Intelligent Credit Scoring. Siddiqi Naeem

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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”; etc.). This factor can work in two ways. On the positive side, it may drive people to improve their payment and other credit habits to get better scores. On the negative side, this may also lead to manipulation. The usage of robust bureau data will mitigate some of the risk, while the usage of unreliable social media or demographics data may not.

      The ever-present discussion on newer, better algorithms will continue. Our quest to explain data better, and differentiate useful information from noise, has been going on for decades and will likely go on for decades more. The current hot topic is machine learning. Whether it or the other more complex algorithms replaces the simpler algorithms in use in credit scoring will depend on many factors (this topic will also be dealt with in the later chapter on vendor model validation). Banks overwhelmingly select logistic regression, scorecards, and other such methods for credit scoring based on their openness, simplicity, and ease of compliance. Complex algorithms will become more popular for nonlending and nonregulatory modeling, but there will need to be a change in regulatory and model validation mind-sets before they become widely acceptable for the regulatory models.

      The credit crisis of 2008 has been widely discussed and dissected by many others. Let us firstly recognize that it was a complex event and its causes many. Access to cheap money, a housing bubble in many places, teaser rates to subprime borrowers, lack of transparency around models, distorted incentives for frontline staff, unrealistic ratings for mortgage-backed securities, greed, fraud, and the use of self-declared (i.e., unconfirmed) incomes have all been cited.11 Generally, I consider it a failure of both bankers in exercising the basic rules of banking, and risk management in failing to manage risks. Some have even suggested that models and scorecards are to blame. This is not quite accurate and reflects a failure to understand the nature of models. As we will cover in this book, models are built on many underlying assumptions, and their use involves just as many caveats. Models are not perfect, nor are they 100 percent accurate for all times. All models describe historical data – hence the critical need to adjust expectations based on future economic cycles. The amount of confidence in any model or scorecard must be based on both the quality and quantity of the underlying data, and decision-making strategies adjusted accordingly. Models are very useful when used judiciously, along with policy rules and judgment, recognizing both their strengths and weaknesses. The most accurate model in the world will not help if a bank chooses not to confirm any information from credit applicants or to verify identities. As such, one needs to be very realistic when it comes to using scorecards/models, and not have an unjustified level of trust in them.

      “… too many financial institutions and investors simply outsourced their risk management. Rather than undertake their own analysis, they relied on the rating agencies to do the essential work of risk analysis for them.”

– Lloyd Blankfein, CEO Goldman Sachs (Financial Times, February 8, 2009)

      Chapter 2

      Scorecard Development: The People and the Process

      “Talent wins games, but teamwork and intelligence wins championships.”

– Michael Jordan

      Many years ago, I developed a set of scorecards for a risk management department of a bank. The data sent to us by the risk folks was great, and we built a good scorecard with about 14 reasonable variables. About two weeks after delivering the scorecard, we got a call from the customer. Apparently, two of the variables that they had sent to us in the data set were not usable, and we needed to take them out. I have had bankers tell me stories of changing scorecard variables because information technology (IT) gave them estimates of three to four months to code up a new derived variable. IT folks, however, tell me they hate to be surprised by last-minute requests to implement new scorecards or new derived variables that cannot be handled by their systems. Almost every bank I’ve advised has had occasions where the variables desired/expected by the risk manager could not be in models, where models built could not be used because other stakeholders would not agree to them, or where other surprises lay waiting months after the actual work was done.

      These are some of the things that cause problems during scorecard development and implementation projects. In order to prevent such problems, the process of scorecard development needs to be a collaborative one between IT, risk management (strategy and policy), modeling, validation, and operational staff. This collaboration not only creates better scorecards, it also ensures that the solutions are consistent with business direction, prevent surprises, and enable education and knowledge transfer during the development process. Scorecard development is not a “black box” process and should not be treated as such. Experience has shown that developing scorecards in isolation can lead to problems such as inclusion of characteristics that are no longer collected, legally suspect, or difficult to collect operationally, exclusion of operationally critical variables, and devising of strategies that result in “surprises” or are unimplementable.

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