Artificial intelligence has secured its spot in the FinTech ecosystem — making machine learning the chief AI advancement for companies to watch, particularly as it relates to cybersecurity and fraud mitigation efforts for financial institutions.
Need more proof? Just follow the money.
Companies are spending capital hand over fist on researching, developing, and implementing AI and machine learning technology. In 2016, $5 billion in venture capital investments went toward machine learning alone, and corporate investment in AI overall is predicted to triple in 2017. Not keeping up? Now may be the time to invest in machine learning tech.
At its core, machine learning is a type of AI that uses algorithm-based data analysis to draw conclusions, make predictions, and/or learn about potential additional programming. Essentially, the technology "learns" about customers based on their patterns of behavior — what they buy, where they buy it, what time they typically make certain types of purchases, etc.
For banks and credit unions, properly integrating machine learning technology into their business practices can help them proactively distinguish fraudulent activity more quickly and efficiently, and also detect fraud by analyzing data patterns and flagging suspicious financial patterns.
In addition to the aforementioned general business benefits machine learning offers, there are two major advantages that are of special interest to banks and credit unions:
And that's just one example.
As those within the financial industry know, FinTech moves at the speed of light. An innovation is introduced and, in the blink of an eye, it becomes old news as another disruptive technology comes into play — often not to take its place, but to build on the most recent industry improvement.
This is why it's especially important for banks and credit unions to find new ways (and new partners) to help them implement machine learning into their systems in order to take a proactive approach in managing and mitigating fraud.