Fraud Detection Software

Real-time ML scoring pipelines that evaluate transaction risk within 200 milliseconds -- fast enough to inform an accept/decline decision at checkout before the payment processor times out, without adding noticeable latency to the customer experience. Model architecture: gradient boosting (XGBoost or LightGBM) as the primary scorer for its performance on tabular transaction data and its ability to handle the class imbalance typical of fraud datasets (often less than 0.1% fraud rate); a neural network secondary model for card-not-present e-commerce scenarios where sequential transaction patterns across sessions contain signal that tree-based models miss. Feature set constructed at scoring time: transaction context (amount, merchant category code, currency, channel), customer velocity features computed from a Redis-backed sliding window store (transactions in the last 1/6/24 hours by count and value), device and network signals (IP geolocation, IP reputation score from third-party feed, device fingerprint hash), and customer-level behavioural deviation (how much this transaction differs from the customer's 90-day baseline amount distribution, typical merchant categories, and typical transaction time). Scoring API architecture: FastAPI inference endpoint containerised in Docker, deployed behind a load balancer for high availability, with model artefact loaded in memory at startup; median inference latency under 50ms, p99 under 150ms at 500 RPS. Risk score returned alongside the top 5 contributing features ranked by SHAP value so your fraud team understands why a transaction was flagged rather than reviewing a probability score with no context.

Behavioural anomaly detection for account takeover that identifies the statistical deviation between a legitimate account holder's typical access pattern and an attacker using a compromised credential -- catching takeover attempts before the attacker completes a fraudulent transaction or extracts sensitive data. Signal categories evaluated at each login event: device fingerprint (browser characteristics, screen resolution, installed fonts hashed to a device ID) compared against the account's registered device history; IP geolocation and ISP checked against the account's typical access locations and flagged when accessing from a country the account has never used; login time pattern compared against the account's 90-day login time distribution (a user who exclusively logs in during business hours suddenly logging in at 3 AM is a signal even without a geography anomaly); typing cadence and mouse movement pattern analysis for web login flows where passive biometrics are available. Credential stuffing detection at the service level: login attempt velocity per IP subnet, credential pair reuse detection across accounts (if the same username/password pair fails for account A then succeeds for account B within 60 seconds, the successful login is flagged regardless of device score), and Tor exit node / datacenter IP classification blocking anonymous proxies commonly used by automated attackers. Post-login behavioural scoring for the session: immediate high-value action detection (adding a new payee, requesting a large transfer, or changing the email address within 2 minutes of login triggers a step-up authentication challenge regardless of login risk score) -- the pattern that catches sophisticated attackers who achieve a clean login score but then act anomalously.

Fraud scoring models for insurance claims built on the feature sets and domain signals that distinguish legitimate claims from staged incidents, opportunistic inflation, and organised fraud rings -- calibrated to your specific lines of business rather than trained on generic financial transaction data. Feature engineering for claims fraud: claim amount relative to policy coverage and historical claim distribution for that policy type; claim timing (accident-to-claim delay, policy inception-to-first-claim timing); claimant history (number of prior claims, prior declined claims, prior fraud flags); provider or repairer patterns (is this body shop or medical provider appearing disproportionately in claims above average value?); geographic clustering of claims to the same incident location within a short window. Social network analysis using graph database queries on Neo4j or AWS Neptune: identifying organised fraud rings where multiple claimants share the same repair shop, legal representative, or medical provider -- relationships that are invisible in row-level claim analysis but obvious when mapped as a graph. Straight-through processing architecture: a fast scoring path for low-risk claims (score below threshold, no network flags, standard claim type) that routes to auto-approve; a review queue for medium-risk claims requiring adjuster review with the score explanation and flagged signals; and a hold queue for high-risk claims routed to the Special Investigations Unit with the full evidence package. Batch scoring for existing open claims portfolio: overnight processing of all open claims against updated model and network analysis, re-prioritising the investigation queue daily as new claims enter and new network connections emerge.

Threshold calibration infrastructure that makes the fraud catch rate vs. false positive rate trade-off an operational control rather than a fixed model parameter -- because the right operating point changes as your fraud environment, customer base, and review team capacity evolve. Precision-recall curve visualisation for each model showing the full range of threshold options with the corresponding catch rate and false positive rate at each operating point, so the threshold decision is made with full visibility into the trade-off rather than accepting a default. Segmented threshold configuration: different operating points for different customer segments (authenticated, high-tenure customers with established history operate on a relaxed threshold; anonymous or new-account transactions operate on a tighter threshold), different channels (card-present in-store transactions have a different risk profile than card-not-present online), and different transaction categories (high-value international wire transfers require higher sensitivity than domestic small-value purchases). Challenge and review paths as an alternative to hard block: for medium-risk scores that do not justify a decline but warrant friction, a step-up authentication challenge (3DS, OTP, or KBA) allows legitimate customers to proceed while creating a deterrent for fraud -- reducing false-positive declines at the cost of minor legitimate customer friction. Monthly false positive rate review: the count of transactions blocked, manually reviewed and released, broken down by model score decile -- identifying the score band where blocked transactions are predominantly legitimate and the threshold can be safely adjusted.

A configurable rule engine deployed in parallel with the ML scoring model -- because fraud actors deliberately adapt to what gets blocked, and your fraud team needs to add explicit blocking rules for new patterns within hours of identification rather than waiting 2-4 weeks for a model retrain cycle. Rule engine architecture: each rule defined as a condition expression (IF transaction_country NOT IN account_registered_countries AND amount > 500 AND hour_of_day BETWEEN 0 AND 4 THEN BLOCK) with a configurable action (block, challenge, review, alert only); rules evaluated in priority order before the ML score to allow hard blocks for known fraud patterns regardless of model score; the ML score evaluated where no hard rule applies, with the final decision combining rule outcome and ML threshold. Rule management interface: your fraud team creates, modifies, and deactivates rules through an admin UI without code deployment; each rule change is reviewed and activated by a second analyst before taking effect; a simulation mode runs a new rule against the prior 7 days of transactions showing how many transactions it would have blocked and what fraction of those are estimated legitimate. Rule versioning and audit log: every rule, its creator, activation timestamp, modification history, and deactivation reason maintained in an immutable audit log -- the documentation trail that regulators require for decisions affecting customer access. Decision explainability export: each block or flag decision logged with the specific rule or model features that triggered it, exportable in human-readable format for customer dispute resolution, regulatory investigation, and analyst training documentation.

A case management interface where flagged transactions and accounts are queued for analyst review with the full context needed to make a confident decision in under 3 minutes rather than assembling information from four different system tabs. Case view per flagged transaction: the transaction details, the model risk score with SHAP feature explanation (showing the three factors that drove the score), the account's transaction history for the prior 90 days with the flagged transaction highlighted, the device timeline showing all devices used by this account and when, IP and geolocation history on a map, similar confirmed fraud cases from the past 6 months that match the current pattern, and all prior review decisions on this account. One-click action interface: Approve (mark legitimate, remove from review queue, update the account's baseline), Block and notify (decline the transaction or lock the account, trigger the customer communication workflow), Escalate (assign to senior analyst or Special Investigations Unit with a reason note), and Request additional information (trigger a verification request to the customer). Queue management: cases assigned to analysts by risk score and specialisation (some analysts focus on account takeover, others on payment fraud); queue depth and average handle time tracked per analyst; cases unreviewed after a configurable SLA period escalated automatically. Review outcome logging: every decision recorded with the analyst ID, timestamp, reason code, and the evidence that supported it -- the audit trail for regulatory examination and for measuring the model's precision by checking what fraction of reviewed-and-approved cases subsequently triggered new fraud flags.