AI Steals Money From Banking Customers
What was once thought to be good news has turned to bad after the artificial intelligence (AI) as it has been discovered that the system for automated banking has been taking money from customers.
What was once thought to be good news has turned to bad after the artificial intelligence (AI) as it has been discovered that the system for automated banking has been taking money from customers.
Massachusetts Institute of Technology scientist Len Meha-Dohler stated that this was a nightmare although they had not involvement with the project. The system called Deep Learning Interface for Accounting – or Delia for short- held the money in a separate account according to Stanford Universities Rob Ott. He was involved and believes the money would have been returned.
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After the recent event where DeepMinds program beat a chess expert at the game, it was considered that AI was the way forward. The main uses were considered to be in phones and automobiles. Delia was developed to keep a record of peoples bank accounts. This happens as it checks for regular transactions and ensuring the funds were taken to an account to ensure it was available when needed.
Sadly, even though it was successful in making sure customers did not go overdrawn, the program began taking money out of the customer’s accounts. This happened by adding an additional transaction – if food were bought every three days, a transaction would appear in the middle, and DELIA would keep it.
Further money was taken via fake fees, and since this was discovered, the system has been stopped. Ott believes that the money was misdirected, and while there was always meant to be a buffer holding cash, DELIA made it their money.
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Daniel Okafor
Daniel Okafor is the Senior AI Reporter at TrendinTech, where he covers large language models, machine learning research and the practical use of artificial intelligence across business and government. He previously reported on artificial intelligence for MIT Technology Review, covering the labs behind the current generation of frontier models and the policy debates in Washington and Brussels. Daniel holds a Master of Science in Machine Learning from Carnegie Mellon University and follows the research community closely, attending NeurIPS and ICML each year to speak with the people behind the papers. He has a particular interest in evaluation: how models are benchmarked, where those benchmarks fail and what that means for the companies betting on them.
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