Credit risk scoring is a crucial part of bank portfolio management. By including innovative machine learning methods and new sources of information about your clients and leads, you can reduce losses incurred by defaults and increase the pool of potential customers.
Traditional methods of credit risk scoring are limited to existing clients or bank statements from new clients. At the same time, typical data sources are helpful but relying solely on them restricts the precision of credit risk scoring as they do not capture the entire client profile. Conventional credit risk scoring models are easy to interpret but often surpassed by other approaches.
We combine the traditional scoring data with insights from the clients' transactions, their digital behavior, geolocation data, and their connections with other persons and apply several advanced machine learning approaches as natural language processing, graph neural networks, and others. We then combine the results into a comprehensive risk score based on a complete 360° profile of the customer.
Since most of our data sources do not rely on internal bank data, we can even score non-clients and new-to-bank clients by analyzing their similarities with the existing customers.
- Scoring even non-clients based on their online behavior and connections
- Additional info about client behavior useful even for existing customers
- Combining several machine learning approaches to get a 360° risk profile of the client