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.
We deploy a detector that finds procurement fraud — bid rigging, split purchases to dodge approval limits, and vendor-employee collusion — by analysing purchasing patterns and entity relationships. Suspicious activity is cased with the supporting purchase and relationship evidence.
We build a detector for fraudulent and business-email-compromise invoices that flags fake vendors, altered bank details, and duplicate or inflated invoices before payment. Suspicious invoices are held with the anomaly and any bank-detail-change evidence for AP verification.
We deploy an accounts-payable matcher that finds duplicate and erroneous payments across invoices, vendors and payment runs using fuzzy matching on amounts, references and dates. Matches are surfaced pre-payment for holds and post-payment for recovery.
We build a detector that finds fictitious and shell vendors by cross-checking vendor master data against registries, employee records, and address/bank overlaps, and by profiling unusual payment patterns. Suspect vendors are flagged for verification before further payment.
We build a click-fraud detection service that scores ad clicks for invalidity using IP/device reputation, click-timing, conversion-consistency and bot-behaviour signals. Invalid clicks are filtered in real time and compiled into refund-claim evidence for your ad networks.
We deploy a detector that flags impression fraud — bot traffic, stacked/hidden ads, and domain spoofing — by analysing viewability signals, traffic sourcing and placement patterns. Fraudulent inventory is scored and excluded from spend with the evidence for makegoods.
We build a detector that finds affiliate fraud — cookie stuffing, self-referral, fake leads and traffic laundering — by analysing conversion quality, traffic sources and affiliate-to-customer links. Fraudulent affiliates are flagged and commissions held before payout.
For app makers paying ad networks per install who suspect many installs are fake. We deploy a detector that checks install timing, device patterns and attribution data, rejects fake installs and prepares claw-back evidence.
We deploy a detector that finds revenue leakage — unbilled usage, pricing errors, missed renewals, and under-charged contracts — by reconciling entitlements, usage and billing data. Each leak is quantified and routed for recovery with the supporting records.
We deploy an automated anomaly-detection layer over your business metrics — revenue, signups, conversion, error rates — that learns seasonality and alerts on statistically significant deviations. Alerts include probable-cause segmentation so teams know where to look.
We build a graph-analytics detector for two-sided marketplace collusion — buyer-seller wash trading, review rings, and fee-avoidance loops — by linking accounts through transactions, devices and payment instruments. Colluding clusters are scored and surfaced for enforcement.
We deploy a detector that scores marketplace listings and sellers for fraud — counterfeit signals, stolen imagery, price-too-good scams, and bait-and-switch patterns. High-risk listings are held from publication and sellers are risk-tiered for monitoring.
We build a detector for ticketing that identifies scalping bots and account-farming during on-sales via velocity, behavioural and account-cluster signals. Detected bots are throttled and purchases limited so real fans get tickets at face value.
We deploy a detector on outbound wires and payments that flags diversion fraud — last-minute bank-detail changes, unusual beneficiaries and social-engineering patterns — before funds are sent. High-risk transfers require callback verification and dual approval.
We build a detector for telecom fraud — SIM-box bypass, international revenue-share fraud, and Wangiri patterns — by profiling call-detail records for abnormal volume, destination and duration signatures. Suspicious numbers and routes are flagged for blocking with the evidence.
We build a detector that flags fraudulent and abusive warranty claims — claims outside coverage, serial-number reuse, and dealer/repairer collusion — by analysing claim patterns and entity links. Suspect claims route to review before reimbursement with the driving indicators.
We deploy a detector that flags odometer rollback and vehicle-history fraud by cross-checking mileage readings across service, inspection and listing records for impossible decreases and inconsistencies. Suspect vehicles are flagged with the conflicting records for verification.
We build a detector for fuel-card abuse — fill-ups exceeding tank capacity, off-route purchases, and non-fuel spend — by profiling each card and vehicle's normal usage and location. Anomalous transactions are flagged or declined with the exception detail.
We build a detector that flags location-spoofing risk — VPNs, proxies, TOR, residential-proxy networks, and GPS spoofing — to protect geo-restricted, licensing and fraud-sensitive flows. Each request gets a location-trust score with the spoofing signals for policy decisions.
We deploy a configurable velocity engine that tracks counts and sums across any entity — card, device, email, IP, address, account — over sliding windows and triggers rules when thresholds are breached. It gives your fraud team a self-serve way to author and test velocity rules without engineering.
For businesses with several fraud tools, rules and lists that do not talk to each other. We build one decision service that combines them all and returns approve, review or decline with reasons, and logs every decision.
We deploy a case-management workbench that aggregates alerts from all your detectors into a prioritized, deduplicated review queue with full entity context, linked history and one-click actions. Analyst dispositions feed back as labels to continuously improve your models.
We deploy a graph platform that continuously builds a network of your entities and transactions and runs link-analysis, community detection and graph features to expose hidden fraud relationships. It powers other detectors with graph signals and lets investigators explore connections visually.
We deploy a system that ingests incoming disputes, classifies reason codes, decides fight-vs-accept by predicted win probability, and auto-assembles representment evidence packages from your order and delivery data. Winnable disputes are contested automatically within network deadlines.
We deploy a detector over your purchase orders, receipts and inventory movements that flags anomalies signalling fraud, error or diversion — phantom receipts, quantity mismatches, and unusual write-offs. Exceptions are surfaced with the reconciling documents for investigation.
We deploy a detector for hospitality and travel booking fraud — stolen-card reservations, chargeback-prone bookings, and serial no-show/deposit-abuse patterns — by scoring bookings on payment, device and history signals. High-risk bookings require deposits or verification before confirmation.
For online businesses losing money to one person running many accounts to grab promos, referral bonuses or refunds. The AI links accounts that share devices, cards and sign-up patterns and holds suspicious groups for your team to review.
If you pay affiliates commission, some may be buying ads on your own brand name and taking credit for customers who were already looking for you. We watch the search ads for your name in every place you sell, match each ad to the affiliate behind it and collect dated screenshots, so you can act under your own affiliate rules.
For companies whose fraud or abuse model is being dodged by clever users. We attack your model on a schedule the way cheaters would, then retrain it on what slipped through so it keeps getting harder to fool.
For platforms whose fraud or risk scores quietly go wrong when a data feed breaks. It watches every feed going into your risk model and switches to the last good data before bad scores reach customers.
For freight brokers and 3PLs worried about loads being stolen or re-brokered by fake carriers. The AI checks each carrier's identity details and tracking against known patterns and holds a load before it is dispatched to someone suspicious.
For streaming services and distributors whose royalty pool is drained by bots faking plays. The AI watches listening patterns, flags fake streams and puts affected tracks on a royalty hold until someone reviews them.
For businesses hit by phishing and fake invoice or fake boss emails. The AI learns from phishing your staff reported and checks every incoming email, quarantining dangerous ones and sending unclear ones to review.
For businesses on Microsoft 365 or Google Workspace. The AI watches admin logs for signs an account was taken over, like hidden forwarding rules or odd app permissions, and can cut the session automatically.
For brands seeing copies of their products, logos and names popping up online. We scan marketplaces, newly registered domains and social platforms for lookalikes every day and give your team a sorted list with screenshots and links.
For community, marketplace and gig platforms flooded with fake accounts and spam posts. The AI links new sign-ups and posts that share devices, templates or odd timing, slows suspicious ones and sends them to review.
For social, review and marketplace platforms where groups of accounts work together to fake reviews, push content or manipulate rankings. The AI finds those coordinated groups and hands your trust team a ranked list with evidence.
For platforms with human moderators who want to know how consistent their decisions really are. We sample decisions fairly, have a second panel re-review them and show accuracy and disagreement by policy area.
For platforms whose moderation rules lag behind new tricks. The AI groups the content your system is unsure about or has no category for, and alerts your policy team when a new pattern is growing.
Every time a price or discount changes, we check it against your costs and history and hold anything that looks like a mistake until you approve it.
We use the batch codes on your packaging to work out which of your distributors' stock is turning up with sellers who shouldn't have it.
We know how each of your customers normally orders and flag anything odd, like ten times the usual amount or a strange address, before it's confirmed.
We predict which orders are likely to be refused at the door and ask those customers to confirm, or pay upfront, before you ship, so fewer parcels make a costly round trip.
We check every survey answer as it comes in and set aside fake or junk ones before you pay incentives or analyse results.
We connect complaints about wrong items with your warehouse records to find the shelves, look-alike products and process steps causing mistakes.
We watch the in-game currency and item flows and catch copying glitches, farming bots and suspicious trading as it happens.
We check every vote in your contest for cheating, set aside suspicious ones and give you a clean result you can trust.
We check each request to send a verification text and block the ones that look like an attack, so you don't pay for messages to fraudsters.