By Shankar Bhaskaran
The increasing use of Artificial Intelligence (AI) has transformed industries across the board. Organizations are harnessing AI tools to automate operations, enhance decision-making, and gain a competitive advantage. The GRC landscape is no exception. AI is reshaping the approach towards GRC functions, bolstering governance, risk, and compliance management, and helping organizations manage the scale and complexity of these functions.
This is a game-changer for modern businesses, given that organizations heavily invest resources in GRC in terms of cost and workforce. As per a PwC Global Risk Survey: India highlights, 70% of Indian business leaders are increasing their overall spending on risk management technology.
Yet, many organizations continue to grapple with the formidable task of managing the intricacies of diverse GRC demands, shifting regulatory landscape and constant scrutiny from regulators. Today’s risk landscape is more complex than ever. Regulators are trying to keep pace by introducing new regulations, policies and modify existing ones to safeguard businesses and consumers. The changing risk and governance landscape poses a problem for Chief Risk Officers (CRO). CROs attempting to protect their organizations from risks are often hamstrung by less than optimal risk management methods, practices and tools available in their organizations. While some organisations may have deployed the most sophisticated risk management solutions, the vast majority are still limited by a manual and siloed in approach, and find it difficult to cope with the pace of emerging risks, multitude remediation methods and the volume of data that requires analysis to be truly insightful. For example, how does an organization proactively and effectively assess, predict, and protect itself from emerging risks arising from a global pandemic? Or a calamity like war or climate change?
The answer lies in bringing to the fore new technologies, like AI.
AI brings a host of possibilities that can reshape how organizations tackle GRC, spanning from risk identification, evaluation and remediation to compliance guidance and oversight to higher degree of automation of manual tasks, control testing and basic decision making to enhanced reporting with actionable insights. AI-powered GRC solutions have immense potential to streamline operations, enhance productivity, improve the management of risk and deliver targeted business outcomes.
AI’s capabilities are still nascent and we are just scratching the surface. Already today we can see it encompass preventive, predictive, and diagnostic methods that enhance GRC processes. This empowers businesses not only to flourish but also to gain maximum benefits within a dynamic market environment. AI tools are adept at forecasting events and anticipating real-time developments by analyzing vast datasets, in a lot of cases, even better than humans can.
Here’s a look at how AI is transforming various aspects of governance, risk, and compliance:
Role of AI in Risk Management
While India was relatively sheltered from the global banking crisis, there has been an indirect impact in various sectors, including tech, markets and startups. The problem has also underscored the need for policymakers and business leaders to collaborate and look at comprehensive solutions for the challenges faced by the banking industry.
AI technologies are transforming how financial institutions handle risk management. They have empowered the latter to efficiently mine vast distributed datasets, uncovering insights that enhance risk mitigation and customer ROI. By using AI, organizations in the BFSI sector can create more precise risk models than those relying solely on conventional statistical analysis.
AI-based risk management enables banks to predict, evaluate, and mitigate risks better. Additionally, AI tools can help identify patterns within risk events, recommending effective control measures.
Role of AI in Managing Regulatory Compliance
AI and machine learning algorithms in regulatory compliance can enhance data governance, bolster continuous control monitoring capabilities, and automate compliance checks. AI-driven systems offer real-time insights and proactive alerts, enabling compliance functions to address issues efficiently. AI-powered GRC systems can predict, act and mitigate risk vectors, before they fully materialise.
Managing controls in large organizations, where thousands of controls are tested, presents a challenge. Incomplete and Redundant testing of controls hinders proactive risk minimization, and AI algorithms can optimize the efficiency of the process. These algorithms provide insights into control effectiveness through data analysis and trend identification.
AI tools can also accurately extract obligation text from regulations, enabling human-in-the-loop review of individual obligations and empowering organizations to focus on impact analysis. Natural Language Processing (NLP) algorithms are key in processing and analyzing text-based data from sources such as regulatory documents and policies.
Role of AI in Cyber Risk Management
AI is swiftly emerging as an indispensable tool in cyber GRC. AI-driven systems empower organizations to bolster their cyber defence capabilities by delivering advanced threat detection, predictive analytics, and real-time monitoring.
AI models can undergo training to gauge deviations in system behaviour that may signal potential cyber risks. This enables the timely identification of security breaches or operational glitches. AI-driven threat intelligence excels at spotting emerging threats and formulating effective mitigation strategies.
Generative AI in GRC
Last but not least, generative AI like ChatGPT and Bard are set to be significant disruptors in GRC. Generative AI models are leveraged in various domains, from producing reports and condensing risk assessment policy findings to generating innovative strategies for fraud risk mitigation. They excel as interactive chatbots, guiding end users through GRC processes.
AI also comes with its set of challenges. Some key aspects to look out for are:
- Ensuring the use of appropriate and high-quality models and data sets for training
- Ensuring transparency in the way the AI models work to deliver results
- Ensuring neutrality of the AI models through managing bias/prejudice effectively, especially in case of regional and demographic nuances
- Anticipating and managing regulatory changes to AI technology.
In Conclusion
With businesses facing increasing demands to deliver results amidst ever-evolving risks, AI-driven GRC technology is the undeniable way forward. Cloud-based AI-enabled GRC software with robust algorithms and proactive intelligence for managing GRC functions will pave the way for these solutions.
AI will empower businesses to navigate uncertainty beyond routine risk occurrences. With AI, organizations can enhance their ability to address unforeseen risks and remain resilient despite continual market shifts.
(The author is Shankar Bhaskaran, Managing Director – India, MetricStream, and the views expressed in this article are his own)
