Fraud & Risk Engine

Overview

Fraud prevention is a balancing act between blocking bad transactions and preserving approval rates on good ones, and getting that balance wrong in either direction has a direct cost: too aggressive, and you lose legitimate revenue and frustrate customers; too permissive, and fraud losses and chargebacks erode margins. Pay Engineers builds fraud and risk engines that give you precise, adjustable control over that balance in real time.

The engine combines deterministic rules, velocity checks and configurable step-up policies for 3-D Secure challenges, so the vast majority of transactions are decisioned automatically in milliseconds, while genuinely ambiguous cases are routed to a human analyst rather than either auto-approved or auto-declined by a rigid rule.

We treat policy as a living asset that must be adjustable quickly as fraud patterns shift, but also fully auditable, since risk and compliance teams need to know exactly what policy was in force for any transaction under later review.

Who This Is For

• PSPs, acquirers and merchants experiencing fraud losses or chargebacks above acceptable thresholds
• Businesses whose current risk approach is either overly manual or overly rigid, with no middle ground
• Platforms wanting to reduce unnecessary 3-D Secure friction on low-risk transactions while maintaining strong protection on high-risk ones
• Fraud and risk teams needing proper case management tooling rather than working from raw transaction logs

What You Get

• Real-time scoring evaluating each transaction against configurable risk signals within the latency budget authorisation flows demand
• Velocity rules detecting abnormal patterns in transaction frequency, amount or behaviour across cards, devices and accounts
• Case management giving fraud analysts a focused queue of borderline transactions, with the context needed to decide quickly
• Policy versioning so every rule change is tracked, attributable and reversible if it does not perform as expected

Technical Approach

The engine is built on Laravel with Redis powering the low-latency velocity checks and counters that real-time scoring depends on, since fraud decisions at authorisation time must complete within a strict latency budget without becoming the bottleneck in your checkout flow. A dedicated rule engine evaluates configurable policies against each transaction, combining deterministic rules with weighted scoring so that no single signal alone determines an outcome.

Where machine learning is appropriate, we build hooks that allow model-derived scores to feed into the same policy engine as rule-based signals, so your risk team can blend statistical models with explicit business rules rather than treating them as separate, disconnected systems. Case management tooling surfaces the specific signals that triggered a review, not just a raw score, so analysts can make fast, informed decisions instead of reverse-engineering why a transaction was flagged.

Delivery Process

• Discovery of your current fraud losses, chargeback patterns and existing rule or vendor setup
• Risk policy design covering rules, velocity checks and step-up authentication thresholds tailored to your risk appetite
• Sprint-based build of the scoring engine, followed by case management tooling for your analyst team
• Shadow-mode testing, running the new engine alongside your existing process to compare outcomes before cutover
• Phased rollout with close monitoring of approval rates and fraud metrics during the transition

Outcomes and Benefits

• Reduced fraud losses and chargebacks without a proportional loss in approval rates on legitimate transactions
• Faster, better-informed analyst decisions through focused case queues rather than raw log review
• The ability to react quickly to emerging fraud patterns through policy changes rather than engineering releases
• A fully auditable policy history, valuable for scheme, regulatory or internal risk committee review