Another Avenue for Digital Twins: Behavioral Modeling for Banks


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

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

This is the takeaway from a survey of 222 financial services executives published by Altair, which shows these respondents are increasingly seeking to simulate transactions and money movement through their organizations. The majority of financial services organizations surveyed, 71%, say their organizations have already leverage digital twin technology. Financial services followed closely behind heavy equipment (77%), automotive (76%), and manufacturing (71%) in digital twin adoption.

Overall, respondents were the most likely industry group to say they are “highly knowledgeable about digital twin technology” at 64% – a number 14 points higher than the overall survey average (50%).

See also: Behavioral Analytics: Use Cases and Rapid Deployment

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

The financial services industry is using digital twin technology for behavioral modeling more than any other industry, the survey shows — 20 percentage points higher than the overall survey average. “By leveraging data from various sources, such as transaction history, social media, and demographic information, machine learning algorithms can identify individual preferences, risk tolerance, and financial goals,” the survey’s authors point out. “This information can be used to personalize customer experiences, offer tailored product recommendations, and provide customized financial advice.”


About Joe McKendrick

Joe McKendrick is RTInsights Industry Editor and industry analyst focusing on artificial intelligence, digital, cloud and Big Data topics. His work also appears in Forbes an Harvard Business Review. Over the last three years, he served as co-chair for the AI Summit in New York, as well as on the organizing committee for IEEE's International Conferences on Edge Computing. (full bio). Follow him on Twitter @joemckendrick.

Leave a Reply

Your email address will not be published. Required fields are marked *