Spot fraud and unusual activity early, before it costs you money.
We deploy a real-time detector that spots BIN enumeration and card-testing bursts by watching micro-authorization patterns, decline ratios and shared device/IP fingerprints across your checkout. It auto-throttles, injects challenges, and quarantines attacking sessions before they burn your processor good-standing.
We train a model on your settled transactions and dispute outcomes to predict which orders will become chargebacks, scoring each at checkout and post-purchase. High-risk orders route to manual review, delayed fulfilment, or proactive refund to dodge the dispute fee.
We deploy a model that separates genuine 'item not received' disputes from friendly fraud by correlating delivery evidence, device history, prior dispute behaviour and account tenure. Flagged first-party fraud gets auto-assembled representment evidence to win the chargeback.
We deploy controls on your outbound money movement — refunds, seller payouts, supplier payments — that score each payout against destination history, velocity and account-change events. Suspicious payouts are held for dual approval before funds leave.
We build a continuous ATO detector that models each user's normal device, geo, and behaviour, and scores login and post-login events for takeover signals like impossible travel, credential reuse and sudden setting changes. High-risk sessions trigger step-up auth or lockout in real time.
For online businesses whose login page gets flooded by bots trying leaked passwords. We deploy a detector that spots these waves, checks for known leaked passwords, and slows or challenges the attack until it stops.
We build a continuous authentication layer that watches for mid-session device, network and session-token drift indicating a hijacked session (no behavioural biometrics such as typing or mouse patterns). When continuity breaks, it forces re-authentication or terminates the session before sensitive actions complete.
We deploy a detector that finds review fraud by clustering reviewer accounts, timing bursts, text similarity, and purchase-verification gaps across your marketplace or storefront. Suspicious reviews are quarantined and rings are surfaced for enforcement.
We build a detector for loyalty-program abuse — points farming, account merging, self-referral and unauthorized redemption — by modelling earning/redemption patterns and account relationships. Anomalous point flows are frozen and flagged for review before redemption.
We deploy a detector that flags refund abuse — serial 'item not received' claims, wardrobing, and refund-then-keep patterns — by profiling customer refund history and cross-account links. High-risk refund requests route to manual review or require proof before processing.
We build a detector that finds promo-code abuse — multi-accounting to reuse first-order discounts, code sharing, and stacking exploits — by linking accounts via device, payment and address signals. Abusive redemptions are blocked or clawed back automatically.
We deploy a returns-abuse system that scores customers and orders for serial returning, bracketing, worn-item returns and return fraud using purchase and return history plus item-condition signals. High-risk returns are routed to manual inspection while good customers keep frictionless returns; statutory return rights are never removed.
We build a detector that identifies resellers and inventory-hoarding bots on limited-stock drops via velocity, account-clustering, checkout behaviour and shipping-consolidation patterns. Detected resellers are throttled, held, or purchase-limited to protect genuine customers.
We deploy a detector that catches serial free-trial abuse and multi-accounting by linking signups through device fingerprints, payment instruments, email/phone patterns and behaviour. Repeat abusers are blocked from re-triailing or converted to paid-only flows.
We build a detector for gift-card fraud — balance-checking bots, cracked codes, and laundering of stolen cards into gift cards — by monitoring redemption velocity, balance-check patterns and purchase-to-redemption links. Suspicious cards are frozen and flagged before value is drained.
We deploy a detector that finds fraudulent cashback and rebate claims — fabricated receipts, duplicate submissions, and self-referral loops — by validating receipts and linking claimant accounts. Suspicious claims are held for verification before payout.
We build a detector that finds electricity/gas/water meter tampering and theft by profiling consumption patterns against expected usage, weather and peer baselines. Suspicious accounts are ranked for field inspection with the anomaly evidence.
We deploy a monitoring service on temperature, humidity and door-event sensors across your cold-chain that detects excursions and spoilage risk in real time. Alerts and predicted spoilage windows fire before product is lost, with a full excursion log.
We build a detector on vehicle telematics that flags fuel theft, unauthorized use, harsh-driving risk and odometer/route anomalies across your fleet. Flags feed a vehicle-level review queue for action; drivers are not individually scored or ranked.
For manufacturers who find defects only after a batch is ruined. We connect your line sensors to a monitor that spots drifting conditions early and tells operators which stage and setting to check.
We build a network anomaly-detection system that baselines normal traffic and flags intrusions, lateral movement and beaconing that signature tools miss. Alerts are enriched, prioritized and pushed to your SOC or SIEM with context.
We deploy a monitor on privileged and admin activity that baselines expected use of high-risk accounts and flags off-hours, off-pattern and escalation anomalies. High-risk actions can require just-in-time approval before they execute.
For businesses with an API or website whose data gets scraped or whose API gets abused. We build a detector that learns each client's normal use and slows, traps or cuts off abusive ones.
We build a detector that scores employee expense reports for fraud and policy abuse — duplicates, inflated amounts, mischaracterized personal spend and out-of-policy vendors — using receipt parsing and peer-pattern models. Flagged reports route to finance review with the specific exceptions.