various economic conditions, lenders can simulate the expected PD at a given risk-grade level in many different scenarios. Thus, lenders can more accurately calculate forward-looking, long-term PD estimates to meet regulatory requirements and calculate capital reserves in a more efficient and reliable manner.

This can help banks free up more capital for lending and credit without taking on unreasonable risk. It can also help improve the transparency of a bank’s compliance program and reduce the time and resources that must be dedicated to compliance.

Approach Already Bearing Fruit

We recently applied our economically calibrated risk-management methodology to the portfolio of a top-10 U.S. credit card issuer. We compared the actual bad rate in the portfolio to predictions from both the traditional historical odds approach as well as the economically calibrated methodology. We found that the latter would have reduced the issuer’s error rate (the difference between the actual and predicted bad rates) by 73 percent over three years, resulting in millions of dollars of loss avoidance.

figure 2

In a second example, European lender Raiffeisen Bank International (RBI) is using an economically calibrated risk-management technique to complement its more traditional credit scoring information. RBI overlays macroeconomic information on the bank’s traditional credit-scoring process, creating a system that leverages and extends the value of RBI’s in-house economic research.

This provides the bank with a forward-looking element to its credit scoring following concerns about the creditworthiness of some of the central and eastern European countries in which the bank operates. RBI is using this new approach on its credit card, personal loan and mortgage portfolios to build future economic expectations into credit risk analysis.

Regulatory compliance was the initial driver of this move, but RBI quickly realized the new approach could help it achieve stability in the overall capital requirements for its retail business segment. Each market the bank serves faces different economic prospects, and calibrating risk strategies for each market can help the bank grow during good and bad economic periods.

In another real-world case, a U.S. credit card issuer retroactively applied this economic-impact methodology to its credit-line-decrease and collections strategies. An analysis of its 2008 data (conducted with the new methodology) found that the predicted bad rate for its portfolio rose more than 250 basis points compared to predictions based on a more traditional approach. The new approach would have decreased the amount of credit extended to a larger portion of the portfolio (and not decreased credit to those less sensitive to the downturn). The lender would have realized millions of dollars in yearly loss savings.

For the same U.S. card issuer, we retroactively used an economically adjusted behavior score in place of the traditional behavior score to treat early-stage (cycle 1) delinquent accounts. Prioritizing accounts by risk, the strategy would have targeted 41 percent of the population for more aggressive treatment in April 2008. We then examined the resulting bad rates six months later (October 2008) and saw that these accounts resulted in higher default rates than the accounts that weren’t targeted. In other words, the economically adjusted scores improved the identification of accounts that should have received more aggressive treatment in anticipation of the economic downturn. Using this strategy, the lender would have been ahead of its competition in collecting on the same limited dollars.

The lender could have saved approximately $4 million by taking aggressive action earlier. FICO calculated this figure using the number of actual bad accounts that would have received accelerated treatment, the average account balance, and industry roll rates. The combination of this loss prevention through more aggressive collection and the millions of dollars the lender could have saved from an improved credit-line decrease strategy would have made a material impact on the lender’s earnings. This underscores the aggregate benefits of economically calibrated risk management when used across a customer lifecycle. And, the benefits are scalable for larger portfolios.

These are just a few examples among many worldwide that illustrate the value of economically calibrated analytics for risk management. In fact, one of the largest financial institutions in South Korea recently adopted this same approach to help it derive forward-looking estimates on the probability of default in its consumer finance portfolio. The lender will be using these predictions to continuously adjust its operational decisions depending on anticipated economic conditions.

Now is the Time to Act

Smart lenders are reevaluating their risk-management practices now – when economic conditions are somewhat calm and there is no immediate crisis that requires their full attention. A reevaluation of risk-management practices can enable measured growth while simultaneously preparing a lender for the next recession.

The use of forward-looking analytic tools will become the risk-management best practice of tomorrow. With improved risk predictions that are better aligned to current and future economic conditions, lenders can more quickly adjust to dynamic market conditions and steer their portfolios through uncertain times.

Andrew Jennings is chief analytics officer at FICO and the head of FICO Labs. Carolyn Wang is a senior manager of analytics at FICO. To read more commentary from Dr. Jennings and other FICO banking experts, visit http://bankinganalyticsblog.fico.com/.

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