Fraud Detection Software Development
Rules-only fraud stacks have two structural problems: they generate high false positive rates because rules are blunt instruments, and they're reactive by design, you write a rule after you see a fraud pattern, so every new tactic gets through until you catch up. ML-based fraud detection changes the model, scoring transactions in milliseconds against patterns learned from your actual transaction data, so results reflect your customer base rather than a generic industry baseline.
ML models scoring every transaction at millisecond latency across velocity, device, geo, and behaviour signals
Account takeover detection with device fingerprinting, session analysis, and impossible travel detection
Synthetic identity and application fraud signals including SSN thin-file, address velocity, and network graph analysis
Configurable rules engine for ops teams with manual review queue, case notes, and SLA management
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The brief
Good software decisions begin with the constraint, not a list of features or a preferred technology.
Running a rules-based fraud stack that generates so many false positives your support team spends more time reversing declined transactions than blocking actual fraud?
Seeing rising chargeback rates but your current processor-provided fraud tools give you no visibility into why specific transactions were approved?
Plain answer
RaftLabs builds custom fraud detection software for fintechs, payment processors, and banks. Core components: real-time transaction scoring at millisecond latency, account takeover detection, synthetic identity checks, chargeback and dispute management, a configurable rules engine, and model monitoring with drift detection and retraining. We launch a validated v1 in 12 to 16 weeks at a fixed price, then expand it.
What to remember
A rules-only stack works one way: your analysts see a new tactic, write a rule, deploy it. Between the first fraudulent transaction and the rule that finally stops it, every copy of that tactic gets through. Meanwhile the rules already running are blunt, so they decline good customers by the thousand, and your support team spends its day reversing those declines instead of chasing fraud.
An ML model scores each transaction against patterns learned from your own data, in milliseconds, before it clears. It catches the variant of a tactic no one has written a rule for yet, and it routes the genuinely ambiguous ones to a human analyst rather than declining them outright.
The rule always ships on Friday. The model was already watching on Tuesday.
Because the model trains on your actual transaction data, it reflects the fraud patterns specific to your customer base, not a generic industry baseline. We build fraud detection infrastructure for payment processors, fintechs, and banks that need better tooling than their current processor provides. It scores every transaction in real time across velocity, device, geo, and behaviour signals, tuned against a target false positive rate under 1 percent.
RaftLabs has shipped production fintech software since 2015. On the payments side, we built a mobile point-of-sale and merchant-acquiring platform for a UAE fintech operator that processed over 10,000 transactions in its first three months and was later audited to PCI DSS. The team that scopes your transaction data is the team that builds the system. We assess the work, lock a fixed price in writing before development starts, and launch a validated v1 in 12 to 16 weeks, then expand it. Every system is built to defend chargebacks, not just block them, so each score carries a model explanation of its top contributing features, stored against the transaction record as representment evidence.
Everything on the left should already be true for your operation. Even one thing on the right, and a processor rules toggle or a manual review team is the smarter first step.
Enough transaction volume for a model to find patterns, with rising chargebacks or false positives now costing you real money.
6-12 months of labelled transaction history, or chargeback records we can backfill labels from before training.
You want visibility your processor's black-box fraud tools don't give you, into why each transaction scored the way it did.
Pre-launch, with no transaction history and no chargeback records to train a model on.
A processor-provided rules toggle already covers your fraud rate and you don't need custom scoring.
You need a live fraud decision this week, before a model can be scoped, trained, and calibrated.
What we build
Every wrongly declined transaction is a support ticket and a customer you may not get back. Tell us the number and we'll show you where a model would move it.
How it works
We map your transaction volume, current fraud rate, and available labelled history. You leave week 1 with a written scope document and a fixed-price quote.
Feature engineering and decision thresholds designed against your actual transaction data.
Scoring engine, rules interface, and case management built in parallel, tested against real transaction scenarios.
Fraud ops team trained on the review queue, with thresholds calibrated against live traffic.
Proof
Tell us your transaction volume, your current fraud rate, and your false positive tolerance. We'll scope a system and give you a fixed cost.
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Read moreRules fire on specific conditions you define and are easy to audit, but have fixed thresholds fraudsters can probe and generate high false positive rates. ML models learn patterns from historical transaction data and score hundreds of signals simultaneously. Most production systems combine both: ML provides the primary score, rules handle hard blocks that should never be overridden.
A three-bucket framework (approve, review, decline) routes ambiguous transactions to a human analyst rather than an automatic decline. Configurable thresholds per channel, merchant category, and customer risk tier, plus a feedback loop from analyst decisions, improve precision over time.
Yes. The fraud scoring layer sits between transaction intake and your processor's authorisation request. If your processor supports pre-authorisation webhooks or a decision API, integration is direct; for issuer-side fraud, we integrate with your card management system's event stream.
Labelled historical transaction data covering confirmed fraud and confirmed legitimate cases, typically 6-12 months minimum. If label coverage is low, we build a pipeline to backfill labels from chargeback records and fraud reports before training begins.
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Bring the rough workflow, half-built product, or messy brief. We will map the smallest useful first move, then send scope, timeline, and price in plain English.
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