The EyeOnRisk© Platform
EyeOnRisk Platform brings together the complete credit score modeling process with an advanced technology to meet Financial Institutions specific needs. We have taken machine learning and credit-scoring out of the black box.
Our end-to-end platform solves the “consumer credit scoring problems” of the new era:
- How to combine financial and alternative data for a more accurate credit scoring model
- How to easily use machine learning techniques with clear model explanation capabilities
- How to solve the pain of moving from research to production
- How to use autonomous modeling flow, continuous monitoring for optimization an complete transparency.
For a complete view of The EyeOnRisk© platform and it’s outcome please download the one-pager
Reducing Default Rates
With EyeOnRisk© Platform lenders get an advanced enterprise solution that significantly reduces risk and decreases overall default rates:
- Advanced, machine learning-based modeling capability that allows processing a much higher number of data points to produce more accurate scoring
- Additional data layers based on alternative data – for example distinguishing between a freelancer and a salaried employee
- Our tool enables autonomous modeling flow – it automatically adds new records (borrowers) to the research flow. Customer can watch a real-time performance of its current model in production and to evaluate alternative algorithms that runs in the background
Increasing Lending Revenues
Using our EyeOnRisk© Platform, lenders can increase their consumer lending revenues significantly by:
- Decreasing false negatives rate – establish creditworthiness for more customers. Many customers are declined credit due to incorrect credit scoring, based on limited data. Now you have the tool to leverage both internal and external data sources.
- Decreasing hidden or partial false negatives – get more revenues from customers for whom only partial credit was approved.
- Adjusting price to risk levels – recover the cost of wrongly pricing credit due to incorrect risk assessment. Adjust the right price for high-risk customers, while gaining back low risk customers that were driven away by a high quote