Credit Risk Management
Overview
Assessing creditworthiness for each client is complex. Banks can now use AI to define the likelihood of default from both commercial and consumer clients.
Requirements
Banks would like to better define the likelihood of default from both commercial and consumer clients. The need to analyse and monitor the full data set in real-time will help to improve the accuracy of ongoing analysis.
An AI model is able to predict which clients are most likely to be at risk of defaulting on their financial obligations.
Challenges
Crunching millions of data points can be tricky and the results are not obvious. Applying innovative methods can also lead to a difficult monitoring of performance, creating more challenges.
Thanks to collaborative AI clients are able to spot the most important KPIs and integrate them throughout the life-cycle of their risk processes.
Solution
A real-time monitoring of credit assessment performance, providing risk managers with more informed KRIs, risk appetite and an improved value chain.
Design the solution, from the data used to the metrics adopted. Using increased data can be challenging, due to the millions of data points.
Benefits
- Metrics defined in advance
- Definition of monitoring KPIs
- Truly collaborative approach, from design to development and deployment
- Real-time monitoring of performance