Unleashing the Power of AI for Governance, Risk, and Compliance (GRC)
We’re living in an era of technological disruption and regulatory uncertainty, and the role of AI in GRC is changing faster than ever before. AI is no longer a pipe dream but is critical in helping organizations navigate complex rules, regulations, and risks. In this article, we’ll look at how AI is changing GRC and how businesses manage compliance, mitigate risks and strengthen governance strategies.
Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing the way businesses operate and manage risks. Integrating AI and ML in various industries has led to significant improvements in efficiency, accuracy, and decision-making, including in Governance, Risk, and Compliance (GRC) transformation. However, it is essential to ensure proper governance and management of AI to mitigate potential risks. This article will review some advancements of AI in GRC and explore the associated risks and benefits.
Use cases of AI in GRC
Predictive Analytics for Risk Mitigation
Predictive analytics helps financial institutions manage operational risks, such as system failures, compliance breaches, and cybersecurity threats. AI algorithms can analyze vast amounts of data and identify potential risks in real time, helping organizations make more informed decisions.
Consider the case of credit risk analysis. AI enables a bank to assess a customer’s real-time data and social media sentiment to more precisely predict the probability of default, leading to better credit risk management. JPMorgan Chase has already implemented advanced machine learning models, resulting in decreased default rates. [i] This approach allows for a more accurate analysis of customer data and social behavior, ultimately improving the bank’s ability to manage and mitigate credit risk.
Artificial Intelligence algorithms can anticipate forthcoming trends and patterns, enabling businesses to preemptively manage risks before they become more severe. This can result in more effective risk mitigation strategies, while also assisting institutions in comprehending and readying for potential market changes, ultimately decreasing the probability and effect of adverse events.
Fraud detection and prevention
By analyzing large datasets, AI algorithms can identify abnormal activity, detect anomalies and flag potential cases of fraud or money laundering. This is particularly advantageous for insurance, e-commerce, and finance industries, allowing organizations to detect and prevent fraud earlier and reduce financial and reputational damage. Additionally, AI can assist with internal investigations by swiftly identifying relevant data and patterns, streamlining the process. Furthermore, AI can identify vulnerabilities in systems and applications, enabling preventive measures to be implemented before any issues arise.
HBSC has implemented Anti Money Laundering AI (AML AI) to analyze their extensive data and detect potentially suspicious activity with greater accuracy. As a result, the system has identified a significant increase, ranging from two to four times, in the amount of suspicious activity compared to their previous system.[ii]
Automating Compliance Monitoring
Financial regulations have evolved into complicated, multifaceted structures that dictate the activities of both financial institutions and businesses. As these regulations become increasingly intricate, the demand for AI-powered solutions to navigate the complex landscape of compliance requirements grows. Basel III, a regulatory framework aimed at creating more robust banks and banking systems, brings with it a host of challenges related to data, technology, modeling, and demonstrating adherence to regulatory standards. Its implementation requires careful consideration and strategic planning to ensure compliance and successful integration within the financial industry.
Artificial Intelligence can streamline the process of monitoring regulatory compliance by analyzing large volumes of data and pinpointing potential compliance issues. This enables organizations to keep pace with evolving regulations and prevent expensive penalties.
Furthermore, AI can aid in generating compliance reports automatically, minimizing the time and effort needed for manual reporting. It also plays a role in managing data privacy regulations, such as GDPR, through the identification, classification, and protection of sensitive data within an organization’s system.
Automation of Repetitive Tasks
One of the key benefits of AI in GRC transformation is the automation of repetitive tasks. AI-powered systems can perform tasks such as data entry, document analysis, and risk assessment with speed and accuracy, freeing up valuable time for GRC professionals to focus on strategic initiatives. Automation reduces manual work and eliminates the need for filling and managing spreadsheets for endless hours. This not only trims down costs but also boosts efficiency.
Automation has still a long way to go [iii]
Data Management
Data management is of high importance for data quality, compliance, informed decision-making, scalability and leveraging data-driven technologies. It provides data integrity, compliance, and operational efficiency, and allows organizations to strategically use their data.
AI can streamline and improve many Data Management processes. I will mention here only a few:
Data Integration — AI can integrate data from different sources, formats and structures. This is particularly important for GRC where data was traditionally “siloed” across the business units, inevitably leading to data duplication and inaccuracy.
Data cleaning — AI systems can handle even the most complex and large-scale data cleaning tasks, validating and cleaning data in real time. It can find and fix dataset issues, duplications, and inconsistencies. Clean data results in a more effective decision-making process.
Data search and retrieval — As organizations are overflowing with data, AI becomes increasingly important in the search and retrieval of data, especially unstructured data or data trapped in silos. AI provides precise results by understanding natural language and context.
Challenges and Considerations
Data Quality
An AI system is only as good as the quality and quantity of data it ingests. Data poisoning is the greatest threat to AI because poor-quality data not only produces poor results but also trains the model to go off the rails for all future calculations and predictions. This concept in AI that highlights the importance of input data quality is called “Garbage in, garbage out” (GIGO).
Image of the famous Garbage in — Garbage out [iv]
To build trust in data that powers our AI models, we need to observe data in real-time. The key to trust is our ability to track each data event from the moment it is created, enriched, processed and distributed. The data life cycle begins in the middle or back office of every organization. The back office is the data factory. It collects, enriches, validates, packages and distributes all data. If we don’t review our back office data management processes and bring transparency and trust to the entire life cycle of data, we will continue to chase the dream of AI transformation, and it will never happen.
The impact of poor data quality in AI can be staggering [v]
Data Privacy and Security
As AI progresses, there are growing concerns about the potential threats to personal data privacy. AI models are trained on enormous datasets including personal information, such as names, addresses or phone numbers. It utilizes machine learning to process data, make autonomous decisions, and adapt to changes without human intervention. Its presence spans every industry, from health care to finance, raising privacy concerns and challenging traditional norms of personal data protection.
Not even the biggest names in the tech industry are safe from data security issues. Only last year AI researchers from Microsoft exposed 38 terabytes of private data including passwords, internal messages, and corporate secrets. This happened during routine open-source AI training material updates.[vi]
Even though there are a myriad of concerns related to security and privacy concerning AI, there is a growing number of solutions that facilitate the safe usage of AI. Data anonymization, data encryption, data classification, and tokenization are some of the protection measures to safeguard data.
Privacy laws will need to continuously adapt as AI rapidly evolves to address new ways personal data is collected and processed. It’s crucial to ensure the protection of individuals’ privacy in this evolving landscape.
AI Integration and Implementation
For many companies, the integration and implementation of AI cause significant challenges.
AI integration means that artificial intelligence platforms are incorporated into services and processes to improve automation and efficiency. This necessitates compatibility with existing systems but also fine-tuning AI models to fit the relevant area. AI integration isn’t a simple plug-and-play process. It requires a thorough analysis of the current system to identify any loopholes and determine how AI can address them. It’s important to approach this with careful consideration and planning.
Challenges include also the need for AI and ML solution providers with extensive expertise, the need for personnel training, and the establishment of robust AI governance at the company level.
Ethics and Bias in AI Decision-Making
Artificial intelligence systems can only be as impartial as the data on which they are trained. The biases inherent in the data can be inadvertently introduced into AI algorithms, leading to biased results. Unintentional biases in the collection, classification, or algorithmic design of data can result in discriminatory or unethical outcomes.
Among the most famous cases of bias is Amazons recruiting engine which showed bias against women. The computer models were trained on historic data covering a 10-year period, which was dominated by men. The model concluded that males are preferred over women as candidates, so far as even a mention of the word “women” in the resume would downgrade a candidate’s chance of getting hired. [vii]
Organizations must address these biases early on and guide how to use AI systems in a way that is fair and ethical. There is a need for regular audits and continuous monitoring to identify and correct any biases.
Transparency and explainability
Another risk with AI is its potential lack of transparency. Machine learning algorithms rely on patterns in data to make decisions, but they often lack clear explanations for their decisions. This is the so-called “black box’ problem and can raise questions about transparency, accountability, and compliance with regulations. Lack of transparency can be especially problematic in sectors like finance or healthcare, where comprehending the rationale behind a decision is vital.
Organizations must make sure they have human oversight and the capacity to interpret AI-generated results.
One company introduced a new automated system after it had been acquired, which eventually ended up firing an employee without the knowledge of any of its superiors. Following this it automatically set off a chain of actions, logging and locking the employee out of the work system and all other systems up to requesting the security guards to escort him out of the building. It took the company three weeks to figure out what happened and put the employee back into the system.[viii] This is a cautionary tale and underlines the need to have human oversight in the AI world.
Accountability
If we are increasingly going to use the assistance of or delegate decisions to AIs, we need to make sure these systems are fair in their impact on people’s lives, that they are in line with values that should not be compromised and able to act accordingly, and that suitable accountability processes can ensure this. [ix]
The question about accountability in AI is not easy, as there are several entities involved including User, Developer, Vendors, Data Providers and Regulatory Bodies. Accountability in AI is a collective effort, an interplay of robust legislation and solid company policies, including procedures, protocols and best practices.
An example is an AI system incorrectly predicting market trends, causing significant business losses. Was it the fault of the AI developers building the system or the data providers who fed the algorithm with incorrect data or even the users for blindly trusting the AI system without additional oversight?
Benefits of AI in GRC
There are always pros and cons to any technological advancement. We have looked at some of the issues and challenges of AI but no one can dispute the myriad of benefits it brings.
Cost Reduction and Resource Optimization
AI technology can automate repetitive tasks, leading to increased efficiency and lower operational costs. By utilizing AI for data analysis, businesses can make more informed decisions, which can result in cost savings and better resource allocation. Additionally, AI can improve predictive maintenance, reducing downtime and maintenance costs for machinery and equipment. Overall, AI’s ability to streamline processes and provide valuable insights can lead to significant cost reductions and optimization of resources across various industries.
Improved Customer Experience
Chatbots, utilizing conversational AI and Natural Language Processing technology, can generate personalized responses, aiding in finding suitable solutions for customers. These tools also alleviate strain on customer service staff, ultimately enhancing productivity. By leveraging AI, businesses are better equipped to handle customer interactions, ultimately leading to improved customer satisfaction and overall operational efficiency.
24*7
AI-based systems are designed to operate around the clock, providing accessibility whenever needed. Unlike humans, they do not require breaks and can maintain productivity consistently. These systems are programmed to handle repetitive and monotonous tasks efficiently, without experiencing fatigue. This 24/7 availability and productivity make AI based systems valuable for businesses and organizations seeking to optimize operations and streamline workflows.
Big data processing
AI is equipped with advanced skills and algorithms to efficiently process and draw insights from large volumes of data. It can swiftly identify and extract relevant information necessary for analysis. AI can further process this data through interpretation and transformation, allowing for quick and accurate conclusions to be made. This ability enables AI to handle big data promptly, providing valuable insights for decision-making and problem-solving.
Smart decision making
Artificial Intelligence has long been utilized to enhance decision-making in business. AI technology is capable of facilitating data delivery, analyzing trends, ensuring data consistency, generating forecasts, and quantifying uncertainties to enable informed decision-making for companies. By avoiding the replication of human emotions in AI programming, it maintains impartiality and aids in optimizing business efficiency. AI’s ability to remain objective and provide valuable insights makes it a valuable tool for strategic decision-making in the business world.
Enhanced Risk Prediction and Prevention
Enhanced Risk Prediction with AI involves leveraging advanced algorithms and machine learning techniques to analyze and interpret vast amounts of data. By integrating AI into risk assessment processes, organizations can gain deeper insights into potential risks and make more informed decisions. AI can identify patterns and correlations that may not be evident through traditional methods, allowing for more accurate and proactive risk management. This technology has the potential to revolutionize risk prediction across various industries, ultimately improving overall safety and security.
Reduction of human error
Automating operations has proven to be highly effective in reducing the occurrence of human errors. AI serves as a prime example of how machines can minimize mistakes and expedite processes. Unlike humans, computers are less susceptible to external influences and are capable of performing tasks with a high degree of accuracy. They can also complete tasks at a faster rate than their human counterparts. By embracing automation, businesses can enhance efficiency and reliability in their operations while also minimizing the potential for human error.
The Future of AI in GRC
Organizations face increasingly complex challenges related to GRC, including regulatory compliance, data privacy, cybersecurity, and reputational risks. AI can play a crucial role in GRC transformation by providing advanced analytics and automation capabilities that can help organizations identify and mitigate risks, ensure compliance, and improve decision-making. By leveraging AI, businesses can gain a more comprehensive understanding of their GRC posture and make informed decisions.
Change management and building a culture of innovation are essential for successful GRC transformation with AI. The implementation of AI-driven GRC practices marks a paradigm shift in how organizations approach risk assessments, governance, and compliance. Therefore, it is essential to have a change management strategy in place to ensure a smooth transition. Building a culture of innovation involves fostering a mindset of continuous improvement and experimentation, encouraging employees to embrace new technologies and ideas. By creating a culture of innovation, organizations can unlock the full potential of AI for GRC transformation.
[i] K. Doherty — JPMorgan Says AI Technology Is Starting to Generate Revenue, 29.11.2023, BNN Bloomberg
[iii] https://www.snaplogic.com/resources/research/the-state-of-data-management
[iv] N. Shakoor, D. Northrup, S. Murray, T. Mockler — Big Data Driven Agriculture: Big Data Analytics in Plant Breeding, Genomics, and the Use of Remote Sensing Technologies to Advance Crop Productivity, 2019
[v] https://www.snaplogic.com/resources/infographics/data-distrust
Snaplogic “The State of Data Management — The Impact of Data Distrust”
[vi] R. Naraine — Microsoft AI Researchers Expose 38TB of Data, Including Keys, Passwords and Internal Messages, 18.09.2023, SecurityWeek
[vii] J. Dastin, Insight — Amazon scraps secret AI recruiting tool that showed bias against women, 11.10.2018, Reuters
[viii] J. Wakefield — The man who was fired by a machine, 21.06.2018. BBC News
[ix] AI HLEG, European Commission (2019) A definition of AI: main capabilities and disciplines. https://ec.europa.eu/digital-inglemarket/en/news/definition-artificial-intelligence-main-capabilities-and-scientific-disciplines.
