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Benefits and Risk Involved in AI-powered Fraud Detection
Artificial intelligence (AI) can be considered to be the latest most advanced technology for risk management. AI has provided new and improved ways to tackle fraud detection by enhancing the risk management strategies, tools, and software. Various business sectors are adopting AI to prevent, detect, and mitigate crimes related to cyberspace.
Banks and large business corporations employ AI to recognize and limit money laundering and loan frauds. Similarly, corporate companies are using AI-powered software to detect employee theft and unauthorized trading. However, with its advanced algorithms, AI comes with a fair share of risks attached to it. Therefore, using AI to thwart online frauds is a distinct approach in risk management only when the benefits outweigh the risks of fraud detection.
Benefits of AI in fraud detection
Although AI-powered tools and software allow companies to identify suspicious patterns that may go unnoticed otherwise, it is necessary first to understand how machine learning or other AI technologies could make their way into risk management. This will enable risk managers to evaluate a strategic fit of the AI technologies before embarking on a journey of employing it in risk management.
For instance, banks and financial firms have eliminated their traditional tools to make way for AI tools that enable them to conduct a multilayer-deep learning analysis of customers’ online footprint. This has not only assisted banks and financial firms in reducing the fraudulent customer activities but has also helped them in recognizing the false positive alerts.
Risks involved in fraud detection by AI
As risk managers examine how AI helps an organization in risk management, they should also consider the internal or external threat that may arise due to this. AI fraud detection should not be conducted in isolation, especially if there is an event for which the tool has not been trained, as it may trigger the alarm even when no rules are being broken.
Furthermore, employing AI to collect public data for better risk management may fuel an outcry among citizens, and an organization could lose its credibility among the people. Thus, business organizations should ensure that they remain attuned to the public interest before applying deep learning, machine learning, or other cognitive technologies for crime and fraud detection.