In more recent times, the introduction of International Financial Reporting Standards (IFRS) 9 to calculate expected losses has expanded the usage of predictive models within all companies. Those institutions that have already invested in fixing their data problems and establishing sustainable and robust analytics functions will find it easier to comply.
In mature markets, banks that had been developing models and scorecards before have now been looking at how to make the process efficient, sustainable and more transparent. Investments in data warehousing, tools to enable analysts to access the data quickly and easily, integrated infrastructure to reduce model risk, governance processes, and other such areas have increased. Many banks that had invested a lot of money into data warehousing were also looking to increase return on investment (ROI). Credit scoring offered a quick and proven way to use the data, not just for reducing losses but also lead to greater profitability.4
Scarcity of modeling/credit scorecard (these two words are used interchangeably throughout this book) development resources has led institutions to try to reduce human resources risk by using modeling tools that encourage sharing and retention of corporate knowledge, reduce training cycles and costs, and are easier to use. Some of the challenges and risks of developing scorecards in-house will be discussed in the chapter on managing the risks of in-house scoring.
In other banks not specifically impacted by the preceding, increasing competition and growing pressures for revenue generation have led credit-granting institutions to search for more effective ways to attract new creditworthy customers and, at the same time, control losses. Aggressive marketing efforts have resulted in a continuously deeper penetration of the risk pool of potential customers, and the need to process them rapidly and effectively has led to growing automation of the credit and insurance application and adjudication processes. The risk manager is challenged to produce risk adjudication solutions that can not only satisfactorily assess creditworthiness but also keep the per-unit processing cost low, while reducing turnaround times for customers. In some jurisdictions without a credit bureau, the risk manager faces an additional challenge of doing so using data that may not be robust or reliable. In addition, customer service excellence demands that this automated process be able to minimize denial of credit to creditworthy customers, while keeping out as many potentially delinquent ones as possible.
At the customer management level, companies are striving ever harder to keep their existing clients by offering them additional products and enhanced services. Risk managers are called on to help in selecting the “right” (i.e., low-risk) customers for these favored treatments. Conversely, for customers who exhibit negative behavior (nonpayment, fraud), risk managers need to devise strategies to not only identify them but also to deal with them effectively to minimize further loss and recoup any monies owed as quickly as possible.
It is in this environment that credit risk scorecards have continued to offer a powerful, empirically derived solution to business needs. Credit risk scorecards have been widely used by a variety of industries for predicting various types of payment delinquencies, fraud, claims (for insurance), and recovery of amounts owed for accounts in collections, among other things. More recently, as mentioned previously, credit scoring has been used widely for regulatory compliance. Credit scoring offers an objective way to assess risk, and also a consistent approach, provided that system overrides are maintained below acceptable policy-specified thresholds.
In the past, most financial institutions acquired credit risk scorecards from a handful of credit risk vendors. This involved the financial institution providing their data to the vendors, and the vendors then developing a predictive scorecard for delivery. For smaller companies, buying a generic or pooled data scorecard was the only option. While some advanced companies have had internal modeling and scorecard development functions for a long time, the trend toward developing scorecards in-house has become far more widespread in the past few years. Some of the regulatory and operational reasons for this phenomenon were covered at the beginning of this chapter. Others will be discussed later.
First, there are more powerful and easy-to-use data mining software today than ever before. This has allowed users to develop scorecards without investing heavily in advanced programmers and infrastructure. Growing competition and the entry of several new data mining vendors made such tools available at ever cheaper prices. Complex data mining functions became available at the click of a mouse, allowing the user to spend more time applying business and data mining expertise to the problem, rather than debugging complicated and lengthy programs. The availability of powerful “point-and-click”–based Extract-Transform-Load (ETL) software enabled efficient extraction and preparation of data for scorecard development and other data mining. Second, advances in intelligent and easy-to-access data storage have removed much of the burden of gathering the required data and putting it into a form that is amenable to analysis. As mentioned earlier, banks and other lenders have made significant investments in data warehousing and data management, and are now looking to use that data to increase profitability.
Once these tools became available, in-house development became a viable option for many smaller and medium-sized institutions. The industry could now realize the significant ROI that in-house scorecard development could deliver for the right players. Experience has shown that in-house credit scorecard development can be done faster, cheaper, and with far more flexibility than any outsourcing strategy. Development was cheaper since the cost of maintaining an in-house credit scoring capability was less than the cost of purchased scorecards. Internal development capability also allowed companies to develop far more scorecards (with enhanced segmentation) for the same expenditure. Scorecards could also be developed more rapidly by internal resources using the right software – which meant that better custom scorecards could be implemented more rapidly, leading to lower losses.
In addition, companies have increasingly realized that their superior knowledge of internal data and business insights led them to develop better-performing scorecards. Seasoned modelers understand that the single biggest contributor to model quality is the data itself, followed by the knowledge level of the analyst of that data. This book will cover in detail how internal knowledge can be applied to build better scorecards. In every phase of the project, we will discuss how appropriate judgment can be applied to augment statistical analyses.
Better-performing scorecards also came about from having the flexibility to experiment with segmentation and then following through by developing more finely segmented scorecards. Deeper segmentation allows for more fine-tuned predictions and strategies. Combined with software that can implement champion/challenger scorecards, this becomes a great way to experiment with different configurations of models. Performing such detailed segmentation analysis through external vendors can become expensive.
Banks have also realized that credit risk scorecards are not a commodity to be purchased from the lowest bidder – they are a core competence and knowledge product of the institution. Internal scorecard development increases the knowledge base within organizations. The analyses done reveal hidden treasures of information that allow for better understanding of customers’ risk behavior and lead to better strategy development. We will cover some of this knowledge discovery in the section on model development, specifically the grouping process.
In summary, leaving key modeling and strategy decisions to “external experts” can prove to be a suboptimal route at best, and can also be quite costly.
This book presents a business-focused process for the development and usage of credit risk prediction scorecards, one that builds on a solid foundation of statistics and data mining principles. Statistical and data mining techniques and methodologies have been discussed in detail in various publications and will not be covered in depth here. I have assumed that the reader is either familiar with these algorithms, or can read up on them beforehand, and is now looking for business knowledge pertaining to scorecard development.
The key concepts that will be covered in the book are:
● The application of business intelligence to the scorecard development process, so that the development and implementation of scorecards is seen as an intelligent business solution to a business problem. Good scorecards are not built by passing data solely through a series of programs or