Machine learning (ML) is one of the most powerful tools in today’s professional world. The technology consists of algorithms that allow devices to learn patterns and trends from large datasets. Then, a company can leverage an ML mobile app that uses this data to develop valuable business insights.

Financial companies are finding innovative uses for machine learning technology.

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Recently, the finance industry has been benefiting greatly from machine learning. There are many valuable uses for the technology in this field, from automating portfolio management to bolstering a bank’s fraud detection tools to optimizing the process of trading assets. In this way, utilizing machine learning in finance can reduce operational costs, streamline key processes, and improve a company’s bottom line.

#1: Replacing Manual Work with Process Automation

Several major financial institutions are using machine learning to automate repetitive tasks. The goal is to enhance productivity, use money more efficiently and improve the customer experience.

For instance, JPMorgan Chase’s Contract Intelligence (CoiN) platform automates some of the simpler tasks that take up a lot of time and money. The platform analyzes documentation and extracts key information, processing 12,000 credit agreements in several seconds. This would have taken 360,000 man-hours in the past.

Other ML applications in process automation include BNY Mello’s integrated process automation, which saves $300,000 a year while bolstering the company’s operational performance. Wells Fargo also developed an AI-powered chatbot that communicates with customers and helps them with their accounts.

All in all, ML is helping financial companies run their businesses more smoothly by freeing employees from tedious manual tasks and allowing them to focus on more creative and meaningful work.

#2: Using Robo-Advisors for Portfolio Management

Machine learning can also automate the process of managing a customer’s portfolio in the form of robo-advisors. Robo-advisors are algorithms designed to calibrate a customer’s financial portfolio. This means the algorithm examines an individual’s investments, goals and risk tolerance, then uses this data to suggest a smart next move.

These robo-advisors—which can be developed to be compatible with mobile apps—ask users to enter goals, such as when they want to retire and how much they would like to have saved by then. The advisor then helps to spread a user’s investments across various asset classes and financial instruments to help the customer reach their objectives.

The system constantly adapts as the user adjusts their goals and the market changes. The technology is one example of how financial institutions can leverage ML to help customers make better financial decisions.

#3: Making Better Decisions with Algorithmic Trading

The goal of algorithmic trading is to develop algorithms that use data to make better-informed trading decisions. Machine learning is applied in the form of mathematical models that monitor the news in real-time, identifying patterns that may make a stock rise or fall. Based on these predictions, the algorithms can be automated to sell, hold or buy stocks in real-time.

Machine learning can analyze thousands of data sources at the same time, which is much easier than hiring hundreds of people to monitor stocks. The end result is algorithms that give traders a slight advantage over the market average, regardless of their experience. Algorithmic trading is proving to be a very profitable venture that requires very little in expenses compared to traditional prediction models.

#4: Machine Learning in Finance Allows for Better Fraud Detection

Another major benefit of machine learning is its ability to monitor and thwart security threats in real-time. ML offers a lean fraud detection model that can adjust its security tactics as threats evolve. Machine learning detects any suspicious behavior, then flags it for security team review.  

For instance, a bank might use ML to keep tabs on thousands of transaction parameters for each account. The algorithm would examine each action a cardholder takes and determine whether or not that action is considered normal for that user. This way, card companies can freeze an account if they detect fraudulent behavior.

ML can also improve a company’s network security by examining thousands of factors that may indicate digital threats and thwarting them in real-time. This means that banks and other financial institutions can automate the process of keeping their servers protected before a threat infiltrates its database.

There are many ways in which financial businesses are leveraging the power of machine learning to ensure their operations run more smoothly while their bottom lines improve. Keeping servers and customer data protected while also making sure that customers are getting the royal treatment is the next logical step for this industry. Now, ML is offering thousands of ways to optimize financial transactions from every angle.

If you’re interested in developing a finance app that uses machine learning, you will need to work with an experienced development team. At SevenTablets, we specialize in custom mobile app development with extensive experience with machine learning technology. In addition, we’re well-equipped to integrate various cutting-edge technologies, including augmented reality, virtual reality, artificial intelligence, blockchain and natural language processing.

SevenTablets is based in Dallas, but we also work with clients in Austin, Houston, and across the nation. To discuss your project, we invite you to contact us today.


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Lacey Williams-McGhee

Lacey Williams-McGhee

Marketing Director at SevenTablets
Lacey Williams is a marketing and design professional living in the great state of Texas. When she's not working hard at the SevenTablets headquarters or designing products for her side gig, she can be found exploring new restaurants, hanging out with her husband and walking her golden retriever.

Lacey earned a B.A. from Baylor University. Sic'em!
Lacey Williams-McGhee