Software Patterns to Identify Credit Risk Patterns

The adoption of Machine Learning (ML) in software applications has increased in domains like healthcare, banking and others. leading to coining of the term MLWare applications. However, challenges like diverse code base, complex components, lack of expertise, etc. persist in development and maintenance of these applications. Application of software engineering patterns and practices for the development of MLWare applications can improve maintainability, extensibility, scalability and other software quality parameters. In this paper, we propose an approach for developing MLWare applications using a pattern oriented approach. We demonstrate the approach on a credit risk scorecard application that can helps loan officer identify risk patterns and make loan decisions.