AI Fraud Detection Software Development

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

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See our work

The problem

Sound familiar?

  • 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?

Short answer

RaftLabs builds AI fraud detection software for fintech companies, payment processors, banks, and e-commerce platforms. Core components include real-time transaction scoring at millisecond latency, account takeover detection with device fingerprinting and session behaviour analysis, synthetic identity and application fraud detection, chargeback and dispute management with pre-dispute alert integrations, a configurable rules engine with manual review case management, and model monitoring with drift detection and retraining pipelines. Delivery takes 12 to 16 weeks at a fixed cost.

Key takeaways

  • 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.

Trusted by

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Aldi logo
Nike logo
Microsoft logo
Heineken logo
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Energia Rewards logo

Fraud detection delivery, by the numbers

score latency
Real-time
false positive target
<1%
cost delivery
Fixed
week delivery
12-16

Fraud detection that learns as fraud tactics evolve

Because models train on your actual transaction data, they reflect the fraud patterns specific to your customer base, not a generic industry baseline. We build fraud detection infrastructure for processors, fintechs, and banks that need better tooling than their current processor provides.

Capabilities

What we build

  • 01
    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.

  • 02
    Account takeover detection

    Login anomaly detection, device fingerprinting, and impossible travel checks flag credential stuffing and session hijacking before a fraudulent transaction is placed.

  • 03
    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.

  • 04
    Chargeback and dispute management

    Pre-dispute alerts catch cardholder disputes before they become chargebacks, with automated evidence package assembly for representment.

    Built with
    Verifi · Ethoca
  • 05
    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.

  • 06
    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.

How we work

From scope to live fraud detection system

  1. Week 1
    01

    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.

  2. Weeks 2-5
    02

    Model and threshold design

    Feature engineering and decision thresholds designed against your actual transaction data.

  3. Weeks 6-13
    03

    Build and integrate

    Scoring engine, rules interface, and case management built in parallel, tested against real transaction scenarios.

  4. Final 2-3 weeks
    04

    Launch and model calibration

    Fraud ops team trained on the review queue, with thresholds calibrated against live traffic.

Why us

Why payment businesses choose RaftLabs

  • 01
    Senior engineers build what they scope

    The engineers who assess your transaction data also build the solution. No bait-and-switch, no offshore handoff after the contract is signed.

  • 02
    Fixed price before development starts

    We scope the work, calculate the cost, and lock it in writing before any development starts.

  • 03
    9 years and 100+ products shipped

    Clients include Vodafone, T-Mobile, Aldi, Nike, Cisco, and Lockheed Martin. Track record building regulated, high-throughput platforms.

  • 04
    Built to defend chargebacks, not just prevent them

    Model explanations and evidence packages support representment, not just blocking.

  • 05
    Monitored for drift, not deployed and forgotten

    Retraining pipelines and drift alerts catch degradation before fraud rates rise.

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.

Talk about your fraud detection project

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AI Fraud Detection Software Development, scoped in one call.

Tell us what's broken. Within one business day you get a straight take on cost, timeline, and the right first step. No deck, no pressure.

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Frequently asked 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.

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.

Work with us

Tell us what you need. We'll tell you what it would take.

We scope AI Fraud Detection Software Development 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.

Go deeper

Browse our case studies

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