Commercial banks receive a lot of applications for credit cards. Many of them get rejected for many reasons, like high loan balances, low-income levels, or too many inquiries on an individual's credit report, for example. Manually analyzing these applications is mundane, error-prone, and time-consuming (and time is money!). Luckily, this task can be automated with the power of machine learning, and pretty much every commercial bank does so nowadays. In this article, we will build an automatic credit card approval predictor using machine learning techniques, just like real banks do.

Commercial banks receive a lot of applications for credit cards. Many of them get rejected for many reasons, like high loan balances, low-income levels, or too many inquiries on an individual's credit report, for example. Manually analyzing these applications is mundane, error-prone, and time-consuming (and time is money!). Luckily, this task can be automated with the power of machine learning, and pretty much every commercial bank does so nowadays. In this notebook, we will build an automatic credit card approval predictor using machine learning techniques, just like real banks do.

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.

In the midst of a political climate already fraught with distrust, the potential for artificial intelligence (AI) to be weaponized is giving pause to tech executives, over half of whom state that regulation of AI is "critical for its safe development," according to the 2019 Edelman Artificial Intelligence survey, conducted in coordination with the World Economic Forum (WEF). The survey found that 54% of tech executives and 60% of the general population said they believe that regulation is necessary. The report cites cases in which AI is used to evaluate attributes about someone's life: "Loan analyses including credit card applications are now often performed using AI algorithms. Yet, how can an algorithm be held accountable if a customer feels that a decision about their credit card application was wrong? Many argue that people have a right to know how decisions that affect them are being made."