AI-Powered Fintech
From Compliance to Intelligence
Fraud moves fast. Compliance doesn't wait. Most fintech teams are still solving both with manual processes and rule-based systems built five years ago. We build AI that fits the constraints of regulated financial environments.
What's Actually Getting in the Way
Fraud patterns evolve faster than your rules
Rule-based fraud detection is reactive by design. By the time you update the rules, the fraudsters have already moved on. You're always one step behind — and each step costs real money.
KYC and AML is mostly manual work
Your compliance team reads documents that a machine could process in seconds. Document verification, entity matching, and risk scoring are high-volume, low-variance tasks — exactly the kind of work AI is built for.
Credit underwriting misses good borrowers
Rigid scoring models penalise borrowers with thin credit files, irregular income, or non-traditional financial histories. Alternative data — transaction patterns, payment behaviour, cashflow — paints a fuller picture that rules-based models can't see.
Customers expect instant answers to complex questions
Account status, transaction history, loan eligibility, EMI schedules — these questions require pulling data from multiple systems and summarising it clearly. Your support team can't scale to handle this volume. A well-built AI can.
AI Systems We Build for Fintech
Fraud Detection Pipelines
Real-time ML models that adapt to emerging fraud patterns — trained on your transaction data, updated continuously, and integrated with your existing risk infrastructure.
AI-Assisted KYC & AML
Automated document verification, entity resolution, and risk scoring that cuts manual review time without cutting compliance corners.
Alternative Credit Scoring Models
ML models that go beyond bureau scores — using transaction behaviour, cashflow patterns, and repayment history to make better lending decisions.
Financial Document Processing
Extract, classify, and validate bank statements, ITRs, invoices, and contracts automatically — feeding clean structured data into your underwriting and compliance workflows.
Conversational Finance Support
LLM-powered support that handles account queries, transaction lookups, loan status checks, and common customer questions — at scale, without a human in the loop.
Portfolio Risk Intelligence
Anomaly detection and predictive analytics across your customer portfolio — surfacing concentration risks, repayment signals, and early warning indicators before they become problems.
From First Call to Production
Discover
We map your current workflows, identify where AI creates real ROI, and validate the highest-impact use case with a working prototype — usually within 2–3 weeks.
Build
We architect and build the production system: data pipelines, model integration, API layer, and UI. Milestones are visible. No black boxes.
Measure
We instrument everything — accuracy, latency, usage, and business impact. You know what the AI is doing and whether it's working. Then we iterate.
Relevant Services
Frequently Asked Questions
How do you handle sensitive financial data?+
We build with data minimisation as a first principle — models train on what they need, nothing more. For most fintech use cases, we work within your existing cloud infrastructure (AWS, GCP, Azure) so customer data never leaves your environment. We can also work with anonymised or synthetic data for model development and switch to production data only for final validation.
Is your AI compliant with RBI and SEBI guidelines?+
We're not your compliance team, and we'll never pretend otherwise. What we do is build systems with auditability baked in — every model decision is loggable, explainable, and traceable. We design for the controls your compliance team will need, and we work alongside your legal and risk teams to make sure what we build passes their review.
Can we deploy AI models on our own infrastructure?+
Yes, and for most regulated fintech deployments, that's what we'd recommend. We build systems that run fully within your cloud environment — no data leaves your VPC. We use open-source models where possible and avoid hard dependencies on third-party AI APIs for anything that touches sensitive data.
How do you prevent AI from introducing new fraud vectors?+
Good question to ask. AI systems can be gamed if they're not built defensively. We include adversarial testing in our development process — actively trying to fool the models the same way bad actors would. We also build monitoring pipelines that flag model drift and performance degradation before they become exploitable.
// other industries
Fraud Doesn't Wait. Neither Should Your Detection.
Tell us which compliance or risk problem is costing you the most right now. We'll scope a build that fits your regulatory constraints.
Get in Touch