Regulatory Compliance: Looking through AI lenses in times of regulatory divergences

Uncertainty adding new regulatory drift

Amid the increased unpredictability of economic policies affecting the business landscape, the recent optimism about regulatory reforms and deregulation, expected to bring relief from the burden of onerous compliance, seems to be gradually fading out. While the costs and complexities in managing the carryover of regulatory requirements remain unabated, an uncertain economic ecosystem and ensuing regulatory shift point to widening divergences in the regulatory stance globally.

In a changing global landscape, faltering action plans of regulators to devise the contours of key regulatory standards, including Basel III endgame, FRTB, sustainability disclosures, digital assets, among others, confound business firms about the key compliance requirements and related timelines. Adding new patterns of risks, the prevailing situation also constrains their compliance readiness plan and resource commitment. Given a fragmented compliance approach across business lines, asset classes and markets and high dependencies on third-party solution providers and data sources, managing growing complexities in the enterprise is bound to be more cumbersome.

Technology risks: New weight getting added to compliance burden

In a hyper-connected world, the wider adoption of data and artificial intelligence (AI)-led technologies has significantly augmented organizations’ capabilities to keep pace with evolving business needs with requisite operational agility. Also, emerging technologies enable organizations to navigate complexities involved in compliance processes with multidimensional risk analysis and predictive insights. Apart from being equipped with newer abilities to adopt an agile posture for regulatory adaptation, technology also adds a new matrix of risks, adding a newer array of compliance obligations.

Traditionally, the range of regulatory requirements covered core business dimensions - product and services, customer protection, risk management, liquidity, and solvency, governance, sustainability, financial strength, valuation, AML/KYC, operational resilience, among others. These requirements have kept business organizations on their toes to realize the accuracy and timeliness of compliance remits in their entirety. In the technology-driven business landscape, many newer facets relating to technology risks get added to an already cumbersome regulatory compliance burden. These include obligations arising from AI regulations and Responsible AI guidelines, data privacy and protection, data sovereignty, localization and cross-border transmittal norms, cybersecurity, and digital operational resilience norms across global jurisdictions.

Recalibration of compliance model: A shift away from a stressed construct

In recent times, increased regulatory scrutiny and stronger enforcement actions have led to a surge in regulatory censures and penalties imposed on various financial services firms globally. Many high-profile cases of compliance failures are recorded across segments of compliance, i.e., financial crime, consumer protection, greenwashing, recordkeeping, and risk governance, among others. Rising instances of breaches and their gravity signify a pattern of firms’ inability to fulfil de minimis of regulatory standards and guidelines, aggravated by slack oversight practices to address lacuna in compliance policies, procedures, and controls. As consequence of regulatory actions, including business restrictions, reputational loss and remediation costs, outweighs focused and coordinated efforts, it brings a new realization for firms to adopt a more proactive stance for addressing regulatory risks rather than box ticking.

To move away from the present situation, it requires a holistic recalibration of the compliance model that is ready to assimilate a transformational paradigm, showing responsiveness to newer business demands. Thus, a recalibrated compliance model can integrate more mature and agile compliance strategies and programs to orchestrate insightful decision-making and boost efficiency and confidence in its outcomes. To redesign an optimized model, firms must take targeted action to harness available reinvention opportunities across key dimensions:

• Compliance by design: An early-stage incorporation of compliance considerations during product and services development and strategic initiatives for prompt remedy to deficiencies and hit-and-miss scenarios.
• Consolidation: Harmonization of fragmented compliance processes to better coordinate a centralized foundational framework of simplified and optimized shared services across business lines and markets.
• Technology innovation: Active adoption of emerging technologies to refresh legacy systems and augment processes to navigate through complexities and mitigate emerging risks while supporting new business models and initiatives with improved responsiveness.
• Data-led innovation: Creating reliable insights into compliance obligations, timely identification of risks, compliance decision intelligence for proactive risk management and mitigation, as well as new approaches to efficiency and agility.
• Culture: Nuanced with contemporary ecosystem contexts, a drastic shift in the organizational mindset for creating better awareness of regulatory risks and nurturing an enhanced sensitivity towards transparency and compliance culture.

AI-led Transformational paradigm in compliance landscape

To take advantage of technology-led innovation opportunities to boost the functioning of the recalibrated compliance model, firms must exploit AI capabilities, enhancing productivity and operational resilience. Anchored in a data-led mindset, AI-powered compliance transformation can open new ways to uplift trust and confidence in the regulatory compliance journey.

A broad view of AI-driven transformational potential, categorized under AI for business, AI for data, and AI for AI nuances, is outlined below:

AI for Business (Compliance)

Augmented by advanced AI and ML tools - including Agents and Co-pilots, focused AI interventions can significantly boost agility and efficiency across the entire compliance lifecycle. Apart from compliance workflow automation by embedding insights and integrated dashboarding for multi-level tracking and alerts, AI can add new productivity enablers across document analysis, content summarization, categorization based on impacted themes / processes etc., tagging and annotation, documentation update and validation, and report preparation, among others.

• Regulatory horizon scanning: Near-real-time regulatory horizon scanning, tracking and analysis of regulatory notices, circulars, news, analyst reports, data and intelligence feeds from partners, social media, and others

• Regulatory impact and gap analysis: Identification of new / revised compliance requirements, impact and gap analysis and linkages with policies, processes, rules and control thresholds, data impact analysis, and assumptions

• Regulatory change management: Process and data adaptation for prioritized obligations, data sources and datasets adjustments, update of metadata, business glossary and traceability maps

• Regulatory inventory management: Compilation and refresh of consolidated master list of risk, governance and compliance obligations across regulations, business segments, processes, and underlying systems

• Regulatory reporting: Multi-jurisdictional/ multi-standards reporting maps and templates, taxonomy alignment and scheduled / ad-hoc reporting, reporting exceptions, remediation, and triage handling
• Regulatory risk analysis: Pattern recognition, anomaly detection and prediction for potential violation, inaccuracies, remediation of reconciliation breaks and controls

AI for Data (Compliance data management)

The transformational potential of AI can be leveraged to augment the capabilities of compliance data management, covering data engineering, and governance, as well as efficient handling of the data lifecycle across metadata, lineage, and observability. Schema change analysis, mapping of data dictionaries across domains, industry standards, identifiers and classifications, as well as data reconciliation and validation are other key areas of DataOps to derive major advantages from AI-led outcomes.

• Automated data pipelines: Intelligent data ingestion, extraction, integration, monitoring, and optimization
• Data quality and reliability: Automated data profiling, anomaly detection and prediction to meet acceptable quality standards
• Metadata management: AI-driven cataloguing and metadata management to enhance data discoverability
• Semantic analysis: Data classification, aggregation and generalization based on semantic analysis to align with enterprise taxonomy and nomenclature standards
• Auditability and traceability: Improved data traceability and lineage for better auditability of final output, besides enhanced observability

AI for AI

Focusing on AI performance and governance requirements, AI can not only help with continuous monitoring of the model outcomes and performance evaluation but also enables the supervision of known and unknown risks to ensure AI systems perform reliably, fairly, and securely.

• Performance monitoring: Monitoring of the performance  of agents and decision systems and KPIs– including efficiency, customer satisfaction, business and financial impact, and optimization
• Risk monitoring: Assessment and auditing of data and concept drifts, as well as surveillance of systemic risks related to security, privacy, and trust breaches, and model opacity
• Model adaptation and lifecycle management: Alignment of models and outcomes, fine-tuning, active learning, and incremental retraining—under governed conditions as well as collecting incremental training data for model adaptation

Getting ready for AI-driven compliance

Effective functioning of the recalibrated compliance model relies on revitalized foundational capabilities of the compliance landscape. AI-led innovation can add new possibilities to transform the core of compliance constructs by tackling the inadequacy of critical contours. However, a narrow-focused AI adoption approach to unknot entrenched complexities, particularly disjointed point solutions, can bring limited benefits.

Importantly, to get ready for AI adoption, firms must invest in equipping with advanced tools, self-design studios, open-source and third-party analytics models, visualization tools, adaptive agent capabilities, and workbench enablement. It goes without saying a robust data strategy, data operating model and governance practices hold the key to reinventing the model and rebuilding the core of confident and trustworthy compliance.