Banking, Payments & Credit
Chargeback Prediction API
By the time a chargeback notification arrives, the goods have shipped, the fee is charged and your win rate on representment is whatever it is. The useful moment was weeks earlier.
- Payment service providers
- Marketplaces
- Subscription businesses
- Acquirers
- Merchants of record
What your customers would see
example interfaceMockupIllustrative mockup of the interface we would build. Figures shown are examples, not results from a live deployment.
The problem this solves
Disputes arrive late and cost more than their face value. There is the transaction amount, the scheme fee, the operational time to assemble evidence, and — for merchants near a monitoring threshold — the much larger risk of programme placement with its fines and remediation requirements. A chargeback rate that looks like a rounding error on a revenue statement can be an existential problem in a scheme compliance meeting.
The mix matters more than the total. True fraud disputes, friendly fraud where the cardholder genuinely made the purchase and disputes it anyway, and service disputes driven by delivery failures or unclear billing descriptors all look identical in a chargeback report and require entirely different responses. Lumping them together is why most dispute reduction programmes stall.
Prediction is genuinely feasible here, with one important caveat: the signal is not all at authorisation time. A subscription dispute often becomes likely the moment a customer’s card is charged after a long dormancy, or after a support ticket goes unanswered, or when the descriptor on the statement does not resemble the brand they bought from. That means scoring at authorisation and re-scoring as post-transaction events accumulate.
Who buys this
Payment service providers, Marketplaces, Subscription businesses, Acquirers, Merchants of record
What the API returns
What comes back
- Dispute probability at authorisation, with the reasons driving it
- Predicted dispute category — fraud, friendly fraud, service or processing error
- Post-transaction re-scoring as delivery, refund and support events arrive
- Recommended prevention action per case, with its expected cost and effect
- Representment win likelihood, so you can decide which disputes are worth fighting
- Evidence checklist per dispute reason code, assembled from the records you hold
- Portfolio dispute-rate forecasting against scheme monitoring thresholds
- Merchant and descriptor level analysis for platforms carrying many sub-merchants
Endpoints
| Method | Path | Returns |
|---|---|---|
| POST | /v1/disputes/score | Dispute risk for a transaction at authorisation time |
| POST | /v1/disputes/rescore | Update the score as post-transaction events occur |
| GET | /v1/transactions/{id}/prevention | Recommended prevention actions and expected effect |
| POST | /v1/disputes/representment | Win likelihood and the evidence to assemble |
| GET | /v1/portfolio/forecast | Projected dispute rate against scheme thresholds |
| POST | /v1/outcomes | Submit dispute outcomes so calibration stays current |
Request and response
illustrativeRequest
{
"transaction_id": "txn_7741092",
"amount": { "value": 189.00, "currency": "USD" },
"customer": { "id": "cust_22841", "tenure_days": 620, "prior_disputes": 0 },
"subscription": { "is_recurring": true, "cycle": 14, "days_since_last_login": 97 },
"descriptor": "YCO*APPNAME",
"fulfilment": { "type": "digital", "delivered": true },
"events": ["support_ticket_open_6d"]
}Response
{
"transaction_id": "txn_7741092",
"dispute_probability": 0.074,
"band": "elevated",
"portfolio_baseline": 0.008,
"predicted_category": "friendly_fraud",
"category_distribution": {
"friendly_fraud": 0.61, "service": 0.27, "fraud": 0.08, "processing": 0.04
},
"drivers": [
{ "code": "DORMANT_THEN_BILLED", "weight": 0.34,
"detail": "97 days since last login, billed on cycle 14" },
{ "code": "DESCRIPTOR_UNRECOGNISABLE", "weight": 0.21,
"detail": "Descriptor does not contain the brand name shown at checkout" },
{ "code": "OPEN_SUPPORT_TICKET", "weight": 0.17,
"detail": "Ticket open 6 days without first response" }
],
"recommended_actions": [
{ "action": "send_prebilling_notice", "expected_reduction": 0.021, "cost": 0.00 },
{ "action": "resolve_support_ticket", "expected_reduction": 0.019, "cost": 0.00 },
{ "action": "proactive_refund", "expected_reduction": 0.058, "cost": 189.00 }
],
"representment_win_likelihood": 0.29,
"scored_at": "2026-10-21T10:18:00Z"
}Where the data comes from
| Your dispute history with outcomes | Chargebacks with reason codes, representment results and final liability — the label the model is trained on, and nothing substitutes for it |
| Your authorisation and settlement records | The transaction features: amount, channel, authentication, instrument, descriptor and timing |
| Fulfilment and delivery events | Shipment, delivery confirmation, digital access logs and refund records, which separate service disputes from fraud |
| Customer support and engagement data | Ticket timing, response latency and product usage, which carry much of the friendly-fraud signal |
| Scheme reason code definitions and monitoring rules | Published scheme documentation, which governs both evidence requirements and the thresholds you are measured against |
| Issuer dispute-prevention network data | Scheme and network alert services where you subscribe to them; licence terms decide whether alerts can be stored or only acted on |
The intelligence layer
The difference between a demo and a service is mostly in this section. These are the parts that keep the answer trustworthy once real traffic is hitting it.
- Separate models per dispute category, because the features that predict a stolen-card dispute have almost nothing in common with those that predict a subscription complaint
- Survival modelling on time-to-dispute rather than a flat classifier, since the dispute window runs for months and a transaction’s risk is not static
- Event-driven re-scoring triggered by delivery failures, refunds, support contact and dormant-then-billed patterns
- Expected-value framing on every recommended action: a proactive refund that prevents a dispute still costs the transaction, and the API shows that trade explicitly
- Representment win modelling fitted on your own historical outcomes by reason code and evidence completeness
- Calibration monitoring on predicted versus realised dispute rates, reported monthly, because a miscalibrated probability makes the expected-value arithmetic useless
- Portfolio threshold forecasting with uncertainty bands, so you see a monitoring breach coming with weeks of notice rather than days
- Descriptor and sub-merchant level attribution for platforms, which is usually where concentrated dispute risk actually sits
What it changes for the business
Prevented disputes
Prevention actions taken before a dispute is filed avoid the fee, the operational cost and the ratio impact entirely.
Better representment selection
Win likelihood lets your team spend evidence-gathering effort on the disputes that can actually be won.
Threshold protection
Portfolio forecasting against scheme monitoring programmes converts a compliance surprise into a managed number.
Cleaner root causes
Category prediction separates fraud from product and billing problems, which usually sends the fix to a different team.
How it fits into your product
How it embeds
- Scored synchronously at authorisation alongside your fraud check, with a shared latency budget
- Asynchronous re-scoring driven by webhooks from your fulfilment, refund and support systems
- Prevention actions pushed into your existing messaging, refund and support workflows rather than a separate console
- Representment evidence checklists rendered into your dispute management tooling
- Sub-merchant segmentation for platforms, with per-merchant thresholds and alerting
- Deployment inside your infrastructure, which is normal given the breadth of customer data involved
Included in every build
- Authentication, scoped API keys and per-customer rate limiting
- Input validation with predictable, documented error responses
- Versioned endpoints so your integration never breaks without notice
- Request and response logging for audit and dispute resolution
- Latency, error-rate and usage monitoring with alerting
- OpenAPI specification, sandbox environment and client examples
- Backups, failover design and a documented recovery procedure
- An agreed support arrangement once the service is live
How we would build it
about 20 weeksDiscovery and feasibility
Weeks 1–3
This one depends heavily on data access, so we start by proving the sources exist, can be licensed for your use, and actually carry the signal the model needs. If they do not, you find out here.
Architecture and baseline model
Weeks 4–6
A written architecture and a baseline model measured against a holdout period, so you know what accuracy you are buying before the full build starts.
Production build
Weeks 7–16
Data pipelines, model training and serving, historical archive, validation harness, API, security, monitoring and documentation.
Validation and integration
Weeks 17–20
Backtesting against held-out history, accuracy reporting your team can audit, integration support and an agreed retraining cadence.
Commercial models
There is no fixed price list, because the cost is driven by data complexity, integration count, model requirements, request volume and the support level you need. Scope is agreed in writing before development starts.
Fixed-scope prototype
A defined slice of the service, built and priced as one piece of work. The usual first step when the business case needs proving internally.
Fixed-price build
An agreed scope against a written architecture, delivered for an agreed price. Best when the requirements are already clear.
Build then retainer
We build the service, then operate and improve it under a monthly agreement covering monitoring, updates and model maintenance.
White-label product
The service runs under your brand, your domain and your pricing, with MLPivot invisible to your customers.
Questions buyers ask
Can you stop chargebacks?
No. A cardholder can dispute a transaction regardless of what you do, and some disputes are entirely legitimate. What prediction changes is which transactions get a prevention action, which disputes you contest, and how early you see a portfolio problem. Framing it as prevention of all disputes would be dishonest.
How much dispute history do you need?
Enough disputes, not enough transactions — that distinction matters, because disputes are rare. As a rough guide, several hundred resolved disputes with reason codes and outcomes gives a usable model; a few thousand gives a good one. Below that we would build the event-driven prevention logic, which does not need training data, and add the model as outcomes accumulate.
Is a proactive refund not just giving money away?
Sometimes, yes, and the API says so. A refund costs the full transaction value to avoid a dispute that was maybe seven per cent likely. That arithmetic only works for high-value transactions, customers near a dispute threshold, or merchants close to scheme monitoring. The expected-value output exists precisely so this is a visible decision rather than a default.
Does this handle friendly fraud?
It predicts it, which is the useful part, but it cannot resolve the underlying asymmetry — representment win rates on friendly fraud disputes are poor in most reason codes because the evidence standard favours the cardholder. Most of the value is in prevention: recognisable descriptors, pre-billing notices and responsive support.
Will the predictions stay accurate?
Only with maintained outcome feedback and periodic retraining, which we would write into the support arrangement. Dispute behaviour shifts with scheme rule changes and issuer policy, and a model trained on last year’s rules drifts quietly. We report calibration monthly so the drift is visible rather than assumed absent.
Related blueprints
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Transaction Fraud Scoring API
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Business Verification API
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Want this built for your platform?
Tell us what your product does today and what you want another system to be able to ask it. We will come back with an approach, a scope and a price.