Banks are increasingly using digital twins for behavioral modeling

Financial services organizations use digital twins and machine learning algorithms to identify behavioral characteristics including individual preferences, risk tolerance and financial goals.

While digital twins are typically associated with manufacturing plants, another sector is also pursuing the technology aggressively. Banks and financial institutions are embracing digital twins to address challenges such as security, fraud detection and behavioral prediction.

That’s the conclusion of a survey of 222 financial services executives published by Altair, which shows that these respondents are increasingly looking to simulate the transactions and movement of money through their organizations. The majority of financial services organizations surveyed, 71 percent, say their organizations already use digital twin technology. Financial services followed heavy equipment (77%), automotive (76%) and manufacturing (71%) in using digital twins.

Overall, respondents were the most likely industry group to say they were highly knowledgeable about digital twin technology at 64 percent, 14 points higher than the overall survey average (50 percent).

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The survey shows that the top three uses of digital twin technology include optimizing business processes (54%), digitally monitoring real-time behavior (51%) and predicting future behavior using predictive analytics (51%). “These functions allow teams and organizations to better prevent fraud, monitor and predict customer/borrower behavior, track customer satisfaction, and more,” say the survey’s authors.

The survey shows that the financial services industry uses digital twin technology for behavioral modeling more than any other industry – 20% higher than the overall survey average. “Using data from a variety of sources, such as transaction history, social media, and demographic information, machine learning algorithms can identify individual preferences, risk tolerance, and financial goals,” the survey authors noted. This information can be used to personalize customer experiences, provide tailored product recommendations and provide customized financial advice.”


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