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Service 01

Fraud Tool Integration
& Optimisation

Fine-tuning scoring models, calibrating thresholds, and designing rule logic that maximises approval rates without sacrificing fraud protection.

Industry benchmarks
Industry avg false decline rate5–8%
Revenue lost to false declines$443bn / yr
Reduction achievable w/ tuning30–60%
Avg chargeback rate (healthy)< 0.9%
VAMP threshold (Visa)0.9% / 1.8%

The problem most businesses don't realise they have

Most fraud tools are deployed, switched on, and left largely untouched. The initial configuration — done hastily during an integration sprint — becomes the permanent configuration. Rules accumulate without review. Score thresholds drift out of calibration as your customer base and transaction mix evolve. The result is a system that either lets too much fraud through, or declines too many legitimate customers. Usually both, in different pockets of your traffic.

False declines are the hidden cost almost nobody measures properly. Every declined legitimate transaction is lost revenue, a damaged customer relationship, and an invitation for that customer to buy from a competitor. Industry estimates put global false decline losses at over $443 billion annually — dwarfing actual fraud losses by a factor of 13.

A poorly tuned fraud tool doesn't just let fraud through — it declines your best customers. We've consistently found more revenue in false decline recovery than in fraud reduction itself.

What we actually do

Our fraud optimisation work covers the full stack — from the rules and models your system uses to make decisions, through to the data inputs feeding those decisions, and the workflows that handle edge cases.

Scoring model calibration

Machine learning fraud models need regular recalibration. Your customer base shifts. Fraud patterns evolve. Seasonal transaction changes skew score distributions. We audit your model's current performance, identify where score bands are over- or under-represented, and work with your vendor to recalibrate — without requiring a full model retrain.

Threshold and rule optimisation

Most businesses apply a single set of thresholds across all transaction types. But a £10 digital subscription and a £2,000 electronics purchase carry fundamentally different risk profiles and should never share the same accept/decline logic. We build segmented threshold strategies — by product type, channel, geography, device, customer tenure, and more — that materially improve approval rates without increasing fraud exposure.

Rules engine design and hygiene

Rules accumulate. Old rules conflict with new ones. Velocity checks trigger incorrectly on multi-device customers. Block lists haven't been reviewed in years. We audit your full rules engine, remove or amend conflicting and outdated logic, and rebuild the ruleset around a clear hierarchy that's easy to maintain.

3DS strategy

3D Secure is both a fraud tool and an abandonment risk. Applied indiscriminately, it destroys conversion. Applied intelligently — using risk-based authentication to challenge only what needs challenging — it reduces fraud while keeping friction invisible to most customers. We design 3DS strategies that balance authentication rates with conversion impact.

False positive analysis

If you're not measuring false positives, you're flying blind. We build or improve your false positive tracking — analysing declined transactions that subsequently re-attempted and converted, or that show characteristics inconsistent with fraud — to give you a clear picture of what your fraud tool is costing you in real revenue.

Vendor landscape

We have hands-on experience across all major fraud platforms. We don't recommend one over another — the right tool depends on your business model, transaction volume, technical infrastructure, and risk profile. What we can do is tell you whether your current tool is right for you, and how to get more from it.

Sift
Account & identity fraud
Strong on account takeover, login abuse, and promo fraud. Particularly suited to marketplace and platform businesses.
Riskified
Chargeback guarantee
Chargeback guarantee model — Riskified owns the fraud decision and eats the chargeback if wrong. Works well for high-AOV e-commerce.
Stripe Radar
ML adaptive scoring
Native to Stripe. Leverages network-wide signals across millions of businesses. Good out-of-the-box, transformative when tuned.
Kount
Identity network
Equifax-backed identity intelligence network. Strong device fingerprinting and link analysis. Suits mid-market and enterprise.
Signifyd
Commerce protection
Commerce-focused with a financial guarantee option. Strong on buyer identity verification and post-auth fraud.
Sardine
Behavioural biometrics
Newer entrant with sophisticated behavioural biometrics and device intelligence. Particularly strong for fintech and crypto.

Our process

01

Data collection & baseline

We pull 90 days of transaction data, chargeback records, and decline logs. We establish your current approval rate, fraud rate, false decline rate (estimated), and chargeback breakdown by reason code.

02

Rules & model audit

We document every active rule, velocity check, and list. We review score distributions across transaction segments. We identify conflicts, redundancies, and miscalibrations.

03

Segmentation design

We build a transaction segmentation framework — by value, channel, product, geography, customer age, and device — that allows different risk logic to apply to different populations.

04

Recommendations & prioritisation

We deliver a prioritised action plan. Not a wish list — a ranked list of changes with expected impact on approval rate, fraud rate, and revenue, along with implementation complexity.

05

Implementation support

We stay involved through implementation — working directly with your fraud vendor's team, reviewing configuration changes, and validating results against the baseline.

Model CalibrationThreshold OptimisationRules Engine Design3DS StrategyFalse Decline RecoveryVelocity RulesDevice IntelligenceChargeback Reduction

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