Deep learning models more accurately identify inherent risks and propagate these insights as signals that banks can operationalize in downstream workflows. From fraud protection to customer loyalty, these models protect the signal-oriented universal bank against a revolving door of regulatory and antagonistic threats.
The evolution of banking is irrevocably tied to the development of novel methods to quantify and manage risk. Advances in communication and data processing technologies help banks surmount risks and satisfy customers’ changing needs.
We trace the evolution of banking through the ever-changing manner in which technology-enabled analytics techniques help mitigate risk.
Protect people and payments
Put data to work
Bad actors and threats of fraud are an ever-increasing concern in the global digital banking sphere. According to the Federal Trade Commission, consumer reports of fraud including identity theft and imposter scams rose 20% between 2020 and 2021 to 5.7 million instances. To protect customers and funds and maintain compliance with evolving regulatory policy, the signal-oriented universal bank is equipped with ready-built data solutions that maximize compliance and adapt to a changing financial landscape.