Machine Learning for Risk Management

Machine learning and artificial intelligence (AI) can help risk management and compliance professionals increase productivity and efficiency while reducing cost and errors. Risk management departments help companies identify, monitor, and minimize risks across the entire scope of the organization. This might include spotting fraudulent or suspicious activities, analyzing business systems, and making sure that all departments comply with regulatory guidance. Implementing machine learning into risk management workflows can help identify risks faster and collect and analyze your data in less time, helping you respond faster to threats.

Recent studies into how AI models can improve the efficiency and accuracy of risk management work suggest positive results. According to a 2024 study published in the Engineering Science and Technology Journal, AI models for evaluating credit risk can outperform traditional methods by 20 percent and detect anomalies in market risk management with 30 percent increased speed and accuracy. The same study found a reduction in false positives for fraud detection by 60 percent, suggesting much more accurate results [1].

Despite these benefits, only 9 percent of compliance and risk managers are actively using AI, with another 21 percent experimenting or learning to use AI [2]. This gap between the potential benefits and implementation is driven in part by a lack of understanding of how machine learning and AI skills can benefit compliance roles. 

Explore applications for machine learning in risk management, such as risk identification, data analysis, and process automation, as well as machine learning for risk management examples in the financial, manufacturing, and supply chain industries and sectors with high levels of regulation.

How is AI used in risk management?

Machine learning in risk management can help you identify, analyze, and address potential risks proactively and more quickly. Machine learning can help you analyze unstructured data to understand patterns and uncover potential outcomes in a fraction of the time it would take a human data analyst or risk management professional to manually calculate. That’s if the analyst knew what unstructured data they were looking for; the ability to draw insights from unstructured data means that machine learning can explore patterns in an open-ended way and identify potential outcomes your team wasn’t aware of. 

Risk identification and fraud detection

Machine learning can help organizations identify potential fraud and other types of risks, including cybersecurity threats or environmental, social, and climate risks. By understanding the underlying patterns of legitimate user behavior and transactions, a machine learning algorithm can flag events that fall outside those normal parameters. A few examples of how you can use machine learning to identify potential risks include: 

• Identifying fraud: Machine learning can monitor transactions and user behavior in digital spaces to look for suspicious behavior or potentially fraudulent financial activity. 

• Identifying environmental, social, and governance (ESG) risks: Machine learning algorithms can look at large quantities of unstructured data to help you identify risks in the environmental, social, or governance climate of your industry. When new events occur, your machine learning algorithms can help predict how these changes will impact your company. 

• Identifying cyberthreats: Another type of risk that machine learning can help you identify is the risk of cyberattacks or privacy breaches. In addition to monitoring the patterns of your network traffic, machine learning can help simulate security events to help your team prepare for when attacks happen. 

Data analysis and interpretation

Machine learning and AI offer powerful capabilities for understanding data, identifying patterns, and providing an interpretation of the results. Organizations in any industry can gain significant insight from data in their industry if they have the tools and time to capture and understand that data. Machine learning’s ability to gather actionable insight from unstructured data makes it a well-suited tool for extracting and understanding data from a wide range of sources. 

When it comes to risk management, a few primary ways that professionals use machine learning include to analyze the risk of investment decisions, understand the potential impacts of business strategy, forecast future needs and run predictive analysis, and analyze the credit of potential customers or screening individuals for other purposes. 

• Investment decisions: You can use machine learning to predict what individuals will do, how the price of financial products will fluctuate, or other key market indicators based on patterns in historical data.

• Forecasting and data modeling: You can use machine learning to understand the risks associated with your organization’s future revenue projections or other types of data modeling. If real-time data starts to deviate from projections, it can send you an alert to notify you of changes. 

• Customer credit analysis and screening: Machine learning can provide powerful tools for analyzing customers for credit risk or other criteria. Machine learning can find patterns in financial data, such as past credit behavior, to generate a risk profile for each potential customer or individual. 

Process automation

In addition to the examples about what machine learning and AI for risk management can accomplish, you can also use machine learning to complete these tasks automatically. You can use machine learning to automate repetitive tasks or pair this kind of automation with other artificial intelligence for algorithms that can make nuanced decisions. Machine learning can help you automate managing the compliance process, drafting policy and procedure documents, and automating operational risk management, such as cybersecurity.

• Automate the compliance process: You can use machine learning to automate tasks involved with compliance, such as processing documents and compiling data.

• Automate policies and procedures: You can use machine learning to update the policies and procedures your organization uses as regulations or best practices change. 

• Automate cybersecurity: Machine learning can monitor your cybersecurity data and take automated action if potentially suspicious activity occurs, including alerting you so you can take action faster. 

Machine learning in risk management examples

One of the biggest industries benefiting from machine learning in risk management is the financial industry. Financial professionals analyze data for insights and use those insights to make decisions, which is one of the core ways that you can use machine learning and AI to improve and automate your processes. However, many other industries benefit from machine learning for risk management. Explore examples of what machine learning in risk management can look like in different industries: 

• Managing risk in supply chains: You can use machine learning in risk management for supply chains. The logistics of moving goods and supplies can be complex and involve many different supplies and layers of variables that impact efficiency. Machine learning can analyze this unstructured data and find ways to optimize and streamline your supply chain for a smoother and more efficient process. 

• Managing risk in manufacturing: In manufacturing, machine learning for risk management can look for patterns and understand how machines operate to identify potential signs of malfunction and alert a human supervisor. Advanced warning before a machine malfunctions may allow technicians more time to make repairs or maintain the equipment to avoid a potentially costly breakdown.

• Managing risk in industries with more complex regulation: In industries with high regulation, such as health care, human resources, or banking, the organization could develop a virtual compliance expert using machine learning and generative AI with training in the company’s specific compliance needs. You could ask the AI expert questions about compliance to understand what you need to do, and the AI could monitor compliance and alert you when you need to take action. 

Managing the risk of AI 

Machine learning for risk management is different from AI risk management, which refers to the process of managing the risks associated with AI. Companies are adopting AI solutions at an incredible rate, introducing new risks such as the increased risk of cyber threat that comes from sharing data with AI, as well as the ethical issues that AI can raise. Machine learning offers benefits that make it worth using the technology despite the increased risks, including increased productivity, efficiency, and the ability to encourage innovative ideas. An AI risk management framework can help your organization think through risks and make a plan to reduce risks and implement AI solutions in a thoughtful way.

Learn about machine learning on Coursera

Machine learning for risk management could help organizations identify and mitigate cybersecurity risks, operational risks, financial risks, and more. To learn more about working with machine learning to increase efficiency, productivity, and accuracy, you can find programs to help you start learning on Coursera. For example, you can explore the Machine Learning Specialization offered by Deep Learning.AI and Stanford University, which gives you the opportunity to learn to apply best practices for ML development and use unsupervised learning techniques for unsupervised learning including clustering and anomaly detection. Or, explore the IBM Machine Learning Professional Certificate, where you’ll have the chance to learn how to master the most up-to-date practical skills and knowledge machine learning experts use in their daily roles.