Development of credit scoring “turn–key project”
Building a scoring “on a turn-key basis” (automated system of credit scoring) is one of the most urgent tasks for microfinance organizations. Even in today’s realities in the prevailing number of banks, the IFC, the IWC, in evaluating borrowers, give preference to the work of the underwriting department.
It is considered that the accuracy of manual underwriting, including for new customers, is higher than the result of automated scoring in conjunction with underwriting.
Unfortunately, it is not!
Moreover, the use of automated credit scoring on the basis of machine learning to avoid credit errors (at least 5% – for scoring projects developed in FscoreLab.In this case, the unconditional advantage of the machine over a person can be considered the decision-making time, which is reduced to tens Additional contribution and reduction in the likelihood of fraud on the part of the manager or credit inspector.
FscoreLab helps microfinance organizations to implement the process of transition to modern realities and increase the effectiveness of lending by creating an individual automated scoring model based on machine learning.
The model built for you will be based on your historical data on the issuance of loans, adapted to the business model and development strategy and the company’s risk-oriented approach. Among other things, for each project you can use the following functions: social networks, cookies, biometrics, geodata (if available).
We are proud of the quality of our turnkey solutions and take care of our reputation, therefore, the first step towards cooperation and development of scoring is a detailed discussion of the details of cooperation with the CEO and CTO FscoreLab.
The mood of the scorecards (credit scoring) of the property is the unconditional quality guarantee of FscoreLab. If the discrepancy between the forecasts on the test data with the data “on the battlefield” is more than 3% – 100% of the cost of the boxed solution (subject to service requirements) or 200% of the cost of the incident for the month of the SaaS (service) version paid for the month.
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