De las Emergencias Sanitarias

While data-driven models have become the norm for retail credit, in many cases it is physically not possible to develop them. That does not automatically imply a lack of knowledge though, and it may still be possible to get many of the benefits of decision automation—especially speed and consistency of decision-making—through the use of expert models. Indeed, consist-ency by itself may provide a significant improvement.

Expert models can take on different forms. Some are presented as scorecards, others as deci-sion trees, while still others use a combination of the two. They are developed by harnessing the knowledge of people that have the requisite experience. Although these ‘domain experts’

may not be able to define the exact quantum of relationships between the various factors, they usually have a firm grip on the dependencies.

Chorafas (1990) also commented that the power of domain experts comes from a process of inference, representation, decision, and control. In order for an expert system adequately to mimic individual expert(s), it has to consist of three types of knowledge—factual, judg-mental, and procedural—that may each be stored in separate databases, but must work together. The systems are usually based upon probabilities ‘to deal with situations that cannot be reduced to mathematical formulae’ and, as such, are heuristic in nature.

Domain experts are also capable of ranking individual cases, for use in a scorecard develop-ment. The subjective grades can then be used as the target variable in an ordered logistic regression. The resultant scorecard can be used to provide rankings, but default estimates are only possible with proper calibration. Alternatively, scorecard developers/vendors with expe-rience in similar environments may be able to develop a generic. In either case, the resulting model may be used: (i) as an interim measure, until such time as sufficient data and perform-ance are available to develop a data-driven model; or (ii) as a more permanent solution, which is refreshed occasionally using updated inputs from the experts.

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The process can only be automated if the model can be operationalised. The main challenge is to assemble data from different computer systems, or provide a platform for it to be obtained and captured manually. Inputs will be mostly objective data, but can also include subjective evaluations of various factors. The latter is frowned upon, but in some instances can provide a channel for an underwriter’s forward-looking view. It is, however, only advisable if they can provide insights that are not already embodied in the objective data.

Businesses’ expectations of expert models may be high, but the models cannot be held up to the same standards as statistical models. Validation is difficult in the absence of performance data, and it may only be possible to benchmark against external measures, such as bureau scores or rating agency grades. Ultimately, expert scores or grades are usually used for guid-ance only, albeit underwriters’ latitude to override the scores may be limited.

4.3 Conclusion

This textbook focuses on credit risk, but it cannot be treated in isolation from the many other uncertainties that lenders face. This chapter provided a general risk framework, which was split out in terms of: (i) the sources of risk; and (ii) the data and models used to assess risk.

Threats can arise both internally and externally, whether from the market, economic, social, or political environment. The greatest risk is whether the business proposition is appropriate for the market, but credit risk is a close second for many companies. The other primary risks are market and operational risks, with others arising from: business environment (industry, legal/regulatory, environmental, and reputational); business dealings (counterparty, liquidity, concentration, and accounting); extraterritorial (sovereign, country, transfer, and political);

and personal (character and personal distress). Businesses are also affected by their ability to gather and process information, which gives rise to the distinction between idiosyncratic and universal risks (case level), and endogenous and exogenous risks (process level). Systemic risks can also arise from small events that have unforeseen consequences. Finally, and importantly in the context of this book, significant risks can arise if the models used for decision-making are ill-defined.

Fortunately, credit risk can be measured more easily than most, if only because there are known outcomes, and lenders usually have sufficient data or experience to determine probabilities. The data can: (i) come from internal or external sources, including the customer;

(ii) be vertical (time series) or horizontal (multivariate snapshots for each observation);

(iii) include subjective and objective inputs; (iv) use leading and lagging indicators; and (v) provide either a forward- or backward-looking view. Credit scoring uses all sources, but tends to rely on horizontal data comprised of objective leading indicators, with a backward-looking view.

The type of model that is appropriate for each situation depends upon how well structured the data is, and how much of it there is. Statistical models require both, but there are a lot of other models that may be used along the way. Pure judgment can be used where both data and structure are lacking, but relies upon the experience of individuals. Policies are applied when

there is significant collective experience, and can provide greater structure in other instances (they are not always appropriate, and overrides provide a countermeasure). Expert models can be used if individuals’ experiences can be encapsulated in a model, whether a decision tree or scorecard. And finally, hybrids can be used to combine statistical models and judgment where the former are insufficient to provide a decision.

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