AI-Driven Regulatory Compliance in Capital Markets

Data Quality Challenges in Capital Markets

Persistent issues in financial services despite standardized data models:

  • Heterogeneous handling of trade attributes across organizations

  • Regulatory requirements for trade data normalization

  • Implementation variations between systems

End-to-End Solution Architecture

Four-stage AI pipeline for regulatory compliance:

Data quality workflow

Fig. 8 Multistage error resolution process:

Stage 0: Raw Trade Data

  • Source: FpML XML from counterparty systems

Stage 1: CDM Conversion

  • Transformation: JSON via DRR engine

Stage 2: Regulatory Mapping

  • Output: ESMA EMIR reports

Stage 3: Rule Validation

  • Process: Automated EMIR rule checks

Stage 4: Error Resolution

  • Method: Chatbot-assisted diagnosis/correction

Implementation Example

Error resolution case

EMIR compliance error resolution for missing reportSubmittingEntityID:

Error Context:

  • Common compliance failure point

  • Critical field for entity identification

Resolution Process:

  1. LLM identifies missing mandatory field

  2. Provides correction guidance (marked with green indicators)

  3. Generates compliant field value

Technical Notation:

  • XML Schema: FpML 5.10

  • Validation Rule: EMIR Art.9(5)