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How artificial intelligence analyzes transactions
Introduction: A transaction is a story, not a string in a database
Each transaction carries a context: who initiated, from which device, what payment method, what background of the session and payments, how related accounts behave. The AI 's task is to collect this mosaic in milliseconds, assign a risk/intent value and choose the right action - from instant confirmation to soft verification or stop. At the same time, decisions must be understandable and respect privacy.
1) Data: What AI sees "on the wire"
Payment events: deposit/withdrawal, method (card/wallet/bank), amount, currency, commission, status, retrai, chargeback/dispute.
Channel context: web/mobile, OS/browser, network/ASN, proxy/TOR, geo (if agreed), connection quality.
Account and behavior: account age, KYC/AML status, method history, trusted devices, pace of operations, cancellation of conclusions.
Product signals: bet/buy rhythm, TTFP/hit-rate (to interpret "success"), participation in promo/bonuses.
External directories: BIN, sanctions/PEP lists, risk assessment of IP/numbers, georisks, stop lists of providers.
Principles: single event bus, idempotency, accurate timestamps, PII tokenization, minimum storage.
2) Fichy: turning raw fields into meaning
Time series: frequency of transactions in windows (30s/5m/1h/1d), "depozit→vyvod" latency, night bursts.
Structuring amounts: repeated operations "slightly below" the thresholds of the CCM/fraud rules, splitting amounts.
Consistency of identity: karta≠IP≠geo, frequent device/method changes, shared devices.
Behavioral biometrics: click/form timing distributions, stable "bot patterns."
Graph of connections: common IP/devices/cards/wallets/referrals → communities, bridges, "mules."
Reputation of methods/providers: historical chargeback-rate, ETA, fault tolerance.
Product context: cancellation of withdrawal before a new deposit, impulsive overbets - RG signals, not automatically fraud.
3) Model stack: from rules to sequences and graphs
Rules-as-Code: jurisdictional redlines (age/geo/limits), stop lists, "hard" thresholds for amounts.
Unsupervised anomalies: isolation forest, autoencoder, One-Class SVM on window vectors (frequencies/sums/geo/methods).
Supervised scoring: GBDT/log on marked cases (chargeback, bonus abuse, ATO). Metrics: PR-AUC, precision @ k.
Graph models: Louvain/Leiden, centralities, link prediction for "rings" and mule chains.
Sequence models: RNN/Transformer on login→depozit→stavki→vyvod trajectories for catching scripted scenes.
Probability calibration: Platt/Isotonic for reliable thresholds by market/channel.
XAI layer: SHAP/surrogate rules → short decision reasons for support/regulator.
4) Decision orchestrator: "green/yellow/red"
Green (low risk): instant confirmation, instantaneous output, transparent status with ETA.
Yellow (doubt): soft 2FA, confirmation of ownership of the method, capping of the sum/frequency, deposition before verification.
Red (high risk): transaction pause, promo frieze, HITL check, advanced graph analysis, AML notification.
All solutions are logged in audit trail (input features, model versions, applied rules).
5) Not to confuse honest luck with suspicious anomaly
A large gain/withdrawal in itself is not a sign of fraud. We check: compliance with the RTP/volatility profile, EVT tails, the absence of "suspicious" graph connections, the stability of the studio/room versions. If everything is valid - a green script and a public proof of honesty.
6) Integration with payment orchestrator
Smart routing: choosing a provider by risk/country/amount/ETA/commission.
Dynamic limits: higher for "green" profiles, lower in doubt.
Auto-retrays: in case of failures - switching provider without user intervention.
Honest statuses: "instantly/need verification/manual verification" + an understandable reason for the step.
7) Privacy, fair controls and RG
Layer consents and personalization toggle switches.
PII minimization: tokenization, encryption, least rights access.
Federated training and local processing where possible; on reports - differential noise.
Fairness monitoring: no systematic skewing across markets/channels/devices.
RG priority: behavioral risks → soft limits/pauses/Focus mode, not sanctions.
8) System Success Metrics
Detection quality: PR-AUC, precision/recall @ k, FPR by "green" profiles.
Fair transaction rate: IFR (Instant Fulfillment Rate) deposits/withdrawals, p95 scoring latency.
Operating: TTD/MTTM (detection/mitigation), share of manual escalations.
Financial: chargeback rate/recovery, saving on support, reducing "extra" retrays.
Trust: NPS to statuses and explanations, share of self-completed verifications.
9) Reference architecture
Event Bus → Stream Aggregator → Online Feature Store → Scoring API (rules + ML + graphs + sequences) → Decision Engine (zel ./yellow/red.) → Action Hub
In parallel: Graph Service, Payment Orchestrator, XAI/Compliance Hub (logs/versions/reports), Observability (metrics/trails/alerts).
10) Cases "from practice"
Structuring for KYC limits: a series of leads 5-10% below the threshold → yellow, capping and deepened KYC.
Mule ring: dozens of accounts share 3-4 wallets and one IP pool → red, frieze, graph investigation.
Account-teikover: new device + new method + fast large output → red, forced password change, confirmation of ownership of the method.
Honest record win: EVT is normal, there are no connections → green, instant conclusion, public status - zero complaints.
Night "overheating": cancellation of withdrawal for the sake of a deposit, overbets → RG-branch: limit/pause/Focus, promo - pause.
11) MLOps and reliability
Versioning of data/features/models/thresholds; reproducibility, lineage.
Drift and calibration monitoring; shadow runs, fast rollback.
Data chaos engineering (gaps/duplicates/delays) → graceful degradation, not failure.
Sandboxes for auditors (replays of historical periods), feature flags by jurisdiction and channel.
12) Implementation Roadmap (6-9 months)
Months 1-2: 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, sequence models, integration with a payment orchestrator.
Months 7-9: Calibration by Market, Federated Learning, Chaos Tests, Regulatory Sandboxes, IFR/TTD/MTTM Optimization
Transaction AI analytics is a nervous system of trust. It combines rules, statistics, ML and graphs to separate honest operations from risks, speed up payments and make every step clear. Winning are those who build a system with four principles: speed, accuracy, transparency and ethics. Then transactions work like clockwork - for players, for business and for the regulator.