Real-time fraud detection before losses hit P&L

Scores fraud risk for every transaction in milliseconds. Analyzes device, behavior, order characteristics, payment details, shipping address. Blocks, allows, or queues for review automatically based on risk score.

Detects account compromises through login behavior analysis, device recognition, location changes, password reset patterns. Triggers additional authentication or blocks access when takeover suspected.

Identifies customers exploiting promotions: creating multiple accounts, using stolen codes, abusing referral programs, circumventing limits. Prevents promotional fraud before losses accumulate.

Flags wardrobing (buy, use, return), serial returners, item swapping, false claims. Cross-references with Returns Intelligence System for comprehensive fraud prevention across purchase and return.

Detects stolen cards, card testing, synthetic identity fraud, friendly fraud. Integrates with payment processors for additional verification (3DS, AVS, CVV). Prevents chargebacks.

Identifies organised fraud: linked accounts, shared devices, coordinated timing, similar shipping addresses. Detects fraud rings operating across multiple accounts systematically.

Identifies automated traffic: inventory scrapers, account creation bots, credential stuffing, checkout bots. Protects inventory, prevents account abuse, maintains site performance.

Routes medium-risk transactions to manual review with full context: risk factors, customer history, similar cases, recommended action. Streamlines fraud analyst workflow, improves decision quality.

Problem

Transaction fraud, account takeovers, promotional abuse, return fraud cost 1-3% of revenue. Manual review can't scale with transaction volume. By the time fraud is caught, losses have accumulated. Chargebacks damage merchant accounts.

Solution

Real-time scoring catches fraud before processing. Pattern detection identifies organised fraud rings. Automated blocking prevents losses. Chargeback prevention protects merchant standing.

Typical impact: Fraud losses down 60-80%, chargeback rate reduced 70%, prevented fraud value 2-5x higher than detection cost

Problem

Rule-based fraud systems block good customers: declined cards, delayed orders, additional verification steps. Customer frustration, abandoned carts, support burden. Lost revenue from legitimate transactions blocked.

Solution

AI distinguishes legitimate high-value orders from fraud. Behavioural analysis confirms customer legitimacy. Fewer false positives mean better customer experience and higher revenue.

Typical impact: False positive rate down 70%, customer complaints down 60%, revenue recovered from legitimate orders previously blocked

Problem

Fraud teams manually review suspicious transactions: checking addresses, googling phone numbers, validating emails. Can't keep up with volume during peak times. Review backlog delays legitimate orders.

Solution

AI handles 85-90% of decisions automatically (clear fraud or clear legitimate). Manual review focuses on ambiguous cases with full context and recommendations. Review time per case drops significantly.

Typical impact: Manual review volume down 85%, review time per case down 60%, fraud team capacity freed for strategic fraud prevention