Eighteen production-grade AI offerings for lenders, NBFCs and fintechs across underwriting, fraud, collections, customer service and RBI compliance. Built on your data, deployed in your environment.
Why now
People reading statements, calling borrowers and compiling returns. We build the AI that does the reading, scoring, calling and explaining, inside the LOS, LMS and dialler you already run.
Thin-file salaried borrowers get declined on bureau alone while the bank statements and payslips that would approve them go unread.
Edited PDFs, synthetic identities and mule rings pass KYC and OCR. The loss surfaces months later as a first-payment default.
Bucket 0 to 30 is worked by dialler teams. Cost rises with the book, and tone on recorded calls is a conduct risk under RBI rules.
The offerings
Offerings 01 to 04Underwriting and risk
The biggest prize in a personal-loan or SME book is the thin-file borrower you decline today and the stressed account you see too late.
The problemBureau-only scorecards decline thin-file borrowers who would repay.
What we buildGradient-boosted and graph models on bank flows, UPI behaviour, employer stability, device signals and bureau data, trained and validated on your own disbursed and rejected book. Served as an API to your LOS with reason codes on every decision.
The problemStatements arrive from dozens of banks in dozens of layouts, and analysts miss edited files.
What we buildLLM plus OCR pipeline that parses any Indian bank statement into clean transactions, with tamper and synthetic-statement detection at pixel and transaction level. Salary regularity, EMI load, bounce history and lender exposure from uploaded PDFs or Account Aggregator feeds.
The problemA salaried loan is really an employer loan, yet whether the employer is real, and still paying PF for this borrower, goes unchecked.
What we buildVerifies that the declared employer exists and is active, and that the applicant appears in its EPFO (PF) contributions. On top of that, a continuous employer health score from MCA filings, EPFO contribution data, hiring and layoff news and salary-delay patterns in your own book. Watchlists and category caps that update themselves, used in underwriting and portfolio monitoring.
The problemThe first signal of stress is a bounced EMI, which is already a DPD.
What we buildA model that predicts the probability of a missed EMI 30 days ahead from repayment history, bank behaviour, app engagement and employer score. Daily risk tiers pushed into the LMS and collections queue, with pre-emptive nudges and restructure offers before the due date.
Offerings 05 to 09Fraud and KYC
Deepfakes, reused photos, synthetic identities and mule rings are now the main source of first-payment default in digital lending. Five offerings stop them before disbursal and keep Aadhaar data compliant.
The problemEvery upload has to be sorted and checked for Aadhaar and PAN by hand, which slows onboarding and lets mismatches through.
What we buildVision models that find and classify Aadhaar and PAN cards inside any photo, scan or multi-page PDF, read the fields, validate format and checksum, and cross-check them against the applicant's details. Responds in milliseconds, so it runs inline in the onboarding flow.
The problemLoan against property and secured loan files carry full Aadhaar numbers across hundreds of scanned pages, and UIDAI rules require them masked.
What we buildFinds every Aadhaar number, card image and QR code across scanned agreements, property papers and KYC packs, and masks the first eight digits before the file is stored or shared. Works on the existing back-book in bulk as well as on new files, with a page-level audit log.
The problemSelfies, video KYC and uploaded documents are being beaten by face-swaps, replay attacks and AI-generated or edited files.
What we buildLiveness detection, face match and injection-attack detection trained on Indian ID formats and lighting conditions, plus detection of AI-generated and edited documents at pixel and layout level. Runs on-premise or in your VPC so biometric data never leaves your control.
The problemThe same face or the same document photo reappears under different names, and nothing compares it across applications.
What we buildFace and image embeddings for every selfie and document photo, searched against your full application history in milliseconds. Flags the same person under a new identity, reused document images and known-fraud faces, with the matched applications shown side by side.
The problemFraud rings apply as dozens of clean-looking individuals, and anomalies are flagged after the money has moved.
What we buildLink analysis across phone, device, email, address, bank account and employer to find clusters. Graph features and millisecond-scale velocity rules scored at application, payout and across live transaction networks, with an analyst console that shows the linked entities behind every flag.
Offerings 10 to 12Collections
Collections shows a measurable number within 60 days without touching your credit policy. Three offerings that cut cost to collect and conduct risk together.
The problemBucket 0 to 30 reminders are worked by human dialler teams, and coverage drops at month end.
What we buildVoice agents in Hindi, Kannada, Tamil, Telugu, Hinglish and English that remind, capture promise-to-pay and offer settlements within approved bands. Guardrails for calling hours, language and escalation are built in, with hand-off to a human the moment the borrower asks or disputes.
The problemAgents call lists in order of amount or DPD, not likelihood to pay.
What we buildA model that ranks each delinquent account by propensity to pay and recoverable amount, then recommends channel (voice AI, WhatsApp, human, field), best time and settlement offer. Feeds the dialler and the voice agents directly.
The problemQA listens to a small sample of recovery calls by ear, and harassment-risk language is found only after a complaint.
What we buildTranscription of every call in Indian languages, compliance scoring against RBI recovery guidelines and your script, and daily flags, agent scorecards and coaching clips for team leads.
Offerings 13 to 14Customer-facing
Two offerings that lift conversion and cut support cost inside the app and on WhatsApp.
The problemEligibility, document and EMI questions flood support in a dozen languages, and drop-off happens at the document step.
What we buildAn assistant in the app and on WhatsApp that answers in the borrower's language, with tool-calling into your LOS and LMS for eligibility, document status, EMI, foreclosure and statements. Escalates to a human with the full context attached.
The problemTop-up and pre-approved limits run as monthly batch campaigns that ignore current cash-flow and risk.
What we buildReal-time decisions on limit, top-up, rate and tenure for each borrower, using repayment behaviour, bank flows and the early-warning score. Delivered in-app, on WhatsApp and through the assistant.
Offerings 15 to 18Operations and compliance
Four offerings that turn documents in any language into decisions, filings and audit trails.
The problemPayslips, property papers, ITRs and handwritten forms arrive in English, Hindi and regional scripts, and generic OCR fails on them.
What we buildDeep-learning OCR that reads printed and handwritten text in English and major Indian scripts across scans, phone photos and PDFs, and returns structured fields with confidence scores. It feeds every other offering on this page, from statement parsing to Aadhaar masking.
The problemA credit analyst opens bureau, statement, payslip and KYC in four tabs and types the memo by hand.
What we buildOne agent that pulls bureau, statement analysis, employer score, fraud flags and KYC into a one-page credit memo with a recommended decision checked against your policy. The analyst edits and approves, and every step is logged.
The problemRBI returns and co-lending reports are compiled by hand, and invoices, expense claims and contracts are keyed in or scanned manually.
What we buildAutomated RBI returns and co-lending reconciliation with partner banks. Document AI for invoice reconciliation, expense management and vendor bills: extract, validate and post to the ERP. Clause-level regulatory scanning of contracts and policies.
The problemRBI's digital lending guidelines expect explainable, fair decisions, and model drift goes unnoticed between annual validations.
What we buildReason codes on every score with SHAP-style attributions in plain language, drift, stability and fairness monitoring on live decisions, and a model inventory with validation reports and an audit console.
Where to start
Collections shows a measurable result within 60 days, does not touch your credit policy, and builds the trust that underwriting work needs. The bigger prize comes once that trust exists.
Voice AI collections on bucket 0 to 30 plus the early warning model, measured against a control group.
Aadhaar and PAN detection, deepfake selfie and document checks, fraud photo match and mule detection on live applications.
Bank statement intelligence, the alternative-data credit model, PF-based employer verification and the credit ops copilot, validated on your book.
Aadhaar masking, multilingual OCR, prioritisation, call QA, vernacular assistant, offer engine, regulatory reporting and the explainability layer.
Delivered work
FAQ
Digital lenders and NBFCs offering personal, SME, gold and vehicle loans, co-lending platforms, payment companies and wealth platforms in India. The offerings are modular, so a lender can start with collections or fraud and add underwriting later.
No. Every offering runs on-premise or inside your VPC. Models are trained on your data in your environment and you own the code, the models and the data.
Every credit decision carries reason codes and a source trail, voice agents operate inside calling-hour, language and escalation guardrails, every recovery call is transcribed and scored, and the explainability layer monitors drift and fairness continuously.
Two weeks of discovery on real applications, statements and calls, then the highest-value offering live on a pilot segment in about eight weeks, measured against a control group. Pricing is fixed and milestone-based, with two months of support after go-live.
Loan origination and management systems, bureaus, Account Aggregator frameworks, diallers and telephony, WhatsApp Business, core banking and ERP, through APIs and secure data exchange.
