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Financial Services · Fraud detection

Your rules engine catches the fraud you already know about

Banao builds ML fraud detection that closes the gap your static rule set leaves open — real-time transaction scoring, account takeover signals, and synthetic identity screens, wired into your core and returning a decision in milliseconds.

Every call carries a reason code your investigator can read and your regulator can audit. The model is not a black box delivered once; it is a production system that improves each month as your actual fraud history feeds back in.

What a Banao fraud detection deployment covers

Rules alone leave a gap. A model alone is a black box your risk team cannot defend. A Banao deployment is both — with the audit trail and explainability your compliance team signs off on.

Real-time transaction scoring

Each transaction receives a fraud probability score before the response returns to the customer. The model evaluates velocity, device fingerprint, location drift, and behavioural signals that a rules engine cannot encode.

Account takeover detection

Login events, password resets, and session changes scored against device history, geolocation drift, and prior behaviour — so account takeover is flagged before a transfer clears, not after the chargeback arrives.

Synthetic identity screening

Cross-referencing document signals, bureau patterns, and onboarding behaviour to surface identities assembled from real data fragments — the fraud class most rules sets miss entirely because no single signal is wrong.

Case management and investigator workflow

A queue built for your risk analysts: priority-ranked, reason-coded, with one-click access to the transaction graph and the model's evidence chain. No pivot tables, no manual lookups before a decision.

Explainability layer and audit log

Every decision — approve, flag, decline — carries a human-readable reason and a timestamped audit entry. The trail exists from day one, not added retroactively after a regulator asks for it.

Continuous model feedback

Confirmed fraud, investigator overrides, and chargebacks feed back into the model automatically each month. Fraud patterns shift; a model that does not adapt drifts toward the same gap your rules engine already misses.

Where ML fraud detection is already running

Metrics shown dotted (··) are being finalised — we will not publish a number before it is verified. Clients described without naming are bound by contract.

A regulated payments platform

ML scoring layered on top of an existing rules engine

  • ··%additional fraud caught above rules baseline
  • ··%false positive rate reduction
  • ··msaverage scoring latency

A high-volume payments platform had a mature rules engine that caught known fraud well and new-pattern fraud poorly. Banao added an ML scoring layer evaluating velocity, device, and behavioural signals in real time, with a reason code on every flag so the risk team could trust and act on the output from day one.

We run our own company on the AI we sell

Banao operates a ~300-person engineering company on its own AI products before they reach a client. InterviewGod screens our own hires; Vikaas runs our own demand generation pipeline.

For fraud detection specifically, that means our internal systems already carry the audit logs, access controls, and explainability layers we build for clients — because our own security and finance teams required them first.

  • InterviewGodScreens Banao's own engineering hires every week.
  • VikaasRuns Banao's own demand-gen pipeline end to end.

When an ML fraud model is not the right investment

Not every fraud problem is a machine learning problem. We will tell you which kind you have before you commit budget:

  • Low transaction volume: below a few hundred thousand transactions a month, a tighter rules engine and a fraud analyst cost less than an ML pipeline to build and maintain. We'll say so.
  • No labelled fraud history: a supervised model needs confirmed fraud examples. If your chargeback data is thin or unlabelled, the Discovery Sprint is an instrumentation project first and a modelling project second.
  • A policy gap, not a data gap: if the real blocker is a missing dispute process or an under-resourced investigation team, software will not close it. We'll point you back to the cheaper fix.

How we start — know the gap before you build the model

We do not quote a fraud model off a brief. We look at your actual transaction data, your current rules, and your confirmed fraud labels first.

  1. AI Discovery Sprint2 weeks · fixed price

    We audit your transaction data, your current rules engine, and your labelled fraud history. You walk out with a gap analysis — what rules miss and why — an ML feasibility assessment, and ROI maths in basis points, yours to keep either way. If you proceed, the Sprint cost is credited against the build.

  2. Build

    Data pipeline and feature engineering first, then the model, with your CISO's architecture sign-off as the first deliverable. We integrate with your core — real-time or near-real-time — and the explainability and audit layers are built in from the start, not bolted on at the end.

  3. Production & continuous learning

    Deployment with a case management queue for your investigators and a monitoring dashboard for your risk team. Confirmed fraud and investigator overrides feed back into training every month — so the model improves as your fraud patterns evolve.

Frequently asked questions

Yes — and that is usually the right architecture. Your rules engine encodes known-good and known-bad patterns cheaply; the ML layer closes the gap for novel patterns. We combine both signals into a single decision with a merged reason code, so your investigators see one output, not two competing systems.

Typically under 50 milliseconds for a single transaction score. We benchmark against your payment or core-banking SLA before the build starts, and the scoring service is sized to your peak TPS — not average TPS — so it holds under load.

By treating false positives as a first-class metric alongside missed fraud during the build. We tune to your acceptable false-positive rate, build analyst override into the case queue, and feed investigator corrections back into training — so the false-positive rate improves over time rather than stagnating at launch accuracy.

Yes, by design. Every decision carries a human-readable reason code and a timestamped audit log. We work with your compliance team during the build to match the explanation format your regulator expects — not a generic output retrofitted to a checklist.

Typically 8–12 weeks from data-pipeline sign-off to production, depending on data quality and integration complexity. The Discovery Sprint pins down the actual timeline for your data — so the build starts with a realistic scope, not an optimistic estimate.

Find out what your rules engine is missing

Bring your fraud loss numbers and your current rules count. In 45 minutes we will tell you whether ML scoring would close the gap — and what your data needs to make it work.

Book a 45-min scoping call