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Government · Fraud & waste detection

Fraudulent claims and wasted spend don't announce themselves

Ghost beneficiaries, duplicate contractor registrations, and outlier line items in procurement budgets accumulate quietly — flagged only when an audit or a tip arrives, months or years after the money has gone.

Banao builds anomaly-detection models over your beneficiary registers, payment records, and procurement data that surface the patterns worth investigating, before an external audit does it first.

What a Banao fraud-detection deployment covers

Public-sector leakage tends to cluster in a handful of places. We target the three or four with the largest provable cost first.

Duplicate and ghost beneficiary detection

Cross-referencing beneficiary records against death registers, national ID databases, and multiple-registration signals surfaces ghost entries and duplicates before the next disbursement cycle.

Procurement anomaly scoring

Models trained on your own historical bid and vendor data flag single-bid awards, price outliers, and split-purchase patterns — the signatures of inflated contracts and quota manipulation.

Payment pattern analysis

Unusual payment timing, round-number clusters, and vendor concentration ratios are scored against baseline norms so investigators reach the outlier rather than the routine.

Contractor and vendor deduplication

Shell and related-party registrations often use slight name variations, shared addresses, or transposed registration numbers. Entity-resolution models collapse these to a single vendor graph.

Threshold and splitting alerts

Purchases designed to stay just under approval thresholds appear as a pattern across the dataset even when each transaction looks ordinary. The model tracks the pattern, not the individual transaction.

Investigator dashboard and audit trail

Every flagged item is presented as a ranked queue with supporting evidence — the records that drove the score — so an investigator starts with context, not just a case number, and every decision is logged.

We run anomaly detection on our own operations first

Banao operates a ~300-person engineering company on its own AI before any government department sees the system. InterviewGod surfaces credential anomalies in hiring; Vikaas monitors our own demand-generation pipeline for irregular patterns. Anomaly detection that has to survive our own scrutiny is the version we hand to a public-sector client.

For a procurement officer or internal auditor considering AI, that provenance matters: these are not models built only for a tender. They have been under pressure in a live operation long before they reach your office.

  • InterviewGodFlags credential and response-pattern anomalies in Banao's own hiring pipeline.
  • VikaasMonitors Banao's demand-generation data for irregular patterns end to end.

When AI fraud detection is the wrong place to start

Anomaly detection is only as good as the data it runs on. We will tell you early if the conditions aren't right — before you fund a build:

  • Sparse or unstructured records: if payment and beneficiary data still live in disconnected paper registers, the first project is digitization and data consolidation. The model follows, not leads.
  • No investigation capacity: flagging anomalies without an investigator to follow them up creates a queue that becomes noise. We always ask about downstream capacity before scoping a detection layer.
  • Low transaction volume: below a certain payment or application throughput, statistical anomaly detection has too few samples to separate genuine outliers from noise. A rules-based audit is cheaper and more accurate at that scale.

How we start — cost the problem before committing public funds

Fraud and waste are rarely visible in aggregate reports. We begin by finding the number that makes the case, not by quoting a model.

  1. AI Discovery Sprint2 weeks · fixed price

    We audit a sample of your payment, beneficiary, and procurement data, identify the highest-signal anomaly categories, and produce a go/no-go for each — with an estimated recoverable value. The findings are yours to keep regardless of what comes next. Proceed, and the Sprint fee is credited against the build.

  2. Build

    Data pipeline and entity-resolution layer first, then the anomaly models, then the investigator interface. We integrate with your existing records systems and work within your data-residency and security requirements.

  3. Production & handover

    Live deployment with a human-in-the-loop step before any case is opened, a full audit trail on every flagged item, and investigator training. You are not handed a black box — you are handed a documented system your team controls.

Frequently asked questions

That is common. The Discovery Sprint maps which data sources carry enough signal to be worth connecting, and the build includes the extraction and normalisation layer. We work with legacy government ERP, financial management systems, and flat exports — the model sits above the integration layer.

That is the design constraint we plan around, not an afterthought. The output is a ranked queue with precision targets set during the Sprint, and the model is tuned to the investigator's actual workload. Volume of flags is a deliverable requirement, not an output we leave uncalibrated.

The model scores and ranks; it does not open a case. A named officer reviews every flagged item before any action is taken. The system records who reviewed, who approved, and what the outcome was — the audit trail is stronger than a manual process, not weaker.

Yes. We deploy on-premise or in a sovereign cloud and work within your data-residency and security rules. Payment and beneficiary data does not have to leave your environment.

The Sprint deliverable is a ranked opportunity list with estimated recoverable value and a go/no-go per anomaly category — the format is designed to support an internal business case or a formal procurement submission. It is yours to keep either way.

Find out what your payment data is hiding

Bring a description of your payment, beneficiary, or procurement data — volume, format, and what a recent audit flagged. In 45 minutes we'll tell you whether anomaly detection is worth building and what it would take.

Book a 45-min scoping call