Enterprises are trying to march towards a risk-free future by implementing AI in risk management. But, no one knows whether AI will prove to be a game-changer or become a risk itself.
Risk management is a process of identifying, evaluating, and minimizing the threats to an enterprise’s capital and earnings. The process of risk management is important for an enterprise as it helps it to identify and mitigate the risks in their environment. Without risk management, enterprises would be blindsided by the risks surrounding their company and may face heavy losses. The risk management process basically includes five steps: identifying, analyzing, evaluating, treating, and monitoring the risk. AI technology, with its ML algorithms and deep neural networks, can help simplify risk management steps. Features of AI like prediction and content monitoring can help achieve effective risk management for enterprises. But the complicated algorithms of AI are also claimed to be the factors contributing to increased risk by many organizations and individuals. With such a debate on AI, no one can say whether AI in risk management will increase risks or prove to be a game-changer.
How AI is Both a Risk and a Way to Manage it
Despite many benefits, AI is also a source of some new risks that must be identified and managed by the enterprises using it. One of the most common risks associated with using AI technologies is algorithmic bias. AI algorithms identify patterns in data and use them to make predictions, rules, and decisions. Hence, the output of AI is completely dependent on the data used to train them. And, if the patterns in data reflect some existing bias, then the AI decisions might amplify that bias and produce undesirable results. Hence, the outcome of AI can be jeopardized if the input data is incomplete or of poor quality.
There are many such risks associated with the use of AI, but almost all of the risks can be managed. For instance, the risks mentioned above can be handled by ensuring the use of complete and accurate data to train the algorithms.
AI can help mitigate the common risks associated with enterprises. AI’s machine learning algorithms can play a prominent role in risk management. Algorithms can create sophisticated tools that can be helpful in monitoring content and activities in real-time. With so many benefits of using AI, the technology is proving to be a compelling fit for its use in enterprise risk management.
How Enterprises are Using AI in Risk Management
As described above, data is a critical input in risk management strategies. And, IoT devices are helping enterprises collect an enormous amount of unstructured data. So it follows that an increase in data can have a huge impact on how companies are approaching the practice of risk management. AI technology can greatly assist in the process of risk management by improving efficiency and reducing everyday costs in the risk management process.
Assistive AI in Risk Management
AI tools can provide assistance for completing many different tasks. Some general examples of AI assistance are recommendations for shopping, image recognition for biometrics, and chatbots for customer service. AI assistance applications can help risk managers in their day to day activities in a more productive way.
AI can classify fraud incidents into different categories depending on the amount of risk associated with it. Content monitoring abilities of AI can help detect any unusual activities on the customer’s account. Machine learning can then categorize fraudulent activities on the basis of previously recorded fraud scenarios. According to the level of risk associated with previous scenarios, ML algorithms can further determine the severity of fraud. Determining fraud severity will help the fraud prevention team to make decisions like which fraudulent activity to act on first.
AI systems can learn from past instances of fraud in an organization and elsewhere to suggest actions that can mitigate future risks. Different recommendations to mitigate risks can help risk managers to approach a case of fraud that they might have never thought before.
Predictive AI in Risk Management
There are many technologies that can easily help to mitigate the consequences of threat or fraud, but preventing them is more important than mitigating their consequences. And prevention of fraud becomes easier when there are means of predicting it. AI systems can be trained on previous data to predict upcoming risks.
Predictive analytics can itself transform risk management. And, in conjunction with self-learning algorithms of AI, prediction can help detect and prevent fraudulent activities in an organization. AI is long adopted in almost all the industries for fraud detection. Risk managers access more and more data in the enterprise to audit and look for any possible fraud. The major benefit of predictive AI is that it can scan through a tremendous amount of data and past patterns to detect vulnerabilities. AI helps risk managers to analyze the organization’s data more accurately and get a better understanding of the impact different risks can have on the enterprise.
Enterprises can leverage the benefits of AI prediction tools to detect risks associated with its workforce. For instance, with content monitoring, AI can monitor the transfer of data packets over the network. Algorithms can detect any unusual activities with data packets caused by any employee and alert the administrators of the enterprise. Administrators can thus stop any incident of fraud from occurring and take necessary actions against the employee.
Smart Audit with AI in Risk Management
Not only the administrators and risk management team, but also the third line defense team of enterprises can benefit from AI. For instance, AI can enhance the audit process to help the audit and compliance team in an organization. Algorithms can accurately extract information from contracts such as leases, that is needed for verification. AI can then match the details with data in the database and verify them. Employees can thus speed up the process of auditing and verifying details of a customer or other businesses.
Suggestions can be given by AI on the area that needs to be audited. Depending on the success rates of past audits, AI systems can suggest the area of an organization that needs more audits as compared to other areas.
When it comes to identifying blind spots, responding to an ongoing threat, and detecting the cyber threat, machines have always remained ahead of humans. AI systems can collect information about all the events and activities within an organization and detect breaches, if any, with the help of big data and ML algorithms. Once a threat or breach is detected, AI systems can alert the administrators about the same. AI systems can even start an investigation of the breach automatically and collect the necessary information about it. Administrators can use the information gathered by the AI system to investigate the breach further.
The implementation of AI in risk management can help risk managers to get deeper insights into information and data that otherwise might not be possible. Before implementing AI in risk management, it is necessary for developers to develop the system in such a way that it can deal with bias and other issues. Once the risks associated with the adoption of AI technology are dealt with, it can surely go mainstream in enterprise risk management.
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