The Role of Machine Learning in Fintech

Out of the multifarious industries, one that has been greatly benefitted by Machine Learning is- Fintech. It has significantly improved and is continuously improving the way the finance sector operates, be it related to investment decisions, risk management, or streamlining business processes.

Machine Learning in Fintech

The role of machine learning in fintech predominantly lies in pattern identification. The algorithms detect correlations among the large data sets and carry out specific tasks that are unfeasible from the human perspective. Machine Learning uses diverse techniques to handle the large amount of data processed by the system. The ability of ML to learn and predict enables Fintech companies to perceive new business opportunities and enhance their existing operations.

Also Read: How Machine Learning is transforming eLearning?

Use of Machine Learning in the Fintech industry

Let’s take a look at the applications of machine learning in fintech or the areas where it has left an imprint in the banking or finance industry.

1. Fraud Detection

Financial frauds have remained one of the manifold challenges for the financial service providers, for which they leverage ML technology as it is humanly not possible to anticipate. In order to prevent fraudulent activities from happening, machine learning algorithms are continuously trained to detect the irregularities between transactions. It can evaluate the vast data set of multiple transactions in real-time and flags any activity as fraudulent that looks suspicious or unusual by running through the user’s transaction history and previous interactions. Although this doesn’t prove whether the transaction was indeed initiated by a fraudster, Machine Learning does its best to minimize the number of false rejections and improve the preciseness of its system.

2. Instant Loan Approvals With Lower Risks

How do banks decide the loan eligibility of an individual? And if the person is eligible for a loan, how much he/she can borrow? This is evaluated by calculating the individual’s Credit or CIBIL score which is one of the most useful applications of machine learning in Fintech. Not only in banks but this is also considered in Peer-to-Peer or P2P lending.

Alternative to bank loans, there has been a steep rise in Peer-to-Peer Lending in recent years. P2P lending is a type of loan lending business that connects borrowers directly with the investors, enabling the former to obtain loans from the latter without any financial institution acting as the middleman. The interest rates and other terms and conditions are listed on the P2P websites. This is a great alternative for borrowers who want to receive a loan offer better than the traditional bank ones.

But be it any of them, the technique used is the same. ML provides lenders with substantial insights into a borrower’s creditworthiness, such as the payment history, credit utilization, credit type, and more. A credit score is then assigned to the borrower. The better the score, the faster the approval with low-interest rates and fewer risks.

Multiple other factors such as social information, rent payments, and even health checkup records are considered to minimize risk. Machine learning algorithms compare aggregated data points with those of numerous other customers to generate an accurate risk score. The loan is automatically approved if the risk score lies below the threshold set by the lender.

3. Intelligent Automation

Like many other industries that use Machine Learning to streamline their business processes, the finance sector is no behind. The applications of AI enable Fintech companies to eliminate manual efforts by automating tedious and repetitive tasks, popularly known as Robotic Process Automation (RPA). This also helps the service providers cut down the cost significantly and allows the workers to focus on other important areas.

One step ahead of RPA is Intelligent automation which is an amalgamation of artificial intelligence, machine learning, and process automation that focuses on building smart business processes and workflows that can think, learn and adapt on their own, thereby accelerating digital transformation. Integrating it into the business operations allows finance companies to boost efficiency and acquire new capabilities which are beyond human abilities. This includes processing tons of documents and applications, detecting associated issues, solving customer queries, and making smart recommendations.

4. Machine Learning in Fintech enhances Customer Experience

The ability of Machine Learning to extract tons of data and recognize its patterns allows financial institutions to gain better insights into their customers that further allows them to create and send personalized offers or deliver a personalized customer experience. The algorithms analyze customer data and make recommendations best suited to their needs and preferences. This helps companies present offers or services to the relevant customers or audience.

When we talk about how ML in Fintech enhances customer experience, the discussion cannot be concluded without mentioning Chatbots. Today’s generation chatbots are highly smart, intelligent, and very human-like unlike those of previous generations. They analyze conversations, learn from their previous interactions, and present more helpful and relevant content the next time. Moreover, they can be great personal finance coaches as well to remind or guide you on your spending limits and habits. The best thing about Chatbots is they are continuously evolving to adopt a human-like approach so that the customers think as if they are talking to a human and not a bot. The use of chatbots also eliminates the need to build or expand the customer service department. This comes of great use for small and mid-sized finance service providers.

Read: How AI is Improving Customer Experience?

5. Algorithmic Trading

The involvement of Machine Learning in the Finance sector has proved to be a boon for people investing in stocks. They do not need to spend time scanning the markets to execute a trade, the job is performed through Algorithmic trading.

Algorithmic trading or Algo trading uses computer codes that follow pre-defined instructions such as price movements or volatility levels to make a trade. Based on the market and the traders’ objectives, the algorithms automatically execute a trade on their behalf as soon as the current market conditions match the defined instructions. The trades are rapidly executed, possess high accuracy, and don’t involve human emotions unlike in traditional trading where traders are usually affected by greed and fear of loss.

To put it differently, Machine learning algorithms identify patterns and behaviors in the vast volume of traders’ historical data and learn from it. They monitor the available data sources in real-time to pinpoint patterns indicating stock market dynamics. When the defined condition is matched, a computer program automatically monitors the stock price and executes the buy and sell orders. Thus, the trader doesn’t have to monitor live prices and graphs or put in the orders manually. The system does this automatically by correctly identifying the trading opportunity.

Final Word

Machine learning and the applications of AI have undoubtedly proved to be game-changer for the banking and finance sector with their ample benefits. It is the result of this that many Fintech providers have embraced ML technology for rapid and flawless operations. And it won’t be a surprise if we witness ML and AI taking over many other manual operations in the future with more security and even better analysis.
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