Fraud Detection Software
A fraud score is useful only when the business knows what happens next. RaftLabs develops custom fraud detection software around one risk decision, with rules or models, reason codes, an analyst queue, feedback from confirmed outcomes, and monitoring. The first release is calibrated to your fraud policy rather than a generic accuracy claim.
Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.
The brief
Good software decisions begin with the constraint, not a list of features or a preferred technology.
Are missed fraud and false positives both rising while analysts review more alerts with less context?
Can your team reproduce why a transaction, claim, or account event was held or declined?
Plain answer
Fraud detection software scores transactions, claims, or account events against a defined risk policy. RaftLabs develops rules or models, reason codes, analyst review, outcome feedback, and monitoring. A focused first release for one fraud type starts at $25,000 and usually takes 10 to 16 weeks.
Lower the threshold and more risky events enter review. The queue grows. Legitimate customers wait. Analysts rush, and the extra alerts bury the cases that mattered.
The useful question is not whether a model is accurate. It is whether the full decision system reduces loss at a customer-friction and review cost the business accepts.
Adjacent transaction proof
RaftLabs does not currently publish a named fraud-model case study. The transaction figures show experience around payment and operating data; they do not prove fraud-model performance. The first phase must beat the buyer's historical baseline before any production claim is made.
A configured vendor platform is better when it already covers the fraud type, data, and evidence trail.
Confirmed outcomes and analyst decisions can be linked to historical events.
Risk owners can define review, hold, verification, approve, decline, and appeal authority.
The business measures missed fraud, legitimate events delayed, and analyst capacity.
There is no dependable feedback on which past events were fraudulent.
Automatic blocking is requested before review, escalation, and appeal policies exist.
A fraud platform already covers the risk type without costly workflow gaps.
Scope
| Configured platform | Custom software | |
|---|---|---|
| Best fit | Common fraud types and supported payment or claim flows | Proprietary events, evidence, or review policies |
| Signals | Vendor network and standard integration fields | Your event history and approved internal data |
| Decision policy | Configure available rules, scores, and workflows | Design exact thresholds, reasons, review, and escalation |
| Feedback | Return outcomes through the vendor contract | Control how analyst and confirmed outcomes update evaluation |
| First test | Trial on historical and shadow traffic | Prove one fraud decision against the current baseline |
We recommend the established platform when it fits the actual workflow. Custom work is justified only when the proprietary data or decision gap is valuable enough to own.
How it works
The score and the analyst workflow are tested against the same threshold.
Agree the fraud type, evidence, possible outcomes, thresholds, review capacity, appeal path, and owners.
Join event history with confirmed outcomes and analyst decisions. Inspect label quality, time leakage, missing data, and imbalance before training.
Compare candidates on held-out history. Let fraud operations inspect the resulting queue and test the customer-friction trade-off.
Integrate scoring and review, record reasons and versions, monitor outcomes, and put threshold or model changes through an approval path.
The mobile POS platform processed 10,000 transactions in its first three months and included signature-verified webhooks, queued processing, idempotent workers, and reconciliation. The gas-station operations platform processed more than 20,000 transactions in one test day across a rollout that connected 40+ locations during beta.
Both are adjacent transaction-system evidence. Neither is described as a delivered fraud model, catch-rate result, or universal scale promise.
Scope and price
Begin with one fraud type, one decision policy, historical validation, and one analyst review path.
Expand coverage or automation only after the first decision improves on the agreed historical baseline.
Starting investment
Starts at $25,000
A focused first release usually takes 10 to 16 weeks. Real-time decisions, new review tooling, and regulatory obligations move the estimate most.
Baseline before production
The phase compares proposed rules or models with the current decision process on held-out history. We do not invent an accuracy promise before reviewing the data.
Fixed-price phase
Once the data, decision policy, acceptance measures, integration, and handover are agreed, the phase price is locked in writing.
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Choose the operating threshold from business costs and review capacity, not raw model accuracy. Measure confirmed fraud caught, legitimate events delayed or declined, analyst volume, customer appeal outcomes, and decision time. Different channels or customer segments may need different thresholds and review paths.
Rules fit known patterns that fraud teams must change quickly. Models can rank complex combinations of signals when reliable labels and enough history exist. Many systems use both, but a model should not be added until it improves the agreed decision beyond a rules baseline.
The first data set links the event being scored to the information available at that time, the eventual confirmed outcome, and any analyst decision. Useful fields depend on the fraud type. We check label quality, time leakage, missing values, class imbalance, and identity joins before training.
It can support automated outcomes where the business has approved the policy, evidence, monitoring, appeal process, and legal or regulatory review required for that context. Higher-impact or uncertain cases commonly route to step-up verification or a person rather than an automatic decline.
A focused first release for one fraud type starts around $25,000 and usually takes 10 to 16 weeks. Real-time scoring, several data sources, new analyst tooling, regulated decisions, high availability, and historical backfill increase the scope. The phase price is fixed after the data and decision audit.
Work with us
Bring one fraud type, recent confirmed cases, and the current review policy. We will scope the smallest measurable first release.