Infrastructure Design for Credit Risk Modeling

Common Problems Leading Institutions Face when Building Scorecards

One version of the truth. Two people asking the same question, or repeating the same exercise should get the same answer. One way to achieve this is by sharing and reusing data sources, data extraction logic, conditions such as filters and segmentation logic, models, parameters and variables, including logic for derived ones.

Transparency and audit. Anyone who needs to see details on each phase of the development process should be able to do so easily. For example, how data is transformed to create aggregated and derived variables, the parameters chosen for model fitting, how variables entered the model, validation details, and other parameters should preferably be stored in graphical user interface (GUI) format for review.

Retention of corporate intellectual property (IP)/knowledge. Practices such as writing unique code for each project and keeping it on individual PCs makes it harder to retain IP when key staff leave. Using programming-based modeling tools makes it more difficult to retain this IP as staff leaving take their coding skills with them. To counter this, many financial institutions have shifted to GUI software to reduce this loss and to introduce standardization.

Integration across the model development tasks. Integration across the continuum of activities from data set creation to validation, means that the output of each phase seamlessly gets used in the next.

Faster time to results. It sometimes takes months to build a model and implement it in many institutions, resulting in the use of inefficient or unstable models for longer than necessary.