Credit decisioning
Gradient-boosted and alternative-data scorecards with reason codes that credit committees and regulators can follow.
Financial Services
AI for banks, NBFCs, insurers and fintechs.
We help financial institutions decide faster and safer: sharper credit models, real-time fraud signals and agents that clear operational backlogs, all built to satisfy auditors and regulators from day one.
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The challenge
What is holding financial institutions back.
01
Legacy data, siloed by product
Loans, cards, deposits and collections live in different systems, so no one sees the full customer and every model starts with months of data wrangling.
02
Fraud that adapts faster than rules
Static rules either miss new patterns or drown analysts in false positives, and every false decline costs a customer.
03
Manual, document-heavy operations
KYC, underwriting and servicing still rely on people reading PDFs, which caps growth and stretches turnaround times.
04
Regulators want explanations
Any model that touches credit or customers must be explainable, fair, monitored and auditable, which most pilots were never designed for.
Where AI pays off
Use cases we build and run.
Each one is scoped to a measurable business outcome and shipped to production, not left as a proof of concept.
Real-time fraud detection
Streaming features and graph signals that catch mule networks and account takeovers in milliseconds.
KYC and onboarding agents
Document AI that reads IDs, bank statements and financials, then flags only the true exceptions to a human.
Collections optimisation
Propensity models that decide who to contact, when and through which channel to recover more with less friction.
Relationship manager copilot
A grounded assistant that briefs RMs on each client, drafts follow-ups and surfaces cross-sell signals.
Regulatory reporting automation
Pipelines and LLM checks that assemble, reconcile and validate returns before they go out the door.
Our approach
How we deliver in Financial Services.
The same disciplined loop we use everywhere, adapted to the data, risks and rhythms of your industry.
Built responsibly
- RBI guidelines on digital lending and outsourcing
- DPDP Act, 2023 consent and data minimisation
- Model risk management and validation standards
- PCI DSS scoped data handling
- Explainable adverse-action reason codes
Typical stack
01
Weeks 1 to 2
Risk and value discovery
We start with your risk appetite and P&L, not the technology.
- Map decisions across the credit, fraud and servicing lifecycle
- Audit data lineage, quality and consent across core systems
- Rank use cases on value, feasibility and model-risk tier
02
Weeks 2 to 3
Governed design
Every model is designed to pass model-risk review before it is built.
- Target architecture inside your cloud or on-prem boundary
- Fairness, explainability and monitoring requirements agreed upfront
- Champion and challenger evaluation plan with your risk team
03
Weeks 3 to 8
Build with your analysts
Small senior squads ship weekly with your credit and fraud teams embedded.
- Feature store and reproducible training pipelines
- Shadow-mode deployment against live decisions
- Model documentation and validation packs written as we go
04
Ongoing
Scale and monitor
We harden, roll out and hand over with the controls regulators expect.
- Drift, stability and bias monitoring with alerting
- Human-in-the-loop overrides and full audit trails
- Runbooks and training for model owners
Case studies
Results from the field.
Case study 01
Mid-size NBFC, India
Doubling underwriting throughput without adding headcount
The challenge
Loan applications waited three to five days in a manual queue while analysts re-keyed bank statements and bureau data.
What we did
We deployed a document AI pipeline for statements and ITRs, a new scorecard with reason codes, and an agent that routes only borderline files to underwriters.
Results
2xapplications processed per analyst
70%files auto-decisioned within policy
<1 daymedian turnaround
Case study 02
Digital payments company
Cutting fraud losses while approving more good customers
The challenge
Rule-based fraud checks were blocking legitimate users and still missing coordinated mule accounts.
What we did
We built streaming features, a graph model over device and account links, and an analyst console that explains each alert.
Results
38%fewer false positives
24%reduction in fraud loss rate
50msdecision latency at p95
Case study 03
General insurer
Claims triage that routes the simple ones straight through
The challenge
Every motor claim was manually reviewed, including thousands of low-value, low-risk cases each month.
What we did
An intake agent now reads claim forms and photos, estimates severity, checks policy coverage and fast-tracks straightforward claims.
Results
45%of claims fast-tracked
3xfaster settlement on simple claims
Case studies illustrate the kind of engagements we run. Details are generalised and figures are indicative, not guarantees of results.
Other industries
Explore more of our work.
Retail & D2C
Demand forecasting, personalisation, pricing and catalogue intelligence.
Healthcare & Life Sciences
Clinical documentation, claims processing and patient-journey analytics.
Manufacturing & Supply Chain
Predictive maintenance, quality inspection and inventory optimisation.
SaaS & Technology
AI features inside your product, usage analytics and support automation.
HR & Talent
Screening, matching, workforce planning and people analytics at scale.
Let's talk about Financial Services.
Tell us where you are. We will come back within one business day with a point of view, not a sales deck.