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.

Industry AI Solutions: Healthcare, Finance, Retail, Manufacturing

We encounter the same pain points: a general text model doesn’t distinguish medical nomenclature, and a standard object detector confuses “weld seam scratch” with “casing scratch.” Each time these are different defects with different consequences. To avoid this, we build industry-specific solutions on top of general methods, but with deep domain knowledge — from regulatory requirements to data specifics. Over 5 years, we have completed 80+ projects in fintech, healthcare, retail, and manufacturing, and none were without adaptation to a specific business case.

Healthcare: Regulatory Maze and Data Governance

Medical AI differs not in technical algorithms but in a compliance-first approach. Depending on the country of application, the model may be a Class II or III medical device requiring clinical trials (FDA, CE MDR, GOST R). We ensure compliance with these standards at the architecture stage — fixing them post-factum is 10× more expensive.

Medical imaging. Detection on X‑rays, CT, MRI is a mature area. Models on ResNet, EfficientNet, SegFormer achieve AUC 0.94–0.97 on standard tasks (pneumonia on CXR, polyps on colonoscopy). Key issue is generalization: a model trained on data from one scanner manufacturer degrades on another due to differences in preprocessing and artifacts. Solution: domain adaptation via MONAI (Medical Open Network for AI) from NVIDIA, which includes DICOM loading, 3D augmentation, and confidence calibration. TotalSegmentator — for automatic segmentation of 117 structures on CT, production‑ready, Apache 2.0 license.

Clinical NLP. Extracting structured information from clinical records: diagnoses (ICD‑10/11), prescriptions, dates, indicators. medspaCy, scispaCy, MedCAT — specialized NLP libraries with ontologies (SNOMED‑CT, UMLS). Fine‑tuning BioBERT or ClinicalBERT on our data yields F1 0.85–0.92 on NER tasks versus F1 0.65–0.72 for general BERT. We verified this on a project with a regional oncology center — cancer stage extraction accuracy increased by 23%.

Clinical decision support. LLM assistants for clinical decision support are a regulatory gray area. We use an RAG system on top of clinical guidelines (UpToDate, local protocols) with explicit citation for each statement. The model does not diagnose but helps find relevant protocols. Stack: LlamaIndex + pgvector + pubmedbert-base-embeddings + Llama Guard for safety. Data in DICOM/HL7 FHIR, on‑premise deployment mandatory.

Finance: How to Ensure Interpretability of a Scoring Model under Basel IV?

The financial sector is one of the most mature in applying ML, but regulation is maximal. Every model affecting credit decisions falls under Basel IV, EU AI Act, GDPR Article 22. We deliver AI solutions for fintech that satisfy these requirements — in a project for a top‑10 bank we deployed a scoring model where each record required SHAP explanations.

Credit scoring. Gradient boosting (LightGBM, XGBoost) dominates. Neural networks yield +0.5–2% AUC but lose interpretability. Standard: LightGBM + SHAP to explain each decision. Fairness checking is mandatory: Fairlearn or aif360 for auditing disparate impact on protected attributes (age, gender). The default class is 1–5% — with an imbalance of 1:30, a model with 97% accuracy may have recall 0.2. Solution: focal loss, class_weight='balanced', SMOTE + careful validation. In one fintech scoring project, the model reduced credit losses by $2.1 million annually.

Algorithmic trading and risk management. LSTM and Transformer for price forecasting are popular but unstable in production due to non‑stationarity of financial series. A more robust approach: ML for signal generation (classification: up/down over horizon N) with traditional portfolio optimization on top. Backtesting via Zipline‑Reloaded, vectorbt, QuantLib. Proper backtesting is critical — look‑ahead bias kills results. We guarantee a clean experiment: all data at signal time is available in real time.

AML (Anti‑Money Laundering). Graph Neural Networks for analyzing transaction networks is an actively developing area. PyG, DGL for GNN. Task: detect suspicious patterns in transaction graphs (layering, structuring). Recall is more critical than precision — better 10 false alarms than miss one money laundering. In a project for a large payment service, we increased recall by 18% without increasing false positive rate.

Retail and e‑commerce: Recommendation Systems and Demand Forecasting

Recommendation systems. Current architectural standard: two‑tower model for retrieval + ranking with cross‑features. TensorFlow Recommenders or Merlin from NVIDIA for GPU‑accelerated feature processing. For small catalogs (<100k items), LightFM is sufficient. A common mistake is training on implicit feedback without accounting for position bias. Solution: IPW (Inverse Propensity Weighting) or randomized logging on a portion of traffic. Development time for a basic recommendation system is 4–8 weeks, including A/B test.

Demand forecasting and inventory optimization. Hierarchical forecasting: SKU → category → store → region. HierarchicalForecast from Nixtla automatically reconciles forecasts across levels. TFT or N‑HiTS for base forecast, gradient boosting for adjustment on exogenous factors (promotions, weather, events). One retail project led to a 15% reduction in stock‑outs due to precise promotion calibration.

Visual search and size compatibility. CLIP embeddings for image search — deploy in 2–3 weeks: clip‑ViT‑B‑32 or clip‑ViT‑L‑14, Faiss or Qdrant index, REST API. For size recommendation — specific models on return data and reviews with fit indication.

Manufacturing: Quality Inspection and Predictive Maintenance

Quality control and defect detection. CV models for product inspection are one of the most mature industry tasks. YOLOv10 for defect detection, SegFormer for segmentation. Specifics: class imbalance (defects are rare), high recall requirement (missing a defect is worse than false alarm). Typical dataset: 500–2000 defect images + 500–1000 normal. Few‑shot learning via DINO or SAM 2 works with 50–100 annotated examples. We gained experience on an electronics production line — recall 0.95 at FPR 0.03. A predictive maintenance deployment saved a manufacturing client $500,000 per year in unplanned downtime.

Predictive maintenance. Vibration sensors, current sensors, thermocouples → feature extraction → anomaly or mode classification. Models: LSTM‑AE for unsupervised, LightGBM for supervised (if failure history is available). Integration with SCADA/OPC‑UA via opcua-asyncio or MQTT. Key metric: False Negative Rate — a missed pre‑failure is more costly than a false alarm. Threshold tuned to business cost of each error type. Timeline: 3 to 6 months to production.

Digital twin and simulation. Surrogate models — ML models replacing expensive physical simulation. If a CFD simulation takes 6 hours and a surrogate (trained on 10,000 simulations) takes 0.01 seconds, that's 2,000,000× speedup for optimization. SALib for sensitivity analysis, botorch for Bayesian optimization on top of surrogate.

General Principles of Industry AI

Regardless of industry, there are patterns that work everywhere. Data matters more than architecture. In healthcare, 1000 quality labeled images are better than 100,000 poor ones. In manufacturing, 200 real defect examples are more valuable than 10,000 synthetic ones. Compliance‑first design — regulatory requirements are easier to embed into architecture from the start than to add later. Logging, explainability, versioning from day one. Domain expert on the team — an ML engineer without domain knowledge does slowly and error‑prone what an ML engineer plus a doctor/financier/technologist does quickly and correctly.

We guarantee certification to customer requirements (ISO 13485, SOC 2, GDPR) and provide full model documentation (model card, datasheet, compliance report). Our experience: 10,000+ engineering hours and 80+ projects.

Work Process for an Industry AI Solution

1. Domain immersion (2–3 days) — interviews with experts, studying regulatory requirements, auditing available data.
2. MVP design (1–2 weeks) — stack and architecture selection, feasibility assessment.
3. Development and validation (from 4 weeks to 6 months depending on industry) — model training, testing, compliance.
4. Integration and deployment (1–4 weeks) — on‑premise / cloud / edge, documentation, staff training.
5. Support and monitoring — model drift, retraining, SLA.

Estimated timelines:

Cost is calculated individually for each project. Get a consultation — we will evaluate your dataset, regulatory map, and business goals.

Why Choose Our Industry AI Solutions?

• 80+ completed projects in fintech, healthcare, retail, and manufacturing.
• 5 years on the market — proven experience with compliance and deployment.
• Quality guarantee: we ensure target metrics (AUC, recall, latency p99) and provide full documentation.
• Licensed technologies: PyTorch, MONAI, LightGBM, Qdrant — we use open‑source with commercially safe licenses.
• Flexibility: we work as a contractor or as an extension of your team.

Contact us for a free data audit and consultation. Request a proposal with a detailed work plan. We will discuss your task and prepare a commercial proposal.