A4Lеnding

The solution is tailor-made for non-banking financial institutions and leasing companies, and helps to reach optimal credit quality and acceptance rates by utilizing predictive analytics. The solution provides abilities to speed up the lending decision process as well as its automation.

Credit Scoring-as-a-Service
A4Lending is a fully or partially automated decision-making service, depending on your preferences, which is done by incorporating your risk strategy. In addition to individual’s score, the service provides the top reasons contributing to the particular score. This gives the credit inspectors improved ability to monitor and/or decide, if an application should be accepted or rejected.

Machine-Learning Scoring Process
A4E maintains the scoring service to be always up-to-date. By analyzing the changes in market performance caused by the environment and the changes in customers’ segments, we determine the suitable period between the automated scorecard updates, if required, as frequent as every 2 weeks. 

Multiple Scores / Scorecards
We support multiple scorecards as easy as one. All of these are based on our machine-learning mechanism and support different characteristics sets and business rules. You could benefit from that in various ways like: to implement different scoring strategies for different client segments or to have different risk strategies at the same time or to make A/B testing for different strategies with different characteristics or to switch from one strategy to another gradually.

Minimal Upfront Costs
A4Lending needs to integrate with your own processing system, which requires minimal upfront costs. If there are particular expectations regarding the scoring rule, we have to learn about your market understanding and implement it in our modeling process.

Real-time Scoring Backed with Reasons
The response of our system is less than 5 sec. Such a fast response is essential, especially for automated decision-making. 

Integrated Business Rules
A4Lending easily blends existing business knowledge and established rules with the data-driven scoring model.