Risk management, traditionally rooted in data and experience-driven decision-making, is experiencing a paradigm shift with the integration of artificial intelligence (AI). Given that businesses experience an annual loss of approximately $4.7 trillion due to fraud and governance issues, per the Association of Certified Fraud Examiners (ACFE), keeping up with the ever-increasing range of risks is imperative.
The expansion of AI has the potential to revolutionize the analytical processes of risk managers, reshaping how data is analyzed, trends are identified, and outcomes are predicted. AI enables organizations to rapidly gain insights, understand patterns, eliminate redundant efforts, undertake appropriate actions, and choose decision-making processes that enhance governance, risk and compliance (GRC).
AI as a Game Changer for the GRC Landscape
In today’s business landscape, organizations worldwide face increased complexity amid growing digital interconnectedness, dynamic risk scenarios, evolving regulatory landscapes, geopolitical uncertainties, and unforeseen events like the pandemic. Establishing a future-ready GRC framework becomes paramount, given the urgent need to respond to such events.
AI has emerged as a transformative force, empowering organizations to overcome siloed operations by promoting integration and harmonization of risks and controls. AI has significantly enhanced the speed at which teams handling risk, audit, compliance, and IT & cyber functions can access relevant insights, facilitating evidence-based decisions.
Robust AI algorithms excel at analyzing data and detecting unusual patterns and anomalies. With predictive analytics capabilities, AI can pinpoint emerging risks, their origins, and forecast their impacts. Alerting risk managers to potential dangers or breaches empowers them to proactively implement mitigation measures.
Applications of AI in Risk Management
AI applications in risk management cover various crucial functions:
Threat Analysis: AI helps aggregate and analyze threat intelligence data at scale. Machine learning engines process this data for likelihood calculations and risk predictability models. This proves extremely valuable for security teams grappling with the escalation of cloud account hijacking attacks and ransomware infections.
Fraud Detection: In industries like BFSI, AI supports fraud detection through text mining, database searches, social network analysis, and anomaly detection techniques. Machine learning engines assist in processing vast datasets to identify fraudulent transactions and activities, extending to areas like detecting the fraudulent use of cloud services.
Organizational Risk Reduction: AI contributes to risk reduction in the workplace by analyzing data related to workforce activities in high-risk environments. Models evaluate behavioural patterns before accidents occur, generating predictive scenarios to enhance safety procedures and prevent incidents. Additionally, they assist in managing people-related risks, such as identifying illegal or unethical behaviour through communications analysis.
Data Monitoring: AI-based analytics engines process data in cloud environments to classify and tag it based on predefined policies. This enables risk management and compliance professionals to identify sensitive data, ensuring robust security protections.
Compelling Use Cases of AI-Driven Capabilities for GRC
AI plays a crucial role within platforms prioritizing cloud-first practices, providing flexibility to construct diverse models or automate various GRC use cases. Regardless of the provider, these platforms can leverage pre-built AI-powered recommendations to enhance and automate GRC processes.
These AI systems deliver valuable recommendations by analyzing historical patterns, elevating user experience, and facilitating informed business decision-making.
Action Management: AI-driven solutions capitalize on semantic analytics and natural language processing to identify patterns in issues and actions across various programs, such as enterprise and operational risk, compliance, audit, third-party, or IT & cybersecurity. These systems offer recommendations for issue categorization based on semantic similarity, automatically suggesting duplicate issues, and optimal action plans by analyzing historical trends and business context.
Observations Volume: Addressing the challenge of managing a large volume of observations reported by the frontline, AI-powered recommendations automate the triage process. By classifying observations as cases, incidents, issues, or loss events, these recommendations significantly enhance the efficiency of the triage team.
Smart Policy Search: Leveraging AI, smart policy search tools simplify searching for policies through natural language processing (NLP)-based semantic search. This technology enhances search accuracy by discerning the searcher’s intent and contextual meaning.
Third Parties Risks: In the context of risk assessments, AI recommendations for third-party risk assessment automate extracting content from SOC2 and SOC3 reports, along with other questionnaires. These recommendations compute and rank third parties based on the number and type of anomalies identified in the reports, providing an efficient and objective risk-scoring mechanism.
The Way Forward for AI in GRC
AI is poised to evolve as technology progresses into a more refined, predictive, and seamlessly integrated tool across diverse industrial domains. Continuous innovations will likely yield sophisticated AI models, offering businesses deeper insights and more accurate risk assessments.
Some new cloud-based AI-driven GRC platforms that harness large language models and GRC knowledge graphs are emerging. These technologies, equipped with generative AI capabilities, offer cost-effective solutions by eliminating ineffective controls, minimizing control tests, and optimizing processes. Generative AI technologies will contribute to risk management by assisting in data monitoring, risk assessment, and facilitating communication with stakeholders.
AI is poised to assume a more significant role in future enterprise risk management programs. The increasing prevalence of cloud-based AI tools will offer risk management teams advantages in fast processing of extensive datasets, mitigating the constraints associated with traditional manual risk analysis, and more effective, efficient management practices.
As AI progresses, the GRC landscape will continue experiencing significant transformation. In their pursuit of staying ahead, organizations will increasingly unlock AI’s considerable potential to enhance GRC outcomes, gain a competitive edge, and foster stakeholder confidence.
The article attributed to: Shankar Bhaskaran, Managing Director – India, MetricStream
