AI for banking that survives RBI audit and actually ships.
We embed engineers inside banks, NBFCs, fintechs, and wealth platforms for 4 weeks trace every onboarding journey, read every underwriting memo, shadow every credit meeting. Then we ship RBI-ready, DPDPA-aligned AI every month. 48% fewer false-positive fraud holds is not a slide it is what a listed NBFC measured in 90 days.
fewer false-positive fraud holds
faster personal-loan decisioning
lift in collections right-party contact
Why "AI in banking" projects stall at the compliance gate
The model has no audit trail
RBI and SEBI expect every automated decision to be explainable, reproducible, and linkable to a policy. Opaque vendor models fail the first inspection.
Core-banking is treated as a black box
Teams integrate with CRM and a datalake but never read core-banking at transaction granularity. The model misses 60% of the signal.
Risk and IT don't speak the same language
Risk wants Basel-aligned explainability. IT wants REST APIs. Nobody writes the translation layer the project stalls for months.
DPDPA and data-localisation were an afterthought
The pilot used a US vendor with cross-border data flows. Legal kills the rollout. Six months gone.
Every metric is a problem we solved.
Real numbers from live banking & financial services deployments.
Onboarding drops 34% of applicants
Credit memos take 6 hours per file
Fraud models cry wolf, starve revenue
Collections burn 70% on wrong numbers
What your team actually says.
"Our TAT promise is 48 hours. Our median is 3.2 days."
Credit Head
"We review 9,000 transactions a day. 8,700 are false positives."
Fraud Ops Lead
"Onboarding conversion is flat while CAC is up 22%."
Head of Digital
"Collections cost-to-collect is 14% for my self-employed pool."
Collections Head
"Every new model needs a 60-page model risk doc before go-live."
Chief Risk Officer
"My analysts spend 80% of time pulling data, 20% making decisions."
Credit Analyst Lead
Use cases that move the needle.
01Onboarding Funnel Diagnostic
AI stitches events across KYC vendor, core banking, and CRM to pinpoint where applicants drop and why.
02AI Credit Memo Drafting
Generates a policy-aware underwriting memo from raw financial documents and bureau pulls.
03Behavioural Fraud & Dispute Scoring
Graph ML scores transactions and disputes against each customer's behavioural baseline.
04Collections Contact-Propensity Model
Orchestrates WhatsApp, IVR, dialler, and field visits based on per-customer contact propensity.
05KYC Document Intelligence
Extracts, validates, and cross-checks Aadhaar, PAN, GST, and bank-statement data against bureau records.
06AML / Transaction Monitoring Triage
Re-ranks TMS alerts so analysts see the highest-risk cases first, with narrative context auto-generated.
Four pillars. Translated for BFSI.
An Engineer Inside Your Risk and Ops Room
Our Forward Deployed Engineer joins credit committee, sits in on fraud war-rooms, and walks the collections call centre. They speak core-banking, read RBI master directions, and understand why your NPA coverage is what it is.
Risk-Grade R&D
Our Research Engineers run 2-week sprints on your own de-identified portfolio roll-rate shifts, behavioural cohorts, early-warning features so the model survives model-risk review, not just a dev-environment backtest.
A Working Release Every 30 Days
Mid-month shadow-mode in your staging core-banking. End-of-month go-live on one cohort. Every release comes with explainability reports, model-risk documentation, and sign-off from your CRO.
We Own the Risk & Revenue Number
We don't bill hours. Every month you get delta on TAT, false-positive rate, RPC, NPA coverage, and cost-to-serve. If the numbers aren't moving, you'll know and so will your board.
What happens in the first 30 days.
Journey Walk & Data Inventory
Our engineer shadows onboarding, underwriting, fraud ops, and collections. Maps every vendor touchpoint and every core-banking table involved.
Regulatory & Model-Risk Mapping
RBI master directions, SEBI circulars, DPDPA, and PCI-DSS obligations translated into concrete model-risk and explainability requirements.
AI Opportunity Scoring & Mockups
Each opportunity scored by ₹ impact, risk, feasibility, and data readiness. Mockups built for top 3 use cases with CRO and CIO validation.
Strategy Deck & Board-Ready Roadmap
A 12-month AI roadmap with model-risk strategy, integration architecture, and cost-benefit case ready for risk committee and board.
What you walk away with
We speak your regulatory language.
Regulations & Certifications
- RBI Master Directions — KYC, outsourcing, digital lending, cyber-security
- SEBI Circulars — For wealth, broking, AMC, and fintech platforms
- DPDPA 2023 — Indian Digital Personal Data Protection Act
- PCI-DSS — Payment Card Industry Data Security Standard
- IRDAI Guidelines — Where bancassurance is in scope
Data & Protocol Standards
- ISO 20022
- NPCI UPI / IMPS specs
- SWIFT MT / MX
- CKYC XML
- Bureau formats (CIBIL, Experian, Equifax, CRIF)
Enterprise Systems We Integrate
- Core banking Finacle, Flexcube, TCS BaNCS, Temenos
- LOS/LMS Nucleus FinnOne, Finflux, BRE
- Fraud SAS, FICO, ACI, NICE Actimize
- CRM Salesforce Financial Services, LeadSquared
- TMS / AML Oracle FCCM, Actimize
- Data Snowflake, Databricks, on-prem Hadoop
Questions we hear on the shop floor.
Book the 4-Week BFSI Audit
RBI · SEBI · DPDPA · PCI-DSS aware. Core-banking integrated.
The first step is always a conversation.