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Scaling lending through technology and AI

We attended AAVAS Financiers’ (AAVAS) management meet, which focused on technology and analytics. The management highlighted the company’s scalable technology architecture and increasing use of AI across the lending lifecycle. A platform has been designed to support multi-fold growth without the need for major tech changes and operates largely on a pay-per-use model, limiting the need for incremental opex as the business scales. AI/analytics initiatives are already showing benefits through lower TAT, higher FTR and pre-delinquency identification, while the management remains focused on improving productivity. Technology remains a key enabler of operating leverage for AAVAS, with further benefits likely from AI-led underwriting, collections, cross-sell and pricing initiatives. We roll forward our estimates to Sep’28E and upgrade to ‘BUY’ rating (from ‘Accumulate’) with TP of INR1,685 (2.0x Sep’28 P/ABV vs. 2.1x earlier).

Technology platform built for scale; integration improving TAT:

AAVAS has integrated the loan journey from lead generation to disbursement on a common platform by replacing 10+ smaller applications. This has helped eliminate manual hand-offs and automate KYC, account aggregation, document validation, e-signing and disbursement orchestration. Consequently, this has helped reduce login-to-sanction TAT to ~6 days from ~13 days. The management indicated that the architecture can likely support INR1- 3trn book without changes, with technology and fintech partnerships largely operating on a pay-per-use model. Automated CAM preparation, bank-statement analysis and coordination across sales, credit and technical teams have also reduced underwriting and processing time. The broader benefit is 98% process standardization across branches, with greater visibility and defined TATs for each function

AI-led analytics improving process efficiency and risk identification:

AAVAS has 13 AI use cases live, with another 15 under development, spanning onboarding, underwriting, collections and support functions. Its analytics stack includes lead, application, repayment/balance and churn scores, allowing the company to identify opportunities and risks earlier in the customer lifecycle. AI-led quality checks have helped improve first time right (FTR) to 45-50%, with the management indicating this to be ~2x better than peers, thereby reducing rework and improving application quality. The company prepares the predictive bounce file based on ~15% of accounts and shares it with the collections team 7-10 days ahead of the EMI due date. This accounts for ~76% of total bounces, allowing collections to prioritize follow-ups. The company also uses bureau analytics to identify potential BT-out activity in real time and pre-select customers for top-up loans

Productivity gains to support operating leverage:

Employee productivity has already started improving as the technology stack matures, with the management indicating RO productivity at 1.24x in Jul’26 and targeting 1.5x by FY27 and 2x thereafter. AI-led underwriting, pre-delinquency analytics, digital collections and data-driven sourcing could further improve throughput without a commensurate increase in expenses. The management stated that their next opportunity is to move to AI-assisted PD analysis and dynamic pricing, which could improve yield discipline, allowing focus on exceptions rather than routine approvals.

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