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?
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
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 include pattern monitoring so the system adapts as fraud tactics evolve.
Rule-based fraud detection is a fixed target. Fraud actors study what gets blocked and adjust -- new card patterns, new account structures, new device fingerprints -- until they find gaps in the rules. The rules grow more complex with every incident, the false-positive rate climbs, and your legitimate customers increasingly find their transactions declined. Meanwhile, the fraud that does not match any existing rule passes straight through.
Machine learning fraud detection learns the statistical patterns that distinguish fraud from legitimate behaviour, including patterns that no analyst has explicitly defined as a rule. That makes it more adaptive. But it also creates a new problem: a model that blocks too many legitimate transactions costs you customers, and a model that explains its decisions only as a probability score is difficult for your fraud team to review and for regulators to scrutinise. RaftLabs builds fraud detection systems that combine ML scoring with explainable rule engines, calibrated to the false-positive rate your business can tolerate.
What we build
Transaction fraud scoring in real time
Real-time ML scoring pipelines that evaluate transaction risk within 200 milliseconds -- fast enough to inform an accept/decline decision at checkout without adding noticeable latency. The scoring model evaluates transaction context, customer history, device signals, and velocity patterns simultaneously, returning a risk score and the key factors that drove it so your review team can act quickly on flagged transactions.
Account takeover detection
Behavioural models that detect account takeover attempts by identifying login patterns, session characteristics, and post-login behaviour that deviate from the account holder's historical baseline. Device fingerprint changes, geolocation anomalies, credential stuffing signatures, and unusual post-login activity -- detected before the attacker can complete a fraudulent transaction or data extraction.
Insurance claims fraud detection
Fraud scoring models for insurance claims that evaluate claim characteristics, claimant history, provider patterns, and network relationships to identify claims warranting investigation. Batch scoring for processing-time review and real-time scoring for straight-through processing decisions. Social network analysis to surface organised fraud rings where multiple claimants, providers, or accounts are connected.
False positive rate management
Threshold calibration tools that let your fraud team adjust the model operating point as fraud patterns and business priorities change -- tightening detection during high-fraud periods and relaxing it when false-positive volume is damaging customer experience. Segmented thresholds by customer type, channel, or transaction category so high-value customers are not subject to the same friction as anonymous transactions.
Rule engine alongside ML models
A configurable rule engine that sits alongside the ML model, allowing your fraud team to add explicit blocking rules for newly identified fraud patterns without waiting for a model retrain. Rules are versioned and auditable so regulatory review is straightforward. Decision logic is exportable in human-readable format for compliance documentation and analyst training purposes.
Fraud investigation tooling for analysts
A case management interface where flagged transactions and accounts are queued for analyst review, with the full context needed to make a quick, informed decision: transaction history, device timeline, model score explanation, similar confirmed fraud cases, and a one-click action interface for approving, blocking, or escalating. Review throughput metrics and queue management so your fraud operations team can manage workload without losing cases.
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.
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Frequently 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.
An initial fraud detection model -- covering one fraud type, with real-time scoring API, a rule engine, and a basic case management interface for your fraud team -- typically runs $25,000 to $70,000. A full real-time scoring system covering multiple fraud types, with high-availability infrastructure, advanced case management tooling, regulatory reporting, and automated model retraining ranges from $70,000 to $200,000. We provide a fixed-cost quote after reviewing your transaction volume, fraud types, and latency requirements.