// RISK & FRAUD | AI-POWERED
AI Fraud Detection: See the Invisible.
Fraudsters work with AI today. Anyone defending with static rules has already lost. Xanevo uses anomaly detection to find patterns no human sees, in real time, in checkout, in login, in invoice review. Outcome: chargebacks down, false positives low, conversion stable.
THE PROBLEM
Static rule sets are dead
Classic fraud defense works with rules: if cart greater than 500 EUR, then manual review. If shipping address differs from billing, then block. This logic produces two problems at once: too many false positives (real customers get stopped, revenue lost) and too many false negatives (fraudsters learn the rules and bypass them).
In B2B, forged invoices hit the inbox: PDF looks real, letterhead matches, only the IBAN was swapped. CEO fraud, invoice fraud, supplier identity theft, mid-market companies regularly lose six-figure amounts because an invoice got manually released that nobody scrutinized.
In e-commerce, hackers take over existing customer accounts via credential stuffing from data breaches. They order with stored payment data to a drop address. The damage surfaces only when the real customer sees the charge and triggers a chargeback. By then the goods are gone and the merchant carries the loss.
What separates AI fraud detection from rule sets?
AI fraud detection works with anomaly detection instead of fixed thresholds. Instead of asking "Does the transaction exceed a rule?", AI asks: "Does the behavior match the previous pattern of this customer, this device, this region?" Behavioral biometrics, device fingerprinting, and pattern recognition deliver a risk score of 0 to 100 in under 200 milliseconds. Standard goes through, suspicious gets flagged, high-risk gets blocked. Protection that doesn't break revenue.
Four layers against three fraud patterns
/ OUR APPROACH
01
Step 01
Anomaly detection at checkout
At checkout, AI checks in real time: do shipping address, payment method, order timing, and cart match the customer's typical pattern? This also works for new customers because global patterns (e.g. typical drop-address clusters, unusual device constellations) serve as comparison baseline. Cold-start problem solved.
02
Step 02
Account takeover protection
At login, AI catches credential stuffing: login comes from Vietnam, customer lives in Berlin, device is new, typing pattern is off. Behavioral biometrics even analyzes how fast and where the cursor moves. On suspicious pattern: step-up auth, not direct block. Real customers with new devices get through, bots and hackers don't.
03
Step 03
B2B invoice shield
Incoming PDF invoice gets compared against supplier master data and historical payments. If the IBAN deviates from the recorded one, the letterhead looks manipulated, or the language structure is unusual, the invoice goes to review instead of direct release. CEO fraud and invoice fraud get stopped before the money leaves the company.
04
Step 04
Seamless API integration
We integrate via REST API into the checkout process or ERP system. Response time under 200 milliseconds, the customer notices nothing. Risk score 0 to 100 comes back, you decide on action: standard pass-through (0–40), step-up auth (40–70), clerk review (70–85), block (85–100). You set the thresholds.
Protection must not break revenue
Every fraud solution is a balancing act between security and conversion. Too-strict rules stop fraudsters but also real customers who just have a new device or are ordering on vacation. Too-loose rules let chargebacks and invoice fraud through. Behavioral AI solves the balance: false-positive rate low, false-negative rate low, because the model learns per customer and uses global patterns. The mantra: protection that doesn't break revenue.
< 200 ms
Response time per risk score query
An API response under 200 milliseconds means: the customer sees no noticeable difference in checkout. Classic review processes (manual release, external review services) typically take 1 to 5 seconds, long enough that conversion rates measurably drop. Behavioral AI runs synchronously, the checkout stays fluid.
Static rule vs. AI anomaly detection
Aspect
Static rule set
Xanevo anomaly AI
False positives
High (5–15%), real customers blocked
Low (under 1–2%)
Adaptation to fraud patterns
Manual rule updates, weeks of delay
Model learns continuously
Cold-start new customers
Blanket rule, often block
Global patterns as comparison baseline
Latency
Seconds for manual review
Under 200 milliseconds synchronous
Three questions from discovery calls with CFOs and e-commerce leads
False-positive rate is extremely low, significantly lower than with strict rules. Instead of hard blocks, we use graduated reactions: low risk passes through, medium triggers step-up auth, only high risk gets blocked. Real customers with new devices or on vacation get through.
Yes. For new customers without personal history, we use global patterns: typical drop-address clusters, device constellations from bot nets, behavioral patterns from known fraud cases. AI matches the new customer against this reference, not a personal history.
For payment fraud: transaction data (cart, addresses, payment method). For ATO: device fingerprint, user behavior, login context. For invoice fraud: supplier master data plus historical payments. We dock via REST API, on-premise or DACH hosting, GDPR-compliant.
Related deep dives
Advanced AI Services cluster
Fraud detection belongs to the Advanced AI Services cluster family. Find Dynamic Pricing, Predictive Analytics, and Digital Product Passport in context here.
Finance industry page
Invoice fraud, automated document capture, and SAP integration in the finance context. If fraud detection is the single-service topic, the industry page is the end-to-end view.
// FRAUD DETECTION
Secure your spot.
Requirement: willingness to contribute pseudonymized transaction data as training sample. Discovery call clarifies data needs and expectations.