Machine Assistance for Credit Card Approval? Random Wheel can Recommend and Explain
This work addresses the need for trustworthy machine assistance in credit card approval processes for bankers, offering incremental improvements in interpretability and confidence.
The authors tackled the challenge of automating credit card approval by developing an enhanced random wheel classifier that provides more accurate and precise recommendations, along with interpretable confidence measures and explanations for each application.
Approval of credit card application is one of the censorious business decision the bankers are usually taking regularly. The growing number of new card applications and the enormous outstanding amount of credit card bills during the recent pandemic make this even more challenging nowadays. Some of the previous studies suggest the usage of machine intelligence for automating the approval process to mitigate this challenge. However, the effectiveness of such automation may depend on the richness of the training dataset and model efficiency. We have recently developed a novel classifier named random wheel which provides a more interpretable output. In this work, we have used an enhanced version of random wheel to facilitate a trustworthy recommendation for credit card approval process. It not only produces more accurate and precise recommendation but also provides an interpretable confidence measure. Besides, it explains the machine recommendation for each credit card application as well. The availability of recommendation confidence and explanation could bring more trust in the machine provided intelligence which in turn can enhance the efficiency of the credit card approval process.