Predictive ML for Lending & Fintech in India | Credit Scoring, Collections, Fraud

FILE 01 / THE LENDING ML PROBLEM

CIBIL cuts you off from 60% of India. Rules-based scoring cuts you off from the other 40's edge cases.

DIAGNOSIS The thin-file gap

India's credit bureau data covers 400M-odd people. That leaves 800M+ adults invisible to traditional scoring, including most gig workers, small business owners, and first-generation earners in their 20s. Your rejection rate on that segment is 90%+, and the ones you do approve default at 4-6x your normal book.

Alternate-data ML changes the shape of this problem. Bank statement analysis, UPI transaction patterns, GST filings, telecom recharge behaviour, device signals all combine into a score that works on thin-file customers. Approve more of the safe ones, reject fewer of them wrongly, price the risky ones for what they cost.

COMPLIANCE RBI-aware from day one

✓ Every credit decision ships with SHAP explanation attached
✓ Full audit trail of features, model version, and score at decision time
✓ Bias monitoring across protected attributes (age, gender, geography, religion)
✓ Digital Lending Guidelines documentation ready for regulatory review
✓ Deployable in AWS Mumbai, Azure India, or on-prem inside your DC
✓ DPDP-compliant PII masking in training data with re-identification controls
✓ No black-box neural nets used for credit decisions
✓ Quarterly model performance reviews aligned to RBI expectations

FILE 02 / ML PATTERNS FOR INDIAN LENDERS

Six models that move book economics

LND-01

Alternate-Data Credit Scoring

Bank statements (via Perfios / Karza / Finbox), UPI patterns, GST filings, telecom recharge, app usage. Scores new-to-credit customers CIBIL can't see. Straight-through processing on the confident bucket.

70-80% AUC ON NTC SEGMENTS

LND-02

Early Default Warning

Predicts which performing loans will slip into 30 DPD or 90 DPD 60 days ahead. Your risk team intervenes early with restructuring or step-up collections while recovery is still possible.

10-20% REDUCTION IN NPA FORMATION

LND-03

Collections Prioritization

Ranks delinquent accounts by expected recovery per hour of agent effort. Collections team calls the accounts with highest expected recovery first, skips the ones the model says won't pay regardless.

20-35% COLLECTIONS UPLIFT

LND-04

Real-Time Transaction Fraud

Scores every incoming UPI, card, wallet, or net-banking transaction in under 100ms. Rules plus ML combined. Auto-approve, step-up-auth, or hold-for-review based on score.

SUB-100MS REAL-TIME API

LND-05

Cross-Sell & Top-Up Propensity

Predicts which existing customers will accept a top-up loan, credit card, insurance, or investment product. Your CRM gets a ranked propensity list per product per customer.

3-5X CROSS-SELL CONVERSION

LND-06 Bonus AI layer

KYC Document Extraction (Bonus AI layer)

Not strictly predictive ML but often bundled: OCR plus LLM extraction on Aadhaar, PAN, bank statements, ITR, salary slips into structured fields with confidence scores. Speeds up onboarding.

60-80% ONBOARDING TIME CUT

Want to see what your book can predict? 15-min call. Tell us the product (personal loan, business loan, credit card, BNPL), monthly volume, current scoring approach. We come back with a data-audit checklist and prototype scope.

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FILE 03 / LENDING STACK INTEGRATIONS

Fits into your LOS, LMS, and collections stack

INT-01

LOS & LMS

FinnOne · Finflux · Lentra · Nucleus · CredgeAI · Sanchez · Custom LOS / LMS

INT-02

Bureau & Data

CIBIL · Experian · Equifax · CRIF · Perfios · Karza · Finbox · GSTN · IIB

INT-03

Collections

Custom collections CRM · Ozonetel · Ameyo · Exotel · WhatsApp reminder flows via BookMySMS

INT-04

Deployment

AWS Mumbai · Azure India · On-prem DC · Kubernetes · Docker · Real-time FastAPI serving

FILE 04 / PROCESS & PRICING

6-10 weeks. Fixed pricing per phase.

WEEK 1

Discovery

Product, book size, current scoring, data availability, RBI constraints. Written feasibility doc.

PRICE: FREE

WEEKS 2-3

Prototype

Baseline model on your data. Accuracy report, SHAP samples, false-positive analysis. Your risk team reviews.

PRICE: ₹2-3 L

WEEKS 4-10

Production Build

Feature store, retraining pipeline, RBI audit trail, SHAP explainer, API integration into LOS or collections, security review, deployment.

PRICE: ₹8-20 L

ONGOING

Managed Ops

Drift detection, quarterly retraining, bias monitoring, RBI-audit-log maintenance, model performance reviews.

PRICE: FROM ₹50K/MO

ML stack, the ShowUpAI proof of work and pricing across all verticals live on the parent page: Predictive ML Development

REF-A Parent & Related Services

REF-B Predictive ML by Industry

REF-C Guides & Overview

FILE 06 / FIELD MANUAL — FAQ

Questions credit and risk leaders ask on every call

Can ML help underwrite new-to-credit customers?

Yes. This is one of the highest-value use cases in Indian lending. Alternate-data models use bank statement analysis (via Perfios / Karza / Finbox), UPI transaction patterns, GST filings, telecom recharge behaviour, and app usage to score customers without CIBIL history. Combined with a small internal-data model, we typically get 70-80% AUC on new-to-credit segments where CIBIL alone gives no signal.

Is the model RBI-compliant?

We build with RBI's Digital Lending Guidelines and DPDP Act in mind: SHAP-based explanations for every credit decision, full audit trail of features used, bias monitoring across protected attributes, and documentation ready for regulatory review. We do not use black-box neural nets for credit decisions. Every score can be defended to a regulator.

How does collections prioritization work?

Model predicts recovery probability for each delinquent account plus expected recovery amount. Collections team gets a ranked queue: highest expected recovery per hour of agent effort at the top. Reduces wasted calls on accounts that won't pay and focuses effort where it recovers most. Typical improvement: 20-35% more collections recovered with the same team size.

Can the model catch transaction fraud in real-time?

Yes. Real-time scoring APIs return a fraud probability under 100ms for each incoming transaction. Model combines rules (velocity, geography, device change) with ML on transaction patterns and merchant reputation. Approved transactions pass through, borderline get step-up authentication, high-risk get blocked or held for review. Handles UPI, card, wallet, and net-banking flows.

What data do we need?

For credit scoring: 18-24 months of loan applications with outcomes (approved / declined / performance for approved loans). Minimum 15,000-30,000 historical loans with a mix of good and bad. For fraud: 12+ months of transaction data with confirmed fraud flags (even 1-2% fraud volume is enough with the right techniques). For collections: 12 months of delinquency + recovery data.

How much does it cost?

Prototype: ₹2-3 lakh (3 weeks). Full production build: ₹8-20 lakh depending on integration complexity with your LOS or transaction system (6-10 weeks). Ongoing ops: ₹50,000/month covering monitoring, quarterly retraining, drift detection, and RBI-audit-log maintenance. Scoping is free — start via the contact form.

Ready to underwrite the 800M CIBIL can't see?

Book a 15-min call. Tell us your product and monthly volume. We come back with a data-audit checklist and prototype scope in 48 hours.

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