From Data to Decisions: The Impact of AI on Credit Risk Management
A lender could not price risk on borrowers with no formal credit history. We trained a model on their historical loan book. Defaults fell 30 percent.
Background
A financial service provider was facing challenges with managing credit risk, particularly in assessing the creditworthiness of potential borrowers. They wanted to improve their risk management practices and find ways to more accurately predict and manage credit risk. The financial service provider reached out to AfroPavo Analytics for help.
Solution
AfroPavo Analytics worked with the financial service provider to assess their current credit risk management practices and identify areas for improvement. We proposed using machine learning and AI to analyse large amounts of data including past customer data, demographic information, transactional history, and other relevant data.
We developed a customised machine learning model that could predict the creditworthiness of potential borrowers with a high level of accuracy. The model was based on a wide variety of historical loan data and was able to identify patterns and trends that are not apparent to traditional credit-scoring models.
We also developed a customised dashboard that allowed the financial service provider to visualise and understand the results of the model and make data-driven decisions about lending.
Results
As a result of implementing our solution, the financial service provider was able to significantly improve its credit risk management practices. The use of machine learning and AI allowed the provider to more accurately predict and manage credit risk, which led to a decrease in loan defaults by 30% as well as an increase in profitability. The financial service provider was also able to save time and resources that were initially used to manually review and approve loan applications.
