Faster loan file processing
Digitised underwriting that scales an SME lending book.
CSL Finance is a specialised NBFC providing credit to SMEs and high-growth businesses across India. A significant portion of its borrowers operate in seasonal or semi-organised sectors underserved by traditional banks. Over time, CSL developed a strong proprietary credit framework capable of assessing these unconventional income profiles — but it relied on manual spreadsheets and fragmented workflows. As the loan book scaled past ₹1,000+ crore, those processes began limiting speed and consistency. We digitised the methodology into a centralised AI-assisted platform.
The credit team relied on a complex framework of weighted parameters, qualitative assessments, and financial indicators captured manually across multiple files. The model produced strong outcomes, but the structure around it was heavily manual.
Manual calculation of risk parameters increased processing time per loan file
Inconsistent data entry across applications affected accuracy and audit readiness
Multiple spreadsheets and reports fragmented the underwriting workflow
Limited processing capacity restricted how many applications credit officers could evaluate per month
The system needed to preserve CSL’s ability to assess seasonal, fluctuating, or unconventional income — not flatten it into a generic scoring model
AI-assisted credit intelligence platform: digitised scoring engine, Excel parsing for applicant data, validated structured inputs, one-click PDF credit reports, and alternative income modelling.
An NBFC with ₹1,000+ Cr AUM lending to SMEs with seasonal and unconventional income. Their proprietary credit model lived on manual spreadsheets and didn’t scale.
Intelligence and engineering applied where they drive measurable change.
CSL’s internal evaluation logic — weighted averages, risk indicators, qualitative scoring — was transformed into a structured Scoring & Analysis Engine that automatically calculates credit scores and generates a standardised Risk Assessment Summary for every file.
An Excel parsing engine lets officers upload applicant spreadsheets and pre-fills the application automatically. Intelligent dropdowns and validated inputs replace open-text fields, ensuring structured, audit-ready data.
Once an evaluation is complete, the platform generates a comprehensive PDF credit report summarising profile, scoring breakdown, and final recommendation — ready for credit committee review.
Credit teams can capture alternative indicators — business cycles, inventory flows, sector-specific revenue fluctuations — preserving CSL’s ability to confidently fund borrowers traditional models would overlook.
Faster loan file processing
Application handling capacity
Manual data-entry errors
Standardised underwriting framework
The platform converted a manual underwriting process into a scalable digital credit intelligence system. More importantly, the digitised framework lets CSL confidently fund entrepreneurs with seasonal or unconventional income profiles — expanding access to capital for underserved SMEs.
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