Big Data Analytics for Lenders and Creditors

Credit scoring means applying a statistical model to assign a risk score to a credit application or to an existing credit account. Here we are suggesting how data science and big data can help making the better sense of different risk factors and accurate predictions.

By Goran Dragosavac (SAS), @goran_drago.

Credit today is granted by various organizations such as banks, building societies, retailers, mail order companies, utilities and various others. Because of growing demand, stronger competition and advances in computer technology, over the last 30 years traditional methods of making credit decisions that rely mostly on human judgment have been replaced by methods that rely mostly on statistical models. Such statistical models today are not only used for deciding whether or not to accept an applicant (application scoring), but also to predict the likely default of customers that have already been accepted (behavioral scoring) and to predict the likely amount of debt that the lender can expect to recover (collection scoring). The term credit scoring can be defined on several conceptual levels. Most fundamentally, credit scoring means applying a statistical model to assign a risk score to a credit application or to an existing credit account. On a higher level, credit scoring also means the process of developing such a statistical model from historical data. On yet a higher level, the term also refers to monitoring the accuracy of one or many such statistical models and monitoring the effect that score based decisions have on key business performance indicators.


Credit scoring is performed because it provides a number of important business benefits, all of them based on the ability to quickly and efficiently obtain fact-based and accurate predictions of the credit risk of individual applicants or customers. So, for example, in application scoring, credit scores are used for optimizing the approval rate for credit applications. Application scores enable the organization to choose an optimal cut-off score for acceptance, such that market share can be gained while retaining maximum profitability. The approval process and the marketing of credit products can be streamlined based on credit scores: High-risk applications can, for example, be given to more experienced staff or pre-approved credit products can be offered to selected low-risk customers via various channels, including direct marketing and the Web.

Credit scores, both of prospects and existing customers, are essential in the customization of credit products. They are used for determining custom credit limits, down payments or deposits and interest rates. Behavioral credit scores of existing customers are used in the early detection of high-risk accounts and enable the organization to perform targeted interventions, for example by pro-actively offering debt restructuring. Behavioral credit scores also form the basis for more accurate calculations of the total consumer credit risk exposure, which can result in a reduction of bad debt provision.

Other benefits of credit scoring include an improved targeting of audits at high-risk accounts, thereby optimizing the workload of the auditing staff. Resources spent on debt collection can be optimized by targeting collection activities at accounts with a high collection score. Collection scores are also used for determining the accurate value of a debt book before it is sold to a collection agency.  Finally, credit scores serve to assess the quality of portfolios intended for acquisition and to compare the quality of business from different channels, regions, and suppliers.

Building credit models in-house

While under certain circumstances it is appropriate to buy ‘ready-made’ generic credit models from outside vendors or to have credit models developed by outside consultants for a specific purpose, maintaining a practice for building credit models in-house offers several advantages. Most directly, it enables the lending organization to profit from economies of scale when many models need to be built and to afford a greater number of segment specific models for a greater variety of purposes.

Building up a solid, re-usable and flexible data, knowledge and skill base of its own also makes it easier for the organization to stay consistent in the interpretation of model results and reports and to use a consistent modeling methodology across the whole range of customer related scores. This results in a reduced turnaround time for the integration of new models, thereby freeing resources to more swiftly respond to new business questions with new creative models and strategies.

Finally, in-house modeling competency is needed to verify the accuracy and analyze the strengths and weaknesses of acquired credit models, to reduce access of outsiders to strategic information and to retain competitive advantage by building up company-specific best practices.