Intelligent Credit Scoring. Siddiqi Naeem

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payment terms for business customers.

      ● Asking the applicant to provide a deposit for water/electricity utilities services, or for landline phones.

      ● Offering prepaid cellular services instead of postpaid, or offering a lower monthly plan.

      ● Denying international calling access from telecommunications companies.

      ● Asking high-risk applicants for further documentation on employment, assets, or income.

      ● Selecting applicants for further scrutiny for potential fraudulent activity.

      Conversely, high-scoring applicants may be given preferential rates and higher credit limits, and be offered upgrades to better products, such as premium credit cards, or additional products offered by the company.

      Application scores can also help in setting “due diligence” policies. For example, an applicant scoring very low can be declined outright, but those in middling score ranges can be approved but with additional documentation requirements for information on real estate, income verification, or valuation of underlying security.

      The previous examples specifically dealt with credit risk scoring at the application stage. Risk scoring is similarly used with existing clients on an ongoing basis. In this context, the client’s behavioral data with the company, as well as bureau data, is used to predict the probability of ongoing negative behavior. Based on similar business considerations as previously mentioned (e.g., expected risk and profitability levels), different treatments can be tailored to existing accounts, such as:

      ● Offering product upgrades and additional products to better customers.

      ● Increasing or decreasing credit limits on credit cards and lines of credit.

      ● Allowing some revolving credit customers to go beyond their credit limits for purchases.

      ● Allowing better customers to use credit cards even in delinquency, while blocking the high-risk ones immediately.

      ● Flagging potentially fraudulent transactions.

      ● Offering better pricing on loan/insurance policy renewals.

      ● Setting premiums for mortgage insurance.

      ● Deciding whether or not to reissue an expired credit card.

      ● Prequalifying direct marketing lists for cross-selling.

      ● Directing delinquent accounts to more stringent collection methods or outsourcing to a collection agency.

      ● Suspending or revoking phone services or credit facilities.

      ● Putting an account on a “watch list” for potential fraudulent activity.

      In addition to being developed for use with new applicants (application scoring) or existing accounts (behavior scoring), scorecards can also be defined based on the type of data used to develop them. “Custom” scorecards are those developed using data for customers of one organization exclusively, for example, if a bank uses the performance data of its own customers to build a scorecard to predict bankruptcy. It may use internal data or data obtained from a credit bureau for this purpose, but the data is only for its own customers.

      “Generic” or “pooled data” scorecards are those built using data from multiple lenders. For example, four small banks, none of which has enough data to build its own custom scorecards, decide to pool their data for auto loans. They then build a scorecard with this data and share it, or customize the scorecards based on unique characteristics of their portfolios. Scorecards built using industry bureau data, and marketed by credit bureaus, are a type of generic scorecards. The use of such generic models (and other external vendor built models) creates some unique challenges as some of the know-how and processes can remain as black boxes. We will discuss how to validate and use such models in a guest chapter authored by experienced industry figures Clark Abrahams, Bradley Bender, and Charles Maner.

      Risk scoring, in addition to being a tool to evaluate levels of risk, has also been effectively applied in other operational areas, such as:

      ● Streamlining the decision-making process, that is, higher-risk and borderline applications being given to more experienced staff for more scrutiny, while low-risk applications are assigned to junior staff. This can be done in branches, credit adjudication centers, and collections departments.

      ● Reducing turnaround time for processing applications through automated decision making, thereby reducing per-unit processing cost and increasing customer satisfaction.

      ● Evaluating quality of portfolios intended for acquisition through bureau-based generic scores.

      ● Setting economic and regulatory capital allocation.

      ● Forecasting.

      ● Setting pricing for securitization of receivables portfolios.

      ● Comparing the quality of business from different channels/regions/ suppliers.

      ● Help in complying with lending regulations that call for empirically proven methods for lending, without potentially discriminatory judgment.

      Credit risk scoring, therefore, provides lenders with an opportunity for consistent and objective decision making, based on empirically derived information. Combined with business knowledge, predictive modeling technologies provide risk managers with added efficiency and control over the risk management process.

      Credit scoring is now also being used increasingly in the insurance sector for determining auto6 and home insurance7 premiums. A unique study conducted by the Federal Reserve Board even suggests that couples with higher credit scores tend to stay together longer.8

      The future of credit scoring, and those who practice it, is bright. There are several issues, discussed later, that will determine the shape of the industry in the coming 5- to 10-year span.

      The rise of alternate data sources, including social media data, will affect the industry. In reality, the change has already begun, with many lenders now starting to use such data instead of the more traditional scores.9 This issue will be discussed in more detail in several chapters. In many countries, the creation of credit bureaus is having a positive impact on the credit industry. Having a centralized repository of credit information reduces losses as lenders can now be aware of bad credit behavior elsewhere. Conversely, it makes it easier for good customers to access credit as they now have strong, reliable evidence of their satisfactory payment behavior. In addition, the access to very large data sets and increasingly powerful machines has also enabled banks to use more data, and process analytics faster. We will cover this topic in more detail in its own chapter authored by Dr. Billie Anderson.

      Regulatory challenges will continue, but banks are better prepared. Basel II has overall improved the level of analytics and credit scoring in banks. It has introduced and formalized repeatable, transparent, and auditable processes in banks for developing models. It has helped create truly independent arm’s-length risk functions, and model validation team that can mount effective challenges. Basel II, as well as Basel Committee on Banking Supervision (BCBS) regulation 239,10 has also made data creation, storage, and aggregation at banks far better than before. IFRS 9 and other current regulatory initiatives such as Comprehensive Capital Analysis and Review (CCAR), Current Expected Credit Loss (CECL), and stress testing, as well as their global equivalents, will continue to expand and challenge analytics and credit scoring.

      One factor that users of credit scoring will need to be cautious about is the increasing knowledge of credit scoring in the general population. In particular, in the United States, knowledge of bureau scores such as the FICO score, is getting very common. This is evidenced by the number of articles, discussions, and questions on how to improve the score (I personally get such questions via e-mail and on social media at least every week or two weeks – questions such as “How do I maximize my score in the shortest time?”; “If I cancel my card, will it decrease my score”;

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