Fintech Uses Machine Learning to Outsmart Competition

A look at the rising role of machine learning in the fast-moving world of financial technology industry.

By Tiffany Harper

By Tiffany Harper December 24 2019

Financial technology, also known as fintech, depends on cutting-edge IT trends. Every time someone makes a mobile payment or uses a personal budgeting app, this happens in the fintech realm. These actions may seem as simple as tapping buttons on a smartphone, but beneath it all is a growing trend. To handle complex financial data analysis and transactions, fintech companies are increasingly turning to machine learning to deliver new services.

Global fintech market value is expected to reach almost $310 billion by 2020, according to The Business Research Company. This growth is mostly driven by innovations that bring automation, efficiency and higher productivity to traditional financial services.

“We are witnessing the creative destruction of financial services as it rearranges itself around the customer,” said fintech advisor Arvind Sankaran in a financial industry article about trends in banking. “Whoever does this in the most relevant and exciting way using data and digital, wins!”

Fintech Uses Machine Learning to Outsmart Competition Smartphone QR Code

Fintech companies are rapidly integrating traditional and digital techniques to improve financial services and add value to the customer experience. While some fintech companies such as SoFi are becoming household names, for the most part these companies are lesser known. According to Investopedia, top fintech companies in the world include Ant Financial, Adyen, Qudian and Xero.

[Related article: Why Financial Services Move to Hybrid Cloud]

These companies rely on fresh ideas that leverage modern business and consumer technologies. Many new ideas are coming from new ways of using machine learning. Thanks to predictive modeling capabilities, machine learning is helping fintech organizations to maximize efficiency while growing profits and expanding customer bases.

It’s not only a small improvement, but an entire revolution in the field of financial services. Machine learning completely changed the lending framework, but it also contributed to many other alterations in finance. 

Fintech and Machine Learning Explained

According to Fortunly, almost 50% of people exclusively use digital channels for their financial needs. At the same time, 96% of global consumers are aware of at least one fintech service. Fintech deals with all the traditional banking services, as well as risk management, trading, insurance, and many more. Increasingly, machine learning is helping automate and improve how software manages these needs.

By definition, machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Jake Gardner, a fintech writer at Best Essays, explained that machine learning comes in four formats: supervised machine learning, unsupervised machine learning, semi-supervised machine learning and reinforcement machine learning.

[Related story: Banks Progress, Still Struggle to Go Digital]

Industry researchers argue pros and cons of the technology, but overall, machine learning is viewed as a tool that can get better with every problem solved. If that’s true, the risk of making a mistake decreases over time. But despite its promise, machine learning is still not widely used among fintechs worldwide; according to Finances Online, only 15% of organizations are advanced machine learning users.

How Machine Learning Can Improve Fintech Operations

Fintech companies that are using machine learning leverage it for document interpretation and content creation, to minimize loan risks, improve customer service, conduct stock market actions and handle various marketing needs.

Fintech software developer

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.

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