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AI detection of suspicious transactions
Introduction: Why classic rules are no longer enough
Fraud and abuse evolve faster than lists of rules. Account farms, structuring schemes, "mules," delay arbitration, chargeback attacks appear. AI detection supplements rules with models, time series and graphs to recognize new things, reduce false positives and speed up honest payouts. Critical: solutions must be understandable, and processing must meet the requirements of privacy and regulators.
1) Data: what the system needs to see
Payment events: deposit/withdrawal, method (card, wallet, bank transfer), amount, currency, commission, status, retray, chargeback/dispute.
Device and session context: browser/device fingerprint, OS, network/proxy, location (consent!), Behavioral timings.
Account profile: KYC/AML status, limits, method history, account age, trusted devices.
Play/trade signals: rate of bets/rounds, TTFP/hit-rate (for interpretation of "success"), cancellation of conclusions.
Marketing and bonuses: coupons, wagering conditions, activation frequency.
External directories: BIN-tables, sanctions/PEP-lists, georisks, reputation of IP/numbers.
Principles: single event bus, idempotency, accurate timestamps, PII tokenization, minimum storage.
2) Feechee: How to code 'suspicious'
Time series: frequency of transactions through windows (30s/5m/1h/1d), "deposit → withdrawal" rhythm, bursts of night activity.
Structuring amounts: repeat transactions just below KYC/AML limits, split deposits/withdrawals.
Geo/method consistency: karta≠IP≠geo, fast country/device changes, proxy ranges.
Behavioral biometrics: stability of timings, abnormally even click intervals (bot risks).
Connection graph: common devices/IP/cards/wallets/referrals → communities, bridges, "mules."
Method reputation: new method with high historical chargeback-rate; "rotation" of methods in a short time.
Product context: cancellation of withdrawal for the sake of a new deposit, impulsive overbets - it is important not to mix with fraud (these are RG signals).
Online features are in the online feature store for scoring with low latency.
3) Models: from rules to graphs and sequences
Rules-as-Code: geo/age/limits, risk lists, "hard" bans of providers/countries, basic redlines of amounts.
Unsupervised anomalies: isolation forest, autoencoder, One-Class SVM by the vector of window features (frequency, sums, geo, methods).
Supervised scoring: GBDT/log on marked incidents (chargeback, bonus abuse, account taker). The main metrics are PR-AUC, precision @ k.
Graph models: search for communities (Louvain/Leiden), centrality, link prediction for "multiaccounting" and output rings.
Sequence models: RNN/Transformer for deposit-jump-output patterns, scripted run-through scenarios.
Probability calibration: Platt/Isotonic so that the rate is calibrated in deferred periods/markets.
XAI layer: SHAP/rules-surrogates - short reasons for decisions for support and regulator.
4) Decision orchestrator: "green/yellow/red"
For each transaction, the system aggregates rules + scoring and selects a scenario:- Green (low risk): instant confirmation, instantaneous output with matching profiles, transparent status.
- Yellow (doubt): soft 2FA, method/ownership confirmation, request for clarification, amount capping, delayed withdrawal until verification.
- Red (high risk): transaction pause, bonus freeze, HITL check, advanced graph analysis, AML notification.
Each solution falls into the audit trail (input features, model versions, thresholds, applied rules).
5) Typical diagrams and system response
Structuring for KYC limits: a series of deposits/withdrawals just below the threshold → yellow, capping, KYC-deepening.
Rings of "mules": dozens of accounts with common devices/wallets → red, frieze of funds, investigation by a graph.
Account-teikover: new geo/device + adding a new method + sharp output → red, forced password change, confirmation of ownership, rollback.
Bonus farm: mass coupon activation from one IP range → yellow/red, promo frieze, KYC check.
Honest big win → conclusion: EVT in the game/market is normal, there are no connections → green, instantaneous payment and public proof of honesty.
6) Payment orchestrator: the speed of honest and the safety of dubious
Smart routing: choosing a provider by risk, country, amount, ETA and fees.
Dynamic limits: increased for "green" profiles, reduced/piece check at risk.
Frictionless retrai: automatic switching of the provider in case of temporary failures.
Transparent statuses: "instant/need verification/manual verification" + ETA and the reason for the step.
7) Privacy and justice
Layer consents: explicit toggle switches on behavioral/technical cues.
PII minimization: tokenization, storage of only what is needed, access according to the principle of least rights.
Federated learning: models learn from aggregates; raw user data does not leave the region.
Fairness controls: monitoring bias across markets/devices/channels; prohibition of discriminatory features.
RG boundaries: behavioral risks (overheating) → careful measures (limits/pauses/Focus), not sanctions.
8) Metrics that really matter
PR-AUC/precision @ k/recall @ k on marked fraud cases.
FPR on "green" profiles: the share of erroneously delayed honest transactions.
IFR (Instant Fulfillment Rate): proportion of fair deposits/withdrawals "without friction."
TTD/MTTM: incident detection/mitigation time.
Chargeback rate/recovery: dynamics of chargebacks and returns after implementation.
Graph-lift: contribution of graph features to the detection.
NPS of trust: to the statuses and explanations of clients/partners.
9) Solution Reference Architecture
Event Bus → Stream Aggregator → Online Feature Store → Scoring API (rules + ML + graphs) → Decision Engine (zel ./yellow/red.) → Action Hub
In parallel: Graph Service, Payment Orchestrator, XAI/Compliance Hub (logs, reports, versions), Observability (metrics/trails/alerts).
10) MLOps and reliability
Versioning of data/features/models/thresholds; reproducibility и lineage.
Drift monitoring of distributions and calibration; shadow runs, fast rollback.
Data chaos engineering: gaps/duplicates/delays → graceful degradation, not failure.
Sandboxes for auditors: replay historical flows and check the detector.
Feature flags by jurisdiction: different thresholds/procedures, reporting formats.
11) Implementation Roadmap (6-9 months)
Months 1-2: single event bus, rules-as-code, online feature store, transaction statuses for the client.
Months 3-4: unsupervised anomalies, supervised scoring, Decision Engine zel ./Yellow ./Red. , "XAI panel.
Months 5-6: graph service (communities/connections), integration with a payment orchestrator, auto-mapping of amounts.
Months 7-9: market calibration, federated learning, chaos tests, regulator sandboxes, IFR/TTD/MTTM optimization.
12) Frequent mistakes and how to avoid them
Punish "by amount." The amount itself ≠ the risk; form and context are important.
Ignore the graph. Individual scorings skip farms and bridges.
Chase 0% FPR. Excessive thresholds kill payout speed and trust.
Mix RG and fraud. Behavioral anxiety is treated with limits/pauses, not bans.
Without XAI. Unexplained delays give rise to complaints and fines.
Fragile infrastructure. No feature flags/rollback means inevitable downtime with changes.
AI detection of suspicious transactions is an engineering trust loop. It combines rules, models and graphs, explains decisions and respects privacy while speeding up honest operations. The winners are those who build speed (low-latency scoring), accuracy (PR-AUC, graphs), transparency (XAI, statuses) and ethics (RG, fairness) in one architecture - then each transaction becomes predictably safe for all parties.