AI Fraud Detection Software Development Services
AI fraud detection that learns as fraud tactics evolve.
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
Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.
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The brief
Start with what is not working.
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 at a fixed price, then expand it.
What to remember
- A three-bucket decision framework (approve, review, decline) routes ambiguous transactions to a human analyst rather than an automatic decline, with every review-queue decision feeding back into the model as a labelled example.
- Every score includes a model explanation showing the top contributing features, stored against the transaction record for chargeback defence evidence packages.
- Feature drift monitoring and periodic retraining catch model degradation as fraud patterns shift, with automated alerts when accuracy drops below threshold.
- Building the model requires 6-12 months of labelled transaction history; if label coverage is thin, we build a pipeline to backfill from chargeback records before training starts.
A new fraud pattern hit on Tuesday. The rule to catch it shipped on Friday.
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.
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, 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, 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.
- reported US consumer fraud losses in 2023, the first year losses topped that level
- $10B+
- US Federal Trade Commission, 2024
The first decision is not what to build.
It is whether software is the right answer at all. We use four possible recommendations before discussing a development scope.
Buy
Run the build-vs-buy numbersChoose a maintained product when it handles the core workflow and the remaining gaps can be solved with configuration, a connector, or a small process change. Paying for software you do not have to maintain is often the better trade.
Repair
Keep the existing product when users still get value from it and the code can be understood safely. Start with evidence: where the code lives, how the data is organized, what is tested, which outside services it depends on, who can access what, and how updates go live.
Build
Own the system when the workflow shapes margin, customer experience, speed, or a product customers pay for. Start with one complete path and the system connections that path cannot work without.
Wait
Do not turn a changing policy into permanent software. Stabilise the rules, name the workflow owner, and measure the cost of the current process before paying for a product.
Not sure which path is yours? Bring it to a 30-minute call. Leave with the answer in writing.
ML fraud detection pays off when you have volume and labelled history.
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
What we build into a fraud detection system
Real-time transaction scoring
Feature engineering across velocity, device fingerprint, geolocation, and behavioural signals produces a fraud probability score with a model explanation attached for chargeback defence.
Account takeover detection
Login anomaly detection, device fingerprinting, and impossible travel checks flag credential stuffing and session hijacking before a fraudulent transaction is placed.
Application fraud and synthetic identity
SSN thin-file, address velocity, and network graph analysis surface coordinated fraud rings, with borderline applications queued for review rather than declined automatically.
Chargeback and dispute management
Pre-dispute alerts, integrated with networks like Verifi and Ethoca, catch cardholder disputes before they become chargebacks, with automated evidence package assembly for representment.
Rules engine and case management
Fraud ops teams create and deploy custom rules without engineering involvement, with a review queue, case notes, and SLA management built in.
Model monitoring and drift detection
Feature drift and performance dashboards track precision and recall by channel, with periodic retraining and automated alerts when accuracy drops below threshold.
Model governance and adverse-action compliance
Versioned models, decision audit logs, and reason codes on every decline support model risk governance. Where a score feeds a credit or account decision, the system records the specific factors behind it for your compliance team's review.
What is your current false positive rate costing you?
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
From scope to live fraud detection system
- Week 101
Transaction and data scoping
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.
- Weeks 2-502
Model and threshold design
We engineer features and design decision thresholds against your actual transaction data.
- Weeks 6-1303
Develop and integrate
We build the scoring engine, rules interface, and case management in parallel and test them against real transaction scenarios.
- Final 2-3 weeks04
Launch and model calibration
We train the fraud ops team on the review queue and calibrate thresholds against live traffic.
Proof
Adjacent payments work we've shipped
- 01
A PCI DSS-audited mobile POS and merchant-acquiring platform
Payments infrastructure for a UAE fintech operator, handling contactless cards, QR, tap-to-pay, and payment links, later audited to PCI DSS.- audited (2025)
- PCI DSS
- 02
Custom LMS development case study: building Sekou, a French-first school management platform for K-12 schools in French-speaking West Africa
A French-first school management platform and LMS for K-12 schools in West Africa. Attendance, marks, fees, and role-based portals for administrators, teachers, students, and parents, built by a six-person RaftLabs team over about six to seven months.- students supported per school
- 4000+
- people: 4 engineers, QA, and a PM
- 6
- built around Francophone school structures
- French-first
- 03
Over 15+ clinics see success by adopting RPM app, PDC with remote care in 2 months
The RPM app improves remote care with 50% faster response times (client-reported), driving rapid adoption by healthcare providers. It reads health data from wearable health monitoring devices like CGM, BPM, etc., securely storing and sending it to healthcare providers.- devices like CGM, BPM supported
- 4+
- clinics enrolled in 60 days
- 25+
- secure and HIPAA compliant
- 100%
Have a fraud detection project?
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.
What it costs
Every project starts at $10000.
The first phase covers one fraud path, like real-time transaction scoring, with evaluation and case management wired before expansion.
Starting investment
Starts at $10000
Start with the fraud type where false positives cost you the most. After a 30-minute call, you get a written build, buy, repair, or wait recommendation plus first-phase direction. Free, whether or not you build with us.
Work with us
Tell us where the work is stuck.
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.
- 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.
Common questions
Rules 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.
At a 0.1 percent fraud rate, a system that approves everything is 99.9 percent accurate and catches nothing. Judge on recall, precision, and review workload, not accuracy. When it reports 99 percent confidence, are 99 out of 100 of those actually fraud? That is the calibration question every vendor claim must face. Treat claims above 90 percent as unproven until tested on your data. Ask for onboarding pass-through rates, not detection rates. Detection identifies risk; prevention requires an intervention before the money moves.
The biggest cost driver is humans reviewing false positives. Cost scales more with alert volume than transaction volume because every alert needs a human review. The costliest line item is headcount, not the license. The total-cost frame: fraud-loss from missed fraud plus cost of blocking legitimate transactions plus latency impact. TCO equals integration plus manual review plus false declines plus fraud losses, not the license.
Rules break the moment tactics change. Ongoing maintenance is a permanent commitment, not a project. Ask about retraining cadence and who is responsible. Does the model train on network-wide data or only your own history? Does your data train models for competitors? A new fraud pattern hit on Tuesday. The rule to catch it shipped on Friday. That is the adaptation speed to verify.
Four rituals. One: the accuracy test. Recall and precision on your data, not vendor accuracy claims. Two: the failure-mode question. What breaks, and how did they deal with it? Three: the headcount question. What does the review team look like after go-live? Four: the pilot ritual. Proof of results on your own data, hands-on evaluation before signing. Rules-based versus ML-based: rules are explainable and brittle; ML adapts and needs monitoring. Real-time scoring, account takeover detection, synthetic identity, rules engine and case management, drift detection.
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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