While a more risk-aware financial services industry is today’s worldwide reality, Basel II compliance standards provide institutions with the tools to translate this awareness into action. Having met initial standards using the data and analytic methods most readily available, forward-thinking institutions recognize that opportunity for greater gains lies ahead.

Institutions that have mined what they can from first-generation Loss Given Default (LGD) analytic methods, involving expert segmentation or regression trees, can benefit from Fair Isaac solutions with more complex analytics. More advanced LGD modeling techniques move institutions forward into the arena of best-in-class risk management.

Lenders that are able to take a more effective approach to assessing risk—through better credit risk exposure estimates and improved integration of this knowledge into decision-making processes—can reap the rewards of reduced capital reserves, improved investment strategies and strengthened competitiveness. By contrast, lenders that are not adequately prepared to manage risk can expect to face higher capital reserve requirements, increased regulatory scrutiny and weakened investor confidence.

“Institutions can leverage our next-generation LGD models for even greater business benefits that extend beyond basic Basel compliance,” says David Molyneaux, a Fair Isaac principal consultant. “Our models can guide recovery strategy and individual account management decisions, as well as reflect the LGD volatility from collateral value, such as when housing prices shift downward.”

LGD is one of the three main credit risk parameters involved in determining both Expected Loss (EL) and Unexpected Loss (UL), and arguably the one with the greatest potential impact on an institution’s capital reserves calculation. More complex to calculate than probability of default (PD), LGD modeling encompasses multiple component models and is heavily dependent on the actions taken by an institution.

The LGD model framework

The framework for LGD modeling is influenced by an account’s default status, probability of write-off and estimated percent recovery. For unsecured products such as credit cards, Fair Isaac recommends using a two-stage modeling approach when possible, modeling the likelihood of write-off, and the likely recovery amount given write-off both at origination and throughout the account’s lifecycle.

The LGD model framework for unsecured products includes multiple component models to produce highly accurate Basel-compliant risk assessment.

Stage one—probability of write-off (PWO) modeling—begins with considerations including defining “good” and “bad,” how long an outcome time window to use, and whether to pursue a top-down (decision tree-based) or bottom-up (scorecard-based) approach to modeling write-off. Factors that influence these considerations vary by data availability, product and business practices.

Sometimes expected patterns do not reflect reality. For example, Fair Isaac analysts observe that severely delinquent population segments—perhaps those that are experienced at keeping debt levels within charge-off limits—may incur lower charge-offs than less-delinquent segments.

Stage two—recovery results—involves calculating the percent recovery expected within the targeted outcome time frame. Analysts take into consideration factors that influence the economic loss of an account. For example, a more aggressive collection policy may shorten the outcome window and affect the economic loss seen on that account.

“In estimating LGD, loss is defined as economic loss,” explains Molyneaux. “When measuring economic loss, all relevant factors must be taken into account. This must include material discount effects, and material direct and indirect costs associated with collecting on the exposure.” The discount effect, cost of recoveries and sale of debt all come into play in this scenario.

LGD modeling for secured products involves considering an even more complex set of factors, mainly stemming from the unknown market value of the asset and potential actions from the bank. Following a default, multiple scenarios may occur, ranging from an account returning to good standing, to repossession, to an unrecoverable asset. The probability of each scenario should be calculated, and a distinct LGD calculation completed for each.

Understanding the LGD for secured portfolios can bring great potential in ultimately refining mortgage rate and fee decisions that directly impact profitability. Solid risk management practices in this arena can help lenders weather the storm during volatile economic periods that cause significant changes in housing prices—as seen in today’s market.

Models help lenders stay ahead of the curve

Fair Isaac analytic research in LGD modeling has rendered significant insight on how various factors—lender recovery methods and policies, debtor behavior, and loan products—influence calculations, and are significantly different than those involved in calculating PD. Market-leading institutions that set their sights on adopting more complex LGD modeling will continue to operate ahead of the Basel compliance curve and benefit from best-in-class risk management practices.

Fair Isaac has completed Basel II compliance projects for clients in Asia, Europe, the Middle East and North America. Our analytic approach is particularly appropriate for institutions that have enough account and performance data to support the model development. This approach presents a groundbreaking way to enhance risk management practices and turn Basel II compliance into opportunity.