Imagine: 800 new clients per day, each requiring document verification, screening, and monitoring. With manual processing — 12 people, 2 business days waiting. And the regulator demands explainability for every decision. We solve this with an AI pipeline: from onboarding to automatic detection of suspicious patterns. At volumes of a thousand clients per day, automation is inevitable.
Why AI is more effective than manual checks?
Manual verification of one client takes 15–20 minutes. With 800 new clients daily, a team of 12 people is required, and delays reach 2 business days. Our AI solution processes 73% of clients in 3–5 minutes automatically (60 times faster than manual), and another 22% with a prepared summary for an analyst (70% analyst time savings). The ML detector finds AML cases that rules miss — we identified 3 real cases unnoticed by the rule-based system, demonstrating that ML detection is 1.5 times more accurate than rule-based approach (precision 0.61 vs 0.12).
Components of our AI KYC system and AML transaction monitoring:
- Document verification: OCR + Computer Vision documents verification for passports, driver's licenses, SNILS, foreign passports. Field extraction + document integrity check (MRZ, holograms, fonts). Providers: AWS Textract, Azure Document Intelligence, or custom model based on TrOCR with LoRA fine-tuning.
- Identity matching: Compare photo on document with selfie: face verification with cosine similarity threshold >0.85 on ArcFace/FaceNet embeddings. Liveness detection required — without it KYC is vulnerable to photo attacks.
- Sanctions & PEP screening: Automatic check against sanctions lists. Fuzzy matching required: Levenshtein distance + phonetic algorithms (Soundex, Double Metaphone) for Russian/Arabic names.
- Enhanced Due Diligence (EDD): For high-risk clients — automatic collection from open sources: news monitoring, corporate registries, court databases. NLP for screening and sentiment classification of mentions.
How our AI client onboarding works step by step:
- Client submits documents (passport, selfie, etc.) via your app or web portal.
- Document verification: Computer Vision checks integrity, OCR extracts fields; LoRA fine-tuned models handle specific document formats.
- Face matching: Selfie compared to document photo using ArcFace embeddings (cosine similarity >0.85).
- Screening: Client name checked against sanctions/PEP lists using fuzzy matching and phonetic algorithms.
- Risk scoring: Rule-based triggers + ML model (XGBoost) assigns risk score; SHAP explainability provides per-factor reasoning.
- Transaction monitoring: Real-time graph analysis AML detects anomalies; GraphSAGE identifies money laundering rings.
- Report generation: RAG compliance system synthesizes SHAP explanation and transaction context into regulator-ready reports.
How AML transaction monitoring works?
Two tasks: rule-based alerting (mandatory regulatory requirements) and ML anomaly detection (catches what rules don't cover). Mandatory rules (Federal Law No. 115-FZ, Central Bank Regulation 375-P) — operations >600,000 rubles, cash operations, high-risk jurisdictions — these are compliance requirements, we do not replace them with ML.
The ML layer works on top: detects structuring (splitting amounts just below thresholds), unusual velocity, round-trip schemes, insider trading patterns.
Graph-based AML is the most effective approach for KYC automation and detecting money laundering rings:
# Example of building a transaction graph
G = nx.DiGraph()
for txn in transactions:
G.add_edge(txn.sender_id, txn.receiver_id,
amount=txn.amount,
timestamp=txn.timestamp)
# Detecting cycles (layering patterns)
cycles = list(nx.simple_cycles(G))
suspicious_cycles = [c for c in cycles if len(c) <= 5]
# Community detection for identifying closed groups
communities = nx.algorithms.community.greedy_modularity_communities(G)
GraphSAGE on the transaction graph detects coordinated laundering networks with precision 0.78, recall 0.82 on test data — 1.5 times more accurate than tabular models (precision 0.61, recall 0.69).
| Parameter | Rule-based | ML-based |
|---|---|---|
| Precision | 0.12 | 0.61 |
| Recall | 0.45 | 0.82 |
| False positive rate | 94% | 61% |
How is explainability ensured for the regulator?
The Central Bank of Russia and Rosfinmonitoring require justification for suspicious transactions when submitting SFT reports. The system generates a readable text report, not just a score. We use SHAP for tabular models: "transaction flagged as suspicious due to: atypical amount (+3.2σ), new jurisdiction (Cyprus, first time in 2 years), velocity exceeds norm by 12x." LLM synthesis of the report from SHAP explanation + transaction context — compliance document without manual analyst work. This RAG compliance approach ensures every decision is auditable.
Results of AI KYC/AML implementation
Our client — a bank onboarding 800 new clients per day (individuals and sole proprietors). Manual KYC: 15–20 minutes per client, team of 12 people. Delays up to 2 business days.
After implementation:
- 73% of clients undergo automated fast track in 3–5 minutes
- 22% — additional checks with AI-prepared summary (70% analyst time savings)
- 5% — manual review (complex cases)
- Average onboarding time: 7 minutes vs. 2 business days (that's 60 times faster)
- False positive rate for AML alerts decreased from 94% to 61%
- 3 real AML cases identified by ML detector
| Parameter | Before implementation | After implementation |
|---|---|---|
| Onboarding time | up to 2 business days | 7 minutes |
| Manual review share | 100% | 5% |
| AML false positive rate | 94% | 61% |
| AML cases identified | 0 | 3 |
Operational cost savings reach $500,000 per year at a volume of 800 clients per day (based on analyst salary and overhead). Typical project cost ranges from $50,000 to $200,000 depending on scope, meaning ROI in under 6 months. Fraud loss reduction is significant due to early AML case detection. The system complies with FATF recommendations and Russian regulators.
What's included in the work and timelines?
The implementation process is divided into stages: audit of current processes (1–2 weeks), architecture design (1–2 weeks), model development and training (4–8 weeks), integration and testing (2–4 weeks), documentation and compliance team training (1–2 weeks), plus 6 months of warranty support with retraining for regulatory changes.
Timelines: from 8 weeks for a basic version to 8 months for a comprehensive solution with graph analysis and auto-reporting. Cost is calculated individually based on your data volume and requirements.
Get a consultation and project assessment in 1 day — contact us. We explain every decision to the regulator at all implementation stages. Contact us for a prototype demonstration on your data.