AI-Driven RegTech: Automate Regulatory Reporting

Development of AI-Driven RegTech System: Automating Regulatory Reporting

A bank with 500 billion rubles in assets prepares 12 regulatory reports monthly. One missed deadline—a fine of up to 1% of capital (Central Bank of Russia). Manual cross-checking of six systems takes three days and still detects only 60% of anomalies. AI-driven RegTech changes this: a pipeline extracts data from documents in minutes, automatically reconciles sources, and flags deviations before submission.

We develop AI-powered RegTech platforms that automate data collection, report generation, and deadline control. Our solutions cut compliance operational costs by 30–50% (average savings of $500K per year for mid-size banks) and minimize the risk of fines. Request a pilot—we adapt the system to your data sources and regulatory requirements.

How AI Solves Regulatory Reporting Problems

Regulatory reporting is one of the biggest operational cost centers for financial organizations. A mid-sized bank spends up to $2M annually on manual compliance (Deloitte RegTech Survey). AI automation changes this: from extracting data from unstructured documents to automatically submitting reports in the regulator's format.

Regulatory Reporting Landscape

Russian regulator (Central Bank of the Russian Federation):

• Reports of the 0409 series (prudential reporting for banks): daily, weekly, monthly
• XBRL format for some reports
• Federal Tax Service: tax reporting
• Rosfinmonitoring: suspicious transactions (AML/CFT)

International requirements (ESMA, FATF):

• EMIR/DTCC Trade Reporting (derivatives)
• MiFID II Transaction Reporting
• FATCA/CRS (tax exchange)
• Basel III COREP/FINREP
• SWIFT compliance (KYC-registry)

Automating Data Extraction with NLP

A significant portion of data for regulatory reports resides in unstructured documents: contracts, client questionnaires, court rulings, corporate documents. The NLP pipeline includes OCR, NER, relation extraction, and conversion into structured report fields. We use fine-tuned BERT for financial-legal texts—extraction accuracy of 88–93% for standard documents. Rare cases are handled through few-shot learning and chain-of-thought prompting.

Data Reconciliation and Anomaly Detection

Data lineage ensures every value in a regulatory report is traced to its source. AI automatically builds a lineage graph by analyzing ETL and SQL transformations. Multi-source reconciliation automatically validates data across systems: Core Banking ↔ General Ledger ↔ Risk System ↔ Regulatory Report. ML detects not only exact mismatches but also "suspiciously close" values that indicate hidden errors.

Anomaly Detection—an ML model reviews the report before submission for unusual values: deviations from historical patterns (±3σ), violations of cross-report control ratios, anomalous increases or decreases. This prevents fines before they occur. An AI-driven pipeline processes 1,000 documents in 2 hours—12x faster than a team of five analysts.

Why a Comprehensive RegTech Approach Is Better

Separate solutions for each report lead to chaos. A comprehensive platform unifies data lineage, automatic report generation, change monitoring, and deadline control. Our experience—30+ projects for banks and fintech companies—confirms that integrating all modules yields the best results. AI-driven reconciliation reduces errors fivefold compared to manual methods.

Regulatory Change Management

Change monitoring—an NLP pipeline monitors official regulator sources (CBR website, consultant.ru, official gazettes). It classifies changes as applicable or not, extracts specific requirements, and performs impact analysis using a knowledge graph: regulation → report → fields → data sources. Automatic assessment: "New requirement affects 3 reports, 7 data sources, 2 systems." Timeline management consolidates all regulatory deadlines into a single calendar with automatic reminders and dependency tracking: Report B depends on Report A data, so A is prepared first.

Technical Stack

Data ingestion:
  - Core banking: Oracle Database → JDBC
  - ABS: proprietary formats → ETL
  - Market data: Bloomberg feed

Processing:
  - Apache Airflow (scheduling)
  - dbt (SQL transformations with lineage)
  - Great Expectations (data quality)

Output:
  - XBRL generator (python-xbrl)
  - CB API (CBR XBRL format)
  - SWIFT API
  - Internal PDF reports

Monitoring:
  - Grafana for reporting status dashboards
  - PagerDuty for deadline alerts

What's Included in the Project (Deliverables)

• Detailed audit report: current processes, pain points, regulatory map
• Custom NLP pipeline configuration for your document types
• Data lineage setup for all source systems
• Reconciliation rules engine (configurable)
• Integration with regulatory APIs (XBRL, SWIFT, CB)
• Monitoring dashboards and alerting
• Operator training (up to 5 sessions)
• 3 months of post-launch support

How We Implement RegTech: Stages of Work

1. Analytics and design: survey current processes, map reports and data sources.
2. Develop NLP pipeline: fine-tune BERT, configure OCR, integrate with Document Management System.
3. Implement data lineage: automatically build data provenance graph.
4. Set up reconciliation: cross-system validation, custom rules for complex cases.
5. Integrate with regulatory APIs: XBRL, SWIFT, CB API.
6. Test on historical data: quality >95% F1 for extraction.
7. Documentation and training: model cards, operator instructions, one month post-launch support.

Results and Timeline

Pilot project (2–3 sources): 2–3 months. Full platform (5–10 sources): 6–10 months. Metrics: extraction accuracy 88–93%, 80% reduction in reconciliation time, 30–50% reduction in operational costs.

We guarantee compliance with current CBR, FTS, and international regulator requirements. Our solutions are certified and have FSB licenses. Our experience in banks and fintech companies minimizes risks. Contact us to discuss a pilot project. Get a consultation on adapting the system to your data sources.