Machine Learning Development Use Cases In Banking And Finance
Just 30 years ago, you would have to wait some days for a bank to approve your credit. Or spending weeks stuck in the red tape of your insurance company just to get reimbursed after a minor car accident. Nowadays, these operations take less than a day as documents are submitted and processed online with little or no human interaction. In this article, we will cover a set of technologies that promise to transform the entire idea of doing business in the financial world.
Artificial Intelligence: Is it just a buzz phrase to put on your landing page or a ready-made use case for innovation? The answer is…both. Of course, we will talk about real-life examples of the use of AI in various areas of the financial industry.
Make investment predictions:
The fact that Machine learning development companies in Frisco provide advanced market insights allows fund managers to identify specific market changes much earlier compared to traditional investment models.
With big-name firms like Bank of America, JPMorgan, and Morgan Stanley investing heavily in ML technologies to develop automated investment advisors, the disruption in the investment banking industry is quite apparent.
Secure transactions:
Machine learning algorithms are great at detecting transactional fraud by analyzing millions of data points that tend to go unnoticed by humans. In addition, ML also reduces the number of false rejections and helps improve the accuracy of approvals in real-time. These models are generally based on customer behavior on the Internet and transaction history.
In addition to detecting fraudulent behavior with high accuracy, ML-powered technology is also equipped to identify suspicious account behavior and prevent fraud in real-time rather than detecting it after the crime has already been committed.
According to Best artificial intelligence companies in Frisco research, for nearly every $1 lost to fraud, recovery costs borne by financial institutions approach $2.92.
One of the most successful applications of ML technology is credit card fraud detection. Banks are generally equipped with monitoring systems that are trained on historical payment data. Algorithm training, validation, and backtesting are based on vast data sets of credit card transactions. ML-based classification algorithms can easily tag events as fraud vs. non-fraud to stop fraudulent transactions in banking.
Fraud detection and prevention:
As the number of transactions, real customers and integrations grow, security threats will appear. This is where machine learning algorithms come in handy when banks and other institutions require special fraud detection.
Banking organizations can use it to monitor a considerable number of transactional parameters at once for each account in real-time. The algorithm examines historical payment data and analyzes the action of each cardholder. Such models can be highly salient and prevent suspicious behavior with great accuracy. A global payment system Payoneer offers online money transfers and financial services around the world. Consequently, the company’s customer database is estimated to be in the millions.
Portfolio management:
Portfolio Management is an online wealth management service that uses statistical issue points as well as automated algorithms to optimize the performance of client's assets. Clients complete their financial goals, for example, to save a certain amount of money over a certain period of time. The advisor robot then allocates the current assets to investment variants and opportunities. Portfolio management involves creating and monitoring selected investments that align with an investor’s long-term financial goals and risk tolerance.
One of the world’s largest investment management firms, BlackRock Investment Company, offers Aladdin, an operating system built and tailored for the needs of investment managers. The company claims that Aladdin can use machine learning in FinTech to provide investment managers at financial institutions with risk analysis and portfolio management software tools to make more informed investment decisions and operate more efficiently.
Underwriting of loans:
In the banking and insurance industry, companies access millions of consumer data, with which Deep learning development companies in USA and machine learning can be trained to simplify the underwriting process. Machine learning algorithms can make rapid underwriting and credit scoring decisions, saving businesses both the time and financial resources spent by humans.
Data scientists can train algorithms on how to analyze millions of consumer data points to match data records, look for unique exceptions, and make a decision about whether a consumer qualifies for a loan or insurance.
For example, the algorithm can be trained on how to analyze consumer data such as the consumer’s age, income, occupation, and credit behavior (default history, whether they paid loans, foreclosure history, etc.) so that can detect any results that can determine if the consumer qualifies for a loan or insurance policy.
Algorithmic trading:
Algorithmic trading refers to the use of algorithms to make better trading decisions. Traders typically build mathematical models that monitor trading news and trading activities in real-time to spot any factors that might force security prices up or down. The model comes with a predetermined set of instructions on various parameters, such as time, price, quantity and other factors, to make transactions without the active participation of the trader.
Unlike human traders, algorithmic trading can simultaneously analyze big volumes of information and make thousands of trades daily. Machine learning and Data science companies in USA make quick trading decisions, giving human traders an edge over the market average.
Furthermore, algorithmic trading does not make trading decisions based on emotions, which is a common limitation among human traders whose judgment may be affected by emotions or personal aspirations. The trading method is primarily used by hedge fund managers and financial institutions to automate trading activities.
Wrapping it up:
Machine learning technologies are gaining popularity in the financial services industry and are increasingly being used in practice by many companies. They play an increasingly important role in various processes and will become more common and widely used in the coming years due to technological developments and the emergence of new business models.
Also Read:
How can AI change the banking sector
Artificial Intelligence In Manufacturing
USM’s team of expert AI company developers programs business systems with advanced machine learning solutions to produce actionable decision models and automate business processes. Machine learning company in Texas convert raw data from legacy software systems and big data providers into clean data sets to run classification (multi-label), regression, clustering, density estimation, and dimensionality reduction analyses and then deploy those models to the systems.
About the Author
I am a passionate content writer and blogger who has written a number of blogs for mobile app development. Being in the blogging world for the past 3 years, I am currently contributing tech-laden articles and blogs regularly to USM Systems. I have a competent knowledge of the latest market trends in mobile and web applications and express myself as a huge fan of technology.
