Machine learning in finance and banking services is now considered a key aspect of managing assets, calculating credit scores, analyzing fraud, approving loans and cards, Risk Management, and providing financial advisory services to investors. Many leading financial and banking services companies are using machine learning and artificial intelligence technologies in their operations, services to the customers resulting in a better-streamlined process, reduced risks, and better-optimized portfolios and investment decisions.

Why Use Machine Learning in Finance?

Here are some of the reasons why financial and banking services consider using Machine Learning.

• Low operational costs
• Increased revenues.
• Enhanced user experiences.
• Better compliance and reinforced security.

How Machine Learning Is Used in Finance

There are several ways in which machine learning and other tenets of Machine Learning are being employed in the finance industry. Below are Some of the major applications of machine learning in finance:

Financial Monitoring

Financial monitoring is the security use case for machine learning in finance. In this financial monitoring machine learning algorithms to the system to monitor a large number of micropayments and Transactions such as money laundering techniques as smurfing.  

Process Automation

Process automation is the major application of machine learning in finance. Process automation technology allows to replace manual work, automate repetitive tasks, and increase productivity in finance aspects. Machine learning helps companies and organizations to optimize costs, scale-up customer experiences, and improve services to Customers.

Here are some of the automation use cases of machine learning in financial services:

• Chatbots
• Call-center automation.
• Paperwork automation.
• Gamification of employee training, and more.

Secure Transactions

Machine Learning algorithms are excellent at securing transactions at any point in time. The machine learning algorithms detect transactional frauds by analyzing millions of Transactions/data points by human frauds. Machine Learning also reduces the chances of false application recognition and helps improve the precision of real-time transaction approvals instantly.

Algorithmic Trading:

Machine learning helps to make better trading decisions. In algorithmic trading, Machine Learning helps to act proactively to sell, hold, or buy stocks according to its predictions.  Machine Learning helps human traders squeeze advantage over the market average. Unlike human traders, algorithmic trading can analyze large volumes of data and make thousands of trades every day.

Financial Advisory

Machine Learning algorithms offer customers high-quality financial advice and guidance. Machine Learning algorithms track and analyze the customer's spending pattern on a daily basis and monthly basis as well as with this ML we can analyze this data to identify their spending patterns, followed by identifying the areas where they can save and spend.

Fraud detection and prevention

Banks and E-commerce payment systems have already been developing this type of algorithm model to identify and block most fraudulent transactions. These models are built on the client’s transaction history as well as the client’s behavior on the Internet. The ML algorithms that detect online frauds have been developed from Big Data technologies.

Portfolio management (Robo-advisors)

Robo-advisors are online applications that are built using machine learning algorithms, and they provide automated financial advice and portfolio management to investors. The algorithms are used to establish a financial portfolio according to an investor’s goals, amount, and risk tolerance. Robo-advisors require low account minimums and are usually cheaper than human portfolio managers in real-time.

Loan/Insurance Underwriting

In the banking and insurance industry, companies access millions of users’ databases, with the machine learning algorithms we can use to order to simplify the loan / Insurance underwriting process. Machine learning algorithms make quick decisions on loan or Insurance underwriting and credit scoring. It leads to saving organizations both time and financial resources that are used by human effort. With the existing database, ML algorithms takes a decision the customer qualifies for a loan or insurance.

Future prospects of machine learning in finance

In the future, the trends in this financial and banking services area will likely continue to develop at the same pace, if not faster. Keeping up with the current trends in Artificial Intelligence and machine learning, increasing user acceptance will continue day by day, as will the high demand for a more personalized and humanized approach from financial institutions and businesses. Machine Learning improves better customer experiences, optimizes processes, and higher work efficiency.