Retail & E-commerce · Returns fraud detection
One in ten returns is fraud — and your refund policy cannot tell which
Banao builds ML-based returns fraud detection that scores every return transaction at submission time — flagging policy abuse, item swaps, and serial-return patterns before the credit hits your ledger.
The model works across your existing OMS and returns portal data. It does not require new hardware or a warehouse audit team; it reads the signals already in your transaction history and surfaces the cases worth a human review.
The first call is free · 45 minutes · no obligation
What we build
What a Banao returns fraud deployment includes
Fraud detection is not a single score. It is the transaction model, the identity layer, the analyst queue, and the decision logic — we build all four.
Return transaction scoring at submission
Every return request is scored in real time against order history, item attributes, return channel, and account behaviour — before the refund is approved or a label is issued.
Policy-abuse pattern detection
The model flags wardrobing, bracketing, and receipt-reuse patterns by tracking return frequency, category clustering, and timing gaps that manual policy rules consistently miss.
Cross-channel identity resolution
Fraud rings operate across multiple accounts, emails, and shipping addresses. The identity graph links those signals so a serial returner cannot reset by opening a new account.
Item-verification signal integration
Where warehouse scans or image checks supply item-condition data, the model incorporates those signals — flagging weight anomalies, tag-swap evidence, and description mismatches.
Analyst review queue with full case context
High-risk returns route to a structured queue with complete case history, contributing fraud signals, and a recommended action — so your team acts on evidence, not a raw number.
Continuous retraining on confirmed outcomes
Each confirmed fraud case and each overridden decision feeds back into the model. It adapts as return patterns shift instead of waiting for a manual rules update.
Dogfooding
We run AI on our own operation before we sell it
Banao operates a ~300-person engineering company on the same AI products we build for clients. InterviewGod screens our own engineering hires; Vikaas drives our own demand generation. A system that must survive our own operational pressure is not a prototype.
We apply the same standard to a returns fraud model: it must hold up under adversarial real-world input before it goes near a client's refund ledger.
Screens Banao's own engineering hires every week.
Runs Banao's own demand-gen pipeline end to end.
The honest version
When a fraud detection model is not the right call
Not every returns problem warrants an ML build. We will tell you before you invest:
- Low return volume: below a few thousand returns a month, tighter policy rules and manual review are faster and cheaper. We will say so.
- Shallow transaction history: if your OMS does not retain item-level detail or account history beyond 90 days, the model lacks the signal it needs — data retention comes before modelling.
- Undifferentiated SKUs: if every return looks identical in your system — no item attributes, no condition data — detection accuracy will be low and false positives will outpace caught fraud.
How we start
How we start — test on your real returns data first
We do not quote a fraud model against a benchmark dataset. We analyse your transaction history first.
- 01
AI Discovery Sprint
2 weeks · fixed price
We analyse a sample of your real returns data — including confirmed fraud and disputed cases — and hand back a detection-rate estimate, a false-positive risk assessment, and ROI maths across your return volume. Yours to keep. If you proceed, the Sprint cost is credited against the build.
- 02
Build
Train the transaction scoring model on your historical data, build the identity graph, integrate with your OMS and returns portal, and stand up the analyst review queue.
- 03
Production & refinement
Continuous retraining on confirmed outcomes, model monitoring against live return patterns, and threshold tuning as your fraud landscape shifts.
FAQ
Frequently asked questions
What return fraud patterns does the model catch?
Policy abuse (wardrobing, bracketing, receipt reuse), item swaps and false condition claims, serial-return behaviour across accounts, and cross-channel fraud rings. The Discovery Sprint maps which patterns are present and detectable in your specific transaction history.
How do we avoid blocking legitimate high-return customers?
The model scores risk — it does not auto-block. High-risk returns go to a human review queue with full context; your team makes the final call. Threshold calibration during the Sprint sets the false-positive rate you can operationally absorb before anything goes live.
What data does the model need to start?
Order and return history (item, SKU, channel, timestamps), account and address data, and a sample of confirmed fraud cases where available. Warehouse item-condition scans and image data improve accuracy but are not required to begin.
Does it integrate with our OMS and returns portal?
Yes. Banao integrates with Shopify, SAP, custom-built OMS, and third-party returns portals via API. The score is returned at the point a return is submitted, so your existing returns flow does not need to change for most integrations.
How long until the model is in production?
The Discovery Sprint takes two weeks and gives you a validated feasibility verdict. A standard build — model, identity graph, OMS integration, and review queue — typically runs 8–12 weeks depending on data readiness and integration complexity.
Get started
Find out what share of your returns is fraud
Bring 90 days of returns data and a sample of your confirmed fraud cases. In 45 minutes we will tell you what detection rate is realistic on your data — and what it would take to build it.
Book a Discovery Sprint