Regulatory Services

AI for Regulatory Compliance

Regulatory

AI is transforming regulatory compliance by automating complex processes, enhancing efficiency, and enabling proactive risk management. Augmented by predictive analytics and risk assessment tools, AI led systems expertly analyse historical and current data to predict impending compliance risks with high levels of accuracy. Research indicates that predictive AI models for compliance risk management achieve over 85% accuracy in identifying potential failure points, as seen in financial crime monitoring systems.

Actionable insights for regulatory compliance

Through Natural Language Processing (NLP) frameworks, AI brings to table the ability to parse case histories and transactional notes, extracting actionable insights. This mirrors AI-powered legal document parsing tools, which swiftly analyze vast datasets to identify key information, improving decision-making and compliance efficiency.

Predictive compliance management

AI transforms compliance from reactive to proactive by leveraging predictive analytics to anticipate compliance failures. Similar to AI systems used in financial risk management, these systems and frameworks analyze historical data and trends to forecast risks, enabling preemptive measures and reducing regulatory breaches.

Our Services

Our regulatory services leverage advanced AI to navigate complex compliance landscapes with precision. We specialize in financial crime management, predictive compliance, and regulatory reporting, ensuring adherence to global standards like Basel III and GDPR. By integrating cutting-edge tools and data governance frameworks, we empower businesses with fraud detection, risk modelling, and automated reporting for proactive compliance management and operational efficiency.

Financial crime management

Identity, sanction and PEP screening: Automate identity verification, risk assessment and due diligence processes of individuals and businesses.
Transaction monitoring and fraud insights: Monitor transactions, identify suspicious activities and mitigate fraud to prevent unauthorized access to financial resources.

Risk & compliance and regulatory reporting

Risk modelling and calculations: Identify, assess and mitigate key risk areas – credit, market and liquidity risks, through risk modelling, limit setting and stress testing.
Consolidated Financial Reports, Capital and Risk Reporting: Create regulatory reports, ensuring compliance with diverse financial reporting standards and requirements like SEC, FRB and FFIEC.

Data governance and management

Data governance framework: Establish policies and controls to ensure data accuracy, security, compliance, and decision-making alignment
Master and metadata management: Centralize and standardize critical data entities for consistency, quality, and regulatory adherence across systems.
Data lineage: Track data flow and transformations for transparency, integrity, compliance, and streamlined troubleshooting

Regulatory platform implementation:

Financial crime management: Ensure effective, reliable and compliant financial systems through implementation and maintenance of FCM tools.
Risk and regulatory reporting: Enhance the accuracy and timeliness of the regulatory submissions through implementation and maintenance of regulatory reporting tools.
Data governance: Ensure that data is effectively managed, secured, and aligned with regulatory requirements, by implementing and maintaining data governance tools.

Our Accelerators

Frameworks with real-time digital front-end solutions, such as automated data capture using OCR and instant document verification through real-time processing, for expediting KYC customer onboarding process.

In-house, domain-specific data quality validation frameworks that significantly improve the model validity and integrity. Domain-based rules in simple English are converted into SQL and JSON-based quality rules using Gen AI, which are then integrated into Collibra DQ framework for seamless execution.

Advanced AI with contextual anomaly detection models that take the surrounding context into account, minimizing the chances of flagging legitimate actions as fraudulent, thereby reducing false positives

Comprehensive five-step quality assurance process (data source validations, reconciliations, variance reports, pre-and post-report validations), that ensures data accuracy before submission to regulators.