AI Fraud Detection for Enterprise

Rules-based systems require a human to encode every pattern: if velocity > X and amount > Y and device is new, then flag. ML models learn patterns from labeled data and generalize to cases no rule explicitly covers. Three practical consequences: fewer false positives because models capture subtle feature interactions that rules simplify; better novel fraud detection because models pick up pattern drift before a human notices; lower maintenance cost because retraining replaces rule-writing. Most mature programs end up running both in parallel: rules for known high-certainty patterns and regulatory requirements, ML for everything else. They're complementary, not competitive.

Minimum: 12-18 months of transaction data with fraud labels (confirmed fraud vs. legitimate), account data, device and IP data, and outcome data for any manual reviews. Ideal: additionally, historical chargeback data, customer complaint data, behavioral session data (clickstream if available), and third-party enrichment (email age, IP reputation, device reputation). The more signal you provide, the better the model performs, but clean core transaction data with good labels gets you 80% of the value. We run a data quality assessment before building to identify gaps that affect model performance, so you're not surprised by accuracy results.

New accounts lack transaction history, which is the strongest feature set. We handle this with proxy features: device reputation from MaxMind or SEON, email age and mailbox provider risk from Emailage, IP risk signals, behavioral biometrics from session data (typing cadence, mouse movement patterns), and third-party KYC signals from Alloy or Persona. We apply conservative thresholds for accounts under 30 days and progressively relax as behavioral history accumulates. For lines of business with high new-account fraud risk (deposit accounts, BNPL) we layer additional identity verification for early transactions above specific thresholds.

Yes. Every score comes with SHAP-based feature contributions: the top 5-10 features that drove the score, with each feature's directional contribution. Analysts see something like 'velocity (3 transactions in 20 minutes, 98th percentile for this customer): +0.31; new device and new IP: +0.18; amount 4x typical: +0.12; merchant not in historical pattern: +0.08'. This is directly actionable for the analyst and also satisfies regulatory explainability requirements (adverse action notices, CFPB examinations). Graph-model flags include the specific ring members and the shared attributes. The system never returns a score without a reason.

Three mechanisms. First, the isolation forest novelty detector flags transactions that are statistical outliers relative to learned distributions, even if the primary scoring model scored them low. Second, the graph model catches co-occurrence patterns (new device, new IP, new email structure) even when no individual transaction looks suspicious. Third, drift monitoring on feature distributions alerts when an emerging pattern is present in enough recent transactions to be systematic rather than random. When a genuinely novel pattern appears, analysts see it first in the outlier queue, and the first few confirmed cases feed back to retraining within days rather than at the next quarterly cycle.

Wrong in this context is either a false positive (legitimate transaction flagged) or a false negative (fraud missed). False positives are caught by analyst review and by customer dispute resolution, with labels feeding back to the training signal. False negatives are caught by chargeback, customer complaint, or network alerts (e.g. a consortium flag on an account), also labeled and fed back. The model improves every week. We track catch rate and false positive rate weekly and investigate any week-over-week regression. The rules engine remains as a safety net for patterns the team is willing to enforce unconditionally (velocity limits, geography restrictions on high-value transactions).

Every scored transaction writes to a decision log: transaction ID, timestamp, model version, input features, computed score, SHAP contributions, rules-engine output, final decision (approve / review / decline), analyst override if any, and final outcome (confirmed fraud, confirmed legitimate, disputed, charged back). The log feeds into Snowflake or your data warehouse for analytics and into your risk management system for governance. For regulated lenders we produce monthly attestation reports showing model performance, false positive rate by customer segment (to detect unintended disparate impact), and a summary of any drift events. External auditors and examiners get scoped read access. Model cards documenting training data, features, and known limitations are maintained per version.

A focused deployment on a single transaction type runs 14-18 weeks. Weeks 1-3 are data audit and feature engineering on your historical data. Weeks 4-7 build and validate the baseline model with temporal cross-validation. Weeks 8-10 integrate the scoring service and add the graph model. Weeks 11-14 run shadow mode: the ML score is computed alongside the rules engine but doesn't affect decisions, and we compare outcomes weekly. Weeks 15-18 phase into production: 10% traffic, 30%, 60%, full, with gates on catch rate and false positive rate at each step. Additional transaction types or business lines typically add 6-8 weeks each. The ongoing retraining cadence is weekly once you're live.