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Insurance · Fraud & claims detection

Most fraud survives because it looks legitimate at first notice of loss

Banao builds fraud and claims detection AI that scores every incoming claim against historical patterns, network links, and behavioural signals — at intake, before investigation resource is committed.

The system surfaces the suspicious minority for your Special Investigations Unit and fast-tracks the clean majority toward settlement. Investigators close more cases on the files that matter; adjusters spend less time on the ones that do not.

What a Banao fraud detection deployment includes

Fraud detection that only runs on flagged claims is already too late. We build detection into the intake and triage layer, before investigation cost is incurred.

Claim scoring at first notice of loss

Every FNOL is scored against a multi-factor model — claim type, policy history, claimant behaviour, and network signals — the moment it enters the system. Investigators receive a prioritised queue, not a pile of files with no order.

Network and relationship graph analysis

Staged accidents, phantom providers, and organised fraud rings leave relationship signals across your claims data. We build graph models over claimant, provider, and adjuster networks to surface connections that document-level review misses.

Historical pattern matching on your own book

The model is trained on your own closed claims — confirmed fraud and legitimate settlements — not a generic insurance dataset. It learns the patterns specific to your book, your regions, and your fraud exposure.

SIU referral and workflow integration

Claims that breach a score threshold route automatically to your Special Investigations Unit with a reasons summary and supporting evidence. Adjusters receive a clear referral, not just a high score with no context.

Score monitoring and continuous retraining

Fraud patterns evolve. We monitor score distribution and referral outcome data week-on-week, retrain on confirmed outcomes, and alert you when the model needs updating — so detection does not degrade as fraud tactics shift.

Audit trail and regulatory reporting

Every fraud flag is logged with the evidence and the score rationale. Your compliance team has a defensible audit record; regulators can review the basis for any SIU referral without a manual case reconstruction.

Where this is running on live claims

Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified. Client details anonymised at client request.

A multi-line general insurer

Fraud scoring deployed at FNOL across motor and property claims

  • ··%claims auto-scored at intake
  • ··%reduction in investigator load on clean claims
  • ··%confirmed fraud rate in SIU referral queue

The insurer's investigators were reviewing claims by hand and catching fraud late — after settlement or deep into investigation. Banao built a scoring model trained on three years of closed motor and property claims and deployed it at the FNOL intake layer. High-score claims route to SIU on day one; clean claims proceed to the settlement queue.

We run the AI we sell on our own operation first

Banao operates a ~300-person engineering company on its own AI products. InterviewGod screens our own engineering hires every week. Vikaas runs our own demand-gen pipeline end to end.

An insurer's fraud exposure is higher-stakes than our internal operations — but the standard is the same: a model that has to perform inside a business we depend on is already hardened before it reaches your claims queue.

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

When fraud detection AI earns the least

Fraud models are only as good as the labelled outcomes behind them. We will tell you when the conditions are not right:

  • No labelled fraud history: if past claims were settled without a fraud or legitimate outcome recorded, week one is building that record — not training a model. Detection without confirmed labels will surface noise, not fraud.
  • Low claim volume: below a few hundred claims a month, a trained investigator and a well-configured rules engine will outperform a machine learning model on total fraud recovery. We will say so rather than quote a build.
  • Highly manual intake: if claims arrive by phone and paper with no structured FNOL data, the input layer needs work before a detection model can be trained. That is a different project from fraud AI.
  • Fraud patterns that shift weekly: organised rings adapt quickly. We design a continuous-learning cadence from the start — but if the retraining investment is not sustainable in your operation, we will flag it before you commit.

How we start — audit your closed claims before building a model

We do not quote a fraud detection build off a generic pitch. We look at your actual claims history first.

  1. AI Discovery Sprint2 weeks · fixed price

    We pull your closed claims and fraud-outcome records, test whether a detection signal exists in the data, and hand back a feasibility report with referral-rate and false-positive estimates — yours to keep. If you proceed, the Sprint cost credits against the build.

  2. Build

    Train the scoring model on your labelled claims history, integrate with your claims system at the FNOL layer, and wire the SIU referral workflow. Data pipeline and audit logging are part of the deliverable.

  3. Production & continuous learning

    Live deployment with a scored FNOL queue, SIU dashboard, outcome feedback loop, and a quarterly model review. Confirmed fraud and cleared claims both feed the model back so detection sharpens over time.

Frequently asked questions

Enough to represent your fraud patterns — typically a few hundred confirmed fraud cases across the claim types you want to score. The Discovery Sprint establishes whether your closed-claim record is sufficient and what augmentation or proxy labelling can bridge a gap.

Yes. Network and relationship graph analysis is one of the detection layers we build — linking claimants, providers, witnesses, and vehicles across claims over time. Ring structures that are invisible in single-claim review show up as graph patterns.

Banao integrates with your existing claims platform via API or database connection — including legacy core systems. The FNOL scoring layer sits upstream of your adjuster workflow, and referrals appear inside the tool your SIU already uses. Integration is part of the build deliverable.

Every score the model assigns comes with a reasons summary — which signals drove the flag, and how those signals compare to confirmed-fraud cases in the training data. Investigators see the evidence, not just a number, which is what drives adoption in practice.

False positives in fraud detection are expensive — in investigator time and in claimant relations. We set score thresholds collaboratively and monitor the confirmed-to-referred ratio every month. Where a threshold is producing too many false positives, we adjust before it erodes trust in the system.

Show us your most expensive fraud type

Bring your worst fraud exposure and your closed-claims history. In 45 minutes we will tell you whether the pattern is detectable in your data — and what a scoring model would take to build.

Book a 45-min scoping call