Unlike human agents, machine learning systems can scan through thousands of documents every second and draw meaningful conclusions out of huge data libraries. This helps fintech companies to make their activities a lot faster and minimize operational costs.
For example, JPMorgan Chase & Co. uses machine learning to simplify legal procedures and reduce legal workload. The program, called COIN, for Contract Intelligence, does the mind-numbing job of interpreting commercial-loan agreements that, until the project went online in June 2017, consumed 360 thousand hours of work each year by lawyers and loan officers.
Daniel Pinto, Co-President and Chief Operating Officer at JP Morgan, praised the standout performance of the entire system.
“As our clients grow in size and complexity, they require a financial partner who can provide the financing solutions they need, wherever they need them and however they want them delivered,” Pinto said in a recent letter to shareholders.
Machine learning can also write fintech-related content. Most companies need simple and factual reports with accurate data insights, and machine learning can get this job done quickly and efficiently.
Machine learning can help systems create credit scores and help fintech sales agents understand potential clients before sealing the deal, minimizing risky loans. It can also identify quality borrowers that have gone unnoticed before.
The secret lies in machine learning’s ability to analyze huge data libraries coming from many different sources simultaneously. The process stretches far beyond simple credit scores and income level information. While conventional methods still play their role in risk assessments, machine learning takes the extra step and analyzes tons of alternative information, including utility payments, social media accounts, health records (if available), cable and phone bills and rent payments. All of this information allows the machine learning system come up with extremely accurate credit scores. This is not something a human being can do, so the risk of borrowing money to insolvent people and organizations shrinks to the bare minimum.
“Initially, banks will experiment on using machine learning to improve their business operations (e.g. credit analysis, customer acquisition), but will be hesitant to build consumer-facing applications (e.g. customer service bots) due to concerns about performance and reputation risk,” said Ian Foley, CEO & Founder at AcuteIQ, in a collection of interviews published by Plug in Play.
Customer service is one of the first areas where machine learning is put to use. A Walker study found that customer experience will overtake both price and product as the key brand differentiator by 2020. Chatbot technology, powered by machine learning, can ensure around the clock customer service. Mobile Market Research states that 40% of Millennials interact with chatbots on a daily basis.
Machine learning also makes it easy to customize fintech offers and provide clients with personalized products. With so many customer-related inputs floating around the Internet, the system can analyze the existing preferences and predict future needs and expectations.
Machine learning systems analyze enormous data libraries to determine the entire history of stock exchanges and follow even the smallest price change parameters in order to sell stocks before price decrease or buy more stocks in case the price is about to go up.
Predictive analytics can also give machine learning the ability to augment fintech marketing operations across all channels of communication. The platform can quickly scan each client and gather publicly available information. This can improve the effectiveness of marketing campaigns.
As fintech companies continue to introduce cutting-edge IT solutions to the financial industry, machine learning will likely play an influential role in continuously improving business processes and services.
Tiffany Harper contributed this article. Follow her on Twitter @tiffany_harper. Views contained herein do not necessarily reflect those of Nutanix.
© 2019 Nutanix, Inc. All rights reserved. For additional legal information, please go here.