Assessing the Performance of Machine Learning Algorithms for Credit Risk Assessment

Assessment of credit risk is crucial for modern economic systems. Historically, methods from statistics and manual auditing have been used for evaluating it. The latest advances in financial AI are a consequence of an influx of models which are made by machine learning for assessment of risk in credit that have garnered a massive interest from business and also academia. To solve issues of credit risk, a number of significant academic contributions over the previous eight years (76 publications) have used ML, statistical modeling, and DL techniques. In particular, utilizing open datasets, we provide an interactive methodology of categorization for credit which is driven by ML credit threat methodologies and their resulting ranking. We go on to explore other issues such data inequalities, inconsistent datasets, modeling accountability, and insufficient use of the deep learning methods. Our review's findings indicate that 1) the majority deep learning algorithms perform better at estimating credit risk than traditional machine learning and analytical algorithms, and 2) collective approaches offer more accuracy when compared to individual models. Lastly, we provide tables that summarize the data sets and recommended models.