Auto-identification of customers to enable faster credit decisions | Case Study

Auto-identification of customers to enable faster credit decisions

Client – SalaryDost

Project date

September 2020

Type of work

Product SaaS to improve the speed of credit decisions for personal loans. products deployed

Document Classification, Document Image Quality Check, Document Anti-fraud Check, document number verification, Aadhaar Redaction, Bank Statement Analyzer, and Document Data Extraction.

Description of Project

A platform for unsecured loans of up to Rs 1.50 lakh for salaried employees, SalaryDost, founded by Mrityunjay Shahi in 2008, is a Thane-based fintech leader and has disbursed over Rs. 100 million in debt thus far. The platform’s credit approval process is simple and works based on Aadhaar, PAN, and Bank Statement verification. SalaryDost uses IN-D to auto-check the quality of PAN and Aadhaar images received. Post this, IN-D performs an anti-fraud check on these documents, followed by data extraction of key attributes from both documents, and finally verification of the document numbers with the NSDL and UADAI databases to ensure the authenticity and validity of these documents. All of this happens in a matter of 5 seconds! IN-D sends the result of all the aforementioned checks to SalaryDost’s workflow system, where a Credit Manager reviews the result of our document analysis, performs a bank statement check, and approves or rejects the loan application. SalaryDost’s USP is that it targets a credit decision within 30 minutes and its services are accessible to lower-income groups.

SalaryDost is presently also testing IN-D’s bank statement analyzer. Post successful integration of this feature, all the three documents received by SalaryDost from its customers will be handled and processed by IN-D, with the Credit Manager’s TAT further shortened and their work simplified. All this without SalaryDost having to work with different vendors and integrate different software with their workflows for the three distinct document types. Further, since SalaryDost receives thousands of people’s Aadhaar card images, to remain compliant with RBI KYC regulations, SalaryDost is deploying IN-D’s Aadhaar redaction tool, which will automatically scour the SalaryDost document database, identify Aadhaar cards, and reliably redact the first eight digits from all of them. With IN-D tools making SalaryDost’s loan approval process faster, SalaryDost is growing 125% on an MoM basis, has over one million app downloads, 10,000+ customers, 5,000 daily app downloads, and 100k MAUs with 3.7 million sessions across 5000 cities.