AI OCR for gas station operations
- 20K+
- transactions processed in a single day
Fraud Detection Software | Real-Time ML Scoring
Fraud Detection Software
RaftLabs builds custom fraud detection models for financial transactions, insurance claims, account takeover, and e-commerce. Real-time scoring pipelines that evaluate risk at the moment a transaction or event occurs, with the false-positive rate management that keeps legitimate customers from being blocked and your fraud team from drowning in alerts.
We design the detection system around your specific fraud patterns and risk tolerance. Not every business needs the same balance between catch rate and false positives, a payment processor and an insurance company have very different consequences for each type of error. We define that threshold with you before building.
Real-time transaction scoring with sub-200ms latency at production volume
False-positive rate management, legitimate customers are not caught in the net
Rule engine alongside ML models, explainable decisions for regulatory and analyst review
Fraud pattern monitoring so the model adapts as fraud tactics evolve
Recent outcomes
Voice AI · Research
6× deeper insights
Text-based interviews converted to automated phone calls
AI Automation · Ops
20k+ txns day one
Manual invoice OCR across 40+ gas stations
Loyalty · Retail
1,062 users in 4 weeks
SuperValu & Centra loyalty platform with receipt validation
SaaS · Logistics
2,000+ shipments yr 1
Multi-carrier shipping hub for Indonesian eCommerce
4.9
on ClutchThe problem
Sound familiar?
Fraud is slipping through because your rule-based system cannot keep up with the patterns that keep changing?
You are blocking too many legitimate transactions and your fraud team cannot review the volume of alerts you are generating?
Short answer
RaftLabs builds real-time and batch fraud detection models for financial transactions, insurance claims, and account takeover. We manage false-positive rates to avoid blocking legitimate customers, combine ML models with explainable rule engines, and add drift monitoring so the system adapts as fraud tactics change. A first model covering one fraud type, with a real-time scoring API, starts around $25,000 to $70,000 and grows into a multi-type platform from there.
Key takeaways
- Real-time transaction scoring pipelines operate at sub-200ms latency at production volume.
- False-positive rate management is built in so legitimate customers are not blocked by the fraud net.
- ML models are combined with explainable rule engines to support regulatory and analyst review.
- Fraud pattern monitoring is included so the system adapts as fraud tactics evolve.
- A first fraud model covering one fraud type, with a real-time scoring API, starts at $25,000 to $70,000 to get a validated v1 into production.
- From there it grows into a multi-type platform, typically $70,000 to $200,000 as coverage, case tooling, and high-availability infrastructure expand.
Trusted by
Rule-based systems are a fixed target. Fraud rings study what gets blocked and adapt: new card patterns, new account structures, new device fingerprints, until they find the gaps. The rules grow more complex with every incident. The false-positive rate climbs, and legitimate customers start seeing their transactions declined. Anything that does not match an existing rule passes straight through.
Machine learning takes a different approach. It learns the statistical signals that separate fraud from legitimate behaviour, including signals no analyst has ever written as a rule. That makes it adaptive, but it introduces two new risks. A model tuned too aggressively blocks real customers and costs you revenue. A model that explains itself only as a probability score is hard for your team to review and for regulators to scrutinise. RaftLabs builds systems that pair ML scoring with an explainable rule engine, calibrated to the false-positive rate your business can absorb.
The cost of weak detection is not only the fraud that slips through. It is the legitimate revenue you decline, the analyst hours you burn, and the losses you rarely recover.
- $12.5B
- Reported US consumer fraud losses in 2024, up 25% year over year
- FTC, 2025
- $4.41
- Total cost to US financial firms for every $1 lost to fraud
- LexisNexis Risk Solutions, True Cost of Fraud
The FTC figure counts only reported losses; actual losses run higher. And for a financial firm, each dollar of fraud drags several more behind it in fees, labour, and replacement costs. That is why detection pays for itself well before it shows up in the numbers on a fraud dashboard.
Capabilities
What we build
- 01Transaction fraud scoring in real time
Real-time ML scoring that evaluates transaction risk within 200 milliseconds, fast enough to inform an accept-or-decline decision at checkout. Gradient-boosted models weigh transaction context, customer velocity, and device signals, and handle the extreme class imbalance typical of fraud data, where genuine fraud is often well under 1% of events. Each score returns with its top contributing features ranked by SHAP value. Your team sees why an event was flagged, not just a number.
- Built with
- XGBoost · LightGBM · SHAP
- 02Account takeover detection
Behavioural anomaly detection that catches the gap between an account holder's normal access pattern and an attacker using a stolen credential. Each sign-in is scored on device fingerprint, IP geolocation, and deviation from the account's 90-day baseline. Credential stuffing is caught through velocity and reuse checks. A high-value operation right after sign-in triggers step-up authentication, even when the sign-in itself scored clean.
- 03Insurance claims fraud detection
Fraud scoring models for insurance claims that separate legitimate claims from staged incidents and organised rings, calibrated to your lines of business rather than generic transaction data. Features cover claim timing, claimant history, and provider patterns, and graph analysis surfaces fraud rings sharing the same repair shop or legal representative, so low-risk claims auto-approve, medium-risk queue for adjuster review, and high-risk route to your Special Investigations Unit.
- Built with
- Neo4j · AWS Neptune
- 04False positive rate management
Threshold calibration that makes the catch-rate versus false-positive trade-off an operational control, not a fixed model parameter. Precision-recall curves show the full range of operating points before any threshold is set. Thresholds vary by customer segment, channel, and transaction category, and a medium-risk score triggers a step-up challenge instead of a hard block. A monthly review then identifies the score bands safe to adjust.
- 05Rule engine alongside ML models
A configurable rule engine runs in parallel with the ML model, so your fraud team can block a new pattern within hours instead of waiting weeks for a retrain cycle. Rules are managed through an admin UI with second-analyst approval. A simulation mode tests each new rule against the prior 7 days of transactions before it goes live. Each rule edit and block decision is written to an immutable audit log for regulators and dispute resolution.
- 06Fraud investigation tooling for analysts
A case management interface where flagged transactions queue for analyst review, with the full context needed for a confident call in under 3 minutes: the risk score and its SHAP explanation, 90-day transaction history, device timeline, and similar confirmed cases. One-click controls approve, block and notify, escalate, or request verification. Every decision is logged with analyst ID, timestamp, and reason code, the audit trail regulators expect.
- Built with
- SHAP
How we choose the modeling approach
There is no single fraud model. The right method depends on how much labeled fraud you already have, how fast tactics shift, and how rare fraud is in your traffic. We decide this with you before writing code, then combine methods rather than betting on one.
- Supervised models
- When you have a labeled history of confirmed fraud, gradient-boosted models learn the boundary between fraud and legitimate activity and score every new event against it. This is the workhorse for card and payment fraud, where labels are plentiful and the patterns are well understood.
- Anomaly detection
- When fraud is new or labels are scarce, unsupervised models flag events that deviate from a customer's normal behaviour without being told in advance what fraud looks like. This covers the cold-start period on a new product and the novel attacks a supervised model has never seen.
- Class imbalance
- Fraud is often well under 1% of transactions, so a model can hit 99% accuracy by approving everything and catching nothing. We train against precision and recall on the fraud class, using resampling and class weighting, so the rare fraud case is what the model is measured on, not raw accuracy.
- Feedback loop and drift
- Every analyst decision and chargeback becomes a fresh label. A scheduled retraining pipeline folds those labels back in, and distribution monitoring flags when live traffic drifts away from the training data, so catch rate is corrected before it quietly degrades.
Fraud that slips through now will cost more to recover than detection costs to build.
Tell us your current fraud rate, transaction volume, and the types of fraud you are most exposed to. We will scope a detection system calibrated to your risk tolerance and review team capacity.
Related predictive analytics services
Predictive Analytics, overview of our full predictive analytics practice
Demand Forecasting, time-series ML models for inventory and procurement planning
Churn Prediction, customer churn risk models integrated with your CRM
Predictive Maintenance, equipment failure prediction from sensor data
What clients say
What our clients say
Three-year average engagement. Founders and operators describing the work in their own words. No marketing varnish.
Charles E.
USAEntrepreneur at Aggie Technologies
All of the sprints were completed on schedule and on budget. We highly recommend RaftLabs!
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Read moreFrequently asked questions
This is the central design decision in any fraud detection system. A model optimised purely for catch rate, blocking every potential fraud, will also block a significant fraction of legitimate transactions, damaging customer experience and generating analyst review volume your team cannot handle. A model optimised to avoid false positives will let fraud through. The right balance depends on your business: the cost of a missed fraud event vs the cost of a declined legitimate customer, and the capacity of your fraud review team. We work with you to define the operating point before building, and we build in the tools to adjust that balance as your business priorities and fraud patterns change.
The most predictive features vary by fraud type but consistently useful signals include: transaction velocity and pattern deviation from the customer's historical baseline, device and network fingerprinting (IP geolocation, device ID, browser characteristics), time and behavioural patterns (hour of day, transaction frequency bursts), merchant category and transaction amount relative to account history, and for account takeover specifically, login behaviour, password reset activity, and session characteristics. Feature engineering for fraud detection requires domain knowledge about the specific fraud patterns you are defending against, which is why we start every engagement by reviewing your historical fraud cases.
Fraud patterns change deliberately, fraud actors observe what gets blocked and adapt their tactics. A static model trained on historical fraud data will see its accuracy degrade as fraud patterns shift away from what it was trained on. We address this with two mechanisms: a rule engine that lets your fraud team add explicit blocking rules for new patterns without waiting for a model retrain, and a scheduled retraining pipeline that incorporates new confirmed fraud labels on a regular cadence. We also implement distribution monitoring that flags when incoming transaction patterns are drifting from the training distribution, so model degradation is visible before it becomes significant.
We work land-and-expand. A first model covering one fraud type, with a real-time scoring API, a rule engine, and a basic case management interface for your fraud team, starts at $25,000 to $70,000. That is the validated v1 you put into production, then grow. From there a multi-type platform, with high-availability infrastructure, advanced case tooling, regulatory reporting, and automated retraining, typically runs $70,000 to $200,000 as coverage expands. We provide a fixed-cost quote after reviewing your transaction volume, fraud types, and latency requirements.
Work with us
Tell us what you need. We'll tell you what it would take.
We scope Fraud Detection Software in 30 minutes. You walk away with a clear cost, timeline, and approach. No commitment required.
- Scope and cost agreed before work starts. No surprises. No obligation.
- Working prototype within 3 weeks of kickoff.
- Pay by milestone. You see progress before each invoice.
- 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
- All conversations are NDA-protected.