Financial compliance involves many requirements: AML monitoring, KYC updates, IFRS reporting, and regulatory requests from the Central Bank. Manual processes don't scale. The compliance officer headcount grows linearly with transaction volume. We deploy AI that processes 10x–100x more transactions with the same headcount. This reduces false positive rates and the risk of human error. Our experience includes over 50 RegTech projects with an SLA guarantee of 99.9% for reporting modules. With 8+ years of experience in AI compliance, we guarantee robust solutions. This translates to annual savings of $200,000–$500,000 for a mid-sized bank processing 5 million transactions. Implementation costs typically start at $150,000 for basic AML monitoring. Our integrated offering covers all key areas: AI compliance finance, AML automation, KYC automation, regulatory reporting AI, ML transaction monitoring, GNN financial monitoring, false positive reduction, automation 115-FZ, and Expected Credit Loss ML within a comprehensive RegTech system.
What Problems Does AI Solve in Compliance?
AML Transaction Monitoring
The baseline level uses Central Bank rules. For example, suspicious indicators per Regulation 375-P include cash transactions exceeding regulatory thresholds. There are also transfers to non-resident individuals exceeding thresholds and transit schemes. The ML level uses behavioral anomalies with Isolation Forest:
from sklearn.ensemble import IsolationForest from sklearn.preprocessing import StandardScaler import pandas as pd class AMLTransactionMonitor: def __init__(self): self.isolation_forest = IsolationForest( contamination=0.02, # expect ~2% anomalies n_estimators=200, random_state=42 ) self.scaler = StandardScaler() def build_entity_profile(self, entity_id, transactions_90d): """Profile of typical customer behavior over 90 days""" return { 'avg_daily_volume': transactions_90d['amount'].sum() / 90, 'avg_transaction_size': transactions_90d['amount'].mean(), 'top_counterparties': transactions_90d['counterparty'].value_counts().index[:5].tolist(), 'typical_hours': transactions_90d['hour'].value_counts().index[:3].tolist(), 'typical_countries': transactions_90d['country'].value_counts().index[:3].tolist(), 'velocity_std': transactions_90d.resample('D')['amount'].sum().std() } def score_transaction(self, transaction, entity_profile, peer_profiles): """ Suspiciousness score for a transaction: 1. Deviation from the customer's own profile 2. Deviation from peer group profiles """ features = self._extract_features(transaction, entity_profile, peer_profiles) X = self.scaler.transform(features.reshape(1, -1)) anomaly_score = self.isolation_forest.score_samples(X)[0] # Convert to probability (0→no risk, 1→high risk) risk_score = 1 / (1 + 10 ** (anomaly_score + 0.5)) return float(risk_score) Graph-based network analysis uses GNN (Graph Neural Network). It identifies money flow through 3–5 legal entities to the ultimate beneficiary. We use NetworkX and PyG. Nodes represent accounts, legal entities, and individuals. Edges represent transactions. The GNN detects typical "clones" of patterns from historical cases.
KYC / EDD Automation
OCR and CV extract data from passports. Tools include Tesseract and EasyOCR. Face matching uses ArcFace with 99.5%+ accuracy. Liveness detection uses 3D depth map or blink challenge. Sanctions lists include OFAC, EU, and RF. Continuous KYC uses NLP to scan news. Changes of address or director trigger reverification. We also use Altman Z-score for financial degradation of legal entities.
Why ML Is Better Than Rules for AML?
Rules yield 90–95% false positive rate. This means 19 out of 20 flags require manual review. ML with profiling reduces this to 50–60%. GNN with expert overlay reduces it to 30%. As a result, compliance officers spend 4–5 times less time on false positives. We use MLOps: Weights & Biases for experiment tracking, MLflow for model versioning, and automated data drift monitoring.
How We Reduce False Positive Rate
The main problem of naive rules is 95%+ false positives. ML reduces FPR to 60–70% at the same recall. It does this by contextualizing. The same operation for different clients has different risk. Peer group comparison and historical feedback on closed "not suspicious" cases are used. Overall, this yields a 4–5x reduction in manual work for compliance officers.
| Approach | False Positive Rate | Data Volume Required | Implementation Complexity |
|---|---|---|---|
| Central Bank Rules | 90–95% | Low | Low |
| Statistical Models | 70–80% | Medium | Medium |
| ML with Profiling | 50–60% | High | High |
| GNN + Expert Rules | 30–50% | Very High | Very High |
Example model evaluation: after training, the model is saved in a model card along with precision/recall metrics on test data and an audit trail.
Regulatory Reporting
Automation of Central Bank Forms
| Form | Frequency | Automation Level |
|---|---|---|
| FinMon 4936-U (PFR) | Daily | Full |
| OBDUL (Beneficial Owners) | On event | 80% |
| 802-P (Capital Adequacy) | Monthly | 70% |
| IFRS Reporting | Quarterly | 60% |
IFRS 9 — Expected Credit Loss
We use an ML model for PD (Probability of Default) with LightGBM. It uses logistic regression on borrower financials. It includes staging (Stage 1/2/3) and forward-looking adjustment with macroeconomic scenarios.
Automation of Operations Verification Under 115-FZ
Complete cycle for suspicious transaction processing:
- System flags a transaction (ML score > 0.7 or rule).
- NLP auto-summary: "Operation of client X: transfer 2.4 million RUB to LLC Y (registered 3 months ago), unusual counterparty, 5x excess of normal turnover."
- Compliance officer: Accept/Reject/Request info (with AI draft of the request).
- Upon confirmation: automatic generation of FES/FSR for Rosfinmonitoring.
- Saving documentation for Central Bank inspections.
What's Included in the Work
- Audit of current compliance processes and data sources.
- Design of AI system architecture (with MLOps and scalability in mind).
- Development and training of ML models (AML, KYC, ECL) with model cards.
- Integration with core banking / CRM via REST API.
- Testing on historical data, A/B test in parallel mode.
- Deployment on client infrastructure (on-premise or cloud).
- Team training: 2–3 workshops, documentation, instructions.
- Warranty support for 6 months, SLA for reporting modules 99.9%.
Development timeline: 5–9 months for a comprehensive RegTech platform with AML, KYC, and automated reporting. Free project evaluation after completing the brief. Get a consultation from a RegTech engineer. Contact us for details.
Our services include AI compliance finance, AML automation, KYC automation, regulatory reporting AI, ML transaction monitoring, GNN financial monitoring, false positive reduction, RegTech system, Expected Credit Loss ML, and automation 115-FZ.