Case Study

Bank of America – Credit Card Approval Models

The Challenge

Bank of America sought to streamline credit card approval processes

Bank of America, a leading financial institution, faced a significant challenge in optimizing its credit card approval processes. The existing system relied heavily on manual assessment, resulting in inefficiencies and inconsistencies in evaluating applicants’ creditworthiness and determining appropriate credit limits. The client needed a solution that could automate these processes while ensuring accurate risk assessment and prudent credit limit assignments.

The Action

We developed sophisticated ML models for automated credit evaluation

To address this challenge, Calligo leveraged advanced machine learning techniques to develop a comprehensive solution. By analyzing various factors such as applicant demographics, employment details, credit history, financial obligations, and existing relationships with the bank, we created a machine learning model capable of accurately predicting the probability of default for each applicant.

Additionally, we engineered a secondary model that utilized the output of the first model along with supplementary metrics to calculate optimal credit limits tailored to each applicant’s risk profile and financial capacity.

Furthermore, we implemented a third model to continuously monitor credit limits, adjusting them based on established risk thresholds and recommending limit increases after a thorough analysis period of six months.

The Impact

The implemented solution rapidly elevated Bank of America’s credit card approval processes

The implementation of this solution rapidly elevated Bank of America’s credit card approval processes, significantly enhancing efficiency and accuracy while mitigating risks. By automating credit evaluation and limit assignments, the bank experienced reduced processing times and minimized human errors.

Moreover, the predictive capabilities of our machine learning models enabled Bank of America to make data-driven decisions, resulting in more prudent risk management and optimized credit utilization.

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