Four Machine Learning Applications in Finance

Machine learning (ML) is gaining attraction in the industry because it allows businesses to run their systems more efficiently without having to overspend to scale up operations. Because of the need for accuracy, the high volume of work processes they deal with, and the fact that everything is now digitized, technology is especially relevant in the finance world.

Companies have developed effective methods for making investment predictions, managing customer portfolios, marketing, and preventing fraud using machine learning. These are all critical components of maintaining a brand's reputation by providing timely advice in an organized and consistent manner to users.

Here are four ML applications in the finance world:

Making investment predictions

Trading services that use ML technology have been assisting businesses in maximizing their investment opportunities. This is done by using smart algorithms that know when to buy and when to sell stocks. Effective investing software allows investors to automatically buy a stock when it reaches a certain price or sell it when the per-share price falls below a certain threshold. ML can also make investment recommendations based on automated market trend analysis.

Hedge funds have reaped the benefits of these work processes by abandoning traditional predictive analysis methods in favor of ML algorithms. JPMorgan and Morgan Stanley, for example, have already developed automated investment advisors that run on ML, improving their bottom line in ways that any company can as well.

Portfolio Management

With algorithms that adjust an investor's financial portfolio depending on the user's goals and risk tolerance, technology is also capable of optimizing a user's risk. The idea is to create a personalized portfolio based on personal information such as age, income, current financial assets, and desired retirement age. That will enable companies to offer users investment opportunities depending on their circumstances.

Marketing

The ability of ML in finance to make predictions based on past behaviors in order to improve an organization's marketing campaign is an underappreciated role. To predict how effective a new marketing strategy will be for a user, for which software can examine web activity, mobile app usage, and feedback from previous ad campaigns. Marketing executives have had much success in the fintech world by implementing this technology in the past. The number of machine-learning-based advertising startups has skyrocketed in the last year, indicating that technology is the next big thing in marketing.

Fraud detection

We live in a time of data breaches because more financial institutions are digitizing, which means their systems contain large amounts of valuable company data. Previous financial fraud detection systems relied on large, complex sets of rules that took a lot of manpower to implement. However, ML has aided in the automation of the fraud detection and prevention process by learning and calibrating the latest or potential threats.

Systems can use technology to monitor and detect anomalies and flag them for security teams to investigate. ML algorithms compare each transaction to account history to determine whether a transaction is fraudulent.

In addition, the technology can detect unusual activity such as large cash withdrawals or out-of-state purchases in real-time, delaying a transaction until a human can make a decision.

At last, in no time predicting in the future won’t be magic but machine learning.