The irony is not lost on how nonhuman interaction using machine-learning capabilities in financial technology could supply a more personalized member experience, as well as added security and operations performance.
The usage of AI, and its subset machine learning, is a rising trend among financial institutions as they seek to improve customer satisfaction, reduce inefficiencies, and fight fraud.
Jerry Melnick, president/CEO, of San Mateo, Calif.-based machine-learning analytics firm SIOS defined machine learning as a type of artificial intelligence and method of data analysis that uses algorithms to draw conclusions, make predictions or learn without additional programming.
SIOS iQ uses machine and deep learning technology to improve IT operations analysis, optimization, and performance resolution. It acquires a broad set of data in real-time across the infrastructure − CPU, storage, network, applications − and then applies machine-learning analytics to automatically identify abnormal behavior, its root cause and a recommended solution.
AI runs the gamut from voice recognition to neuronetworks to chatbots, Terence Davis, VP and chief architect at Santa Clara, Calif.-based Incedo, the technology unit of business group Indiabulls, said. “One of the subgroups is this machine learning.”
“We focus on innovation and the data lifecycle enabled by a revolution in hardware capabilities and technology around data,” Davis explained.
AI capabilities through integrated natural-language processing engines and chatbots can transform the mobile app user experience.
“Chatbots are one of the more popular of the artificial intelligence technologies because they enable a conversational experience for customers,” Antonio Sanchez, Product Marketing Manager at Austin-based mobility solutions company Kony, said.
Hoboken, N.J. headquartered NICE Actimize’s new product ActimizeWatch, is a cloud-based analytics optimization solution, which leverages machine learning and the cloud to provide proactive fraud analytics optimization and consortium data sharing.
“What we do is monitor the performance of financial institutions’ fraud analytics. We are able to change or optimize analytics very quickly and deliver them back with the ultimate goal of making fraud detection solutions and financial institution as agile as the fraudsters,” Rivka Gewirtz Little, director of fraud product marketing, at Hoboken, N.J. headquartered NICE Actimize said.
Read more about artificial intelligence and machine-learning in the June 7, 2017 print issue of CU Times.
