How Global Players Use AI for Risk Management and How You Can Too
A staggering 80% of business models are currently at risk, according to global consultancy firm McKinsey’s latest data. This statistic underscores the unavoidable presence of risk in any company. More importantly, they occur in various forms, including supply chain vulnerabilities, shifting consumer preferences, product defects, geographical instability, and unforeseen events such as political or natural disasters.
With the global race to integrate artificial intelligence (AI) in all forms of business, can the technology aid in risk assessment and management?
Risk management vs risk assessment
To put it simply, risk assessment involves identifying, analyzing, and evaluating risks for a business. Once the risks are identified, the risk analysis stage begins. It helps companies understand the impact of those risks on business operations.
In the risk evaluation stage, companies see the severity of each risk and the consequences of not eliminating that risk.
On the other hand, risk management is a process that takes place after risk identification. It involves how to treat, control, and mitigate the risks identified.
In other words, risk assessment is a part of risk management. Businesses need to analyze and evaluate to build their risk management strategies.
For risk assessments, companies often turn to AI technologies such as user and event behavior analytics (UEBA), which detect, analyze, and respond to threats.
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Pros and cons of using AI for risk management
In terms of advantages, AI can process large amounts of data that, done manually, would be prone to human error and not feasible. Using AI for risk management means processing tons of data to identify anomalies, patterns, and risks.
AI can also forecast potential risks, allowing companies to conduct proactive risk mitigation and management.
Using predictive analytics can help companies predict trends based on available historical data.
In finance, for example, investment companies use machine learning to predict market fluctuations and credit risks, allowing them to make better investment decisions for the business and for their customers.
Roughly 81% of C-Suite executives, in the banking and financial services industries, believe AI is important to their company’s future success, as per research by AI-computing leader NVIDIA.
Automation and real-time analysis are also among the advantages of using AI in risk management. Using AI, companies can automate data analysis, especially routine tasks, thereby speeding up the analysis process and reducing human error.
Real-time analysis can also provide better and faster solutions for time-bound problems, especially those that require faster response.
No solution is free of disadvantages. While using AI in risk management, companies should heed the potential downsides of AI and accordingly the results.
For example, one of the biggest challenges of AI in business, including risk assessment and management, is biased and incomplete data. Both of these will impact the AI technology’s performance in your organization, resulting in inaccurate risk assessments, and, accordingly, skewed outcomes.
Another common problem is the ‘black box’ challenge, where companies struggle to uncover how an AI system reached a particular conclusion.
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Three examples of AI being used in risk management
Using AI in risk management and assessment is not new. Companies around the world are using various AI models to manage and mitigate various risks.
AI in financial risk management: JPMorgan Chase
For years, American multinational company and one of the world’s largest banks JPMorgan Chase has been using AI in risk management. The entity developed its Contract Intelligence program ‘COiN,’ using machine learning, to review commercial loan agreements.
This task usually took JPMorgan Chase’s loan officers and lawyers “thousands of hours” of work. With AI, the process takes seconds to complete at much higher accuracy.
Not only does the COiN program evaluate risk factors, but it also reduces risks and financial loss resulting from contractual oversight.
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AI in Cybersecurity: Darktrace
British cybersecurity firm Darktrace uses AI to detect and respond to cyber threats in real time. The company’s flagship Enterprise Immune System uses machine learning to understand user and device patterns in an organization. It, then, detects anomalies, or deviations, indicating cyber threats.
Darktrace’s AI-powered cyberthreat detector ensures companies can mitigate risks and threats faster, reducing damage.
AI in Supply Chain: IBM & Maersk
As one of the world’s largest IT companies, IBM, partnered with Danish shipping and logistics company Maersk to develop a blockchain-based shipping solution.
Using AI, the software called ‘TradeLens’ aims to manage and reduce supply chain risks. The AI algorithms analyze data to predict potential disruptions, including weather delays or holdups in customs, and mitigate them.
