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How AI is Helping Financial Institutes in Improving their ROI? [Works in 2019]





Some people are scared of the rise of Artificial Intelligence and self-learning machines in the world. They think that these machines will someday outsmart the Homo sapiens. And they will pose a threat to the existence of the current most intelligent species of the world. Of course, that possibility cannot be overruled entirely because “you never know.”

From the trains (rumored as witches) to the Wi-Fi (for its so-called harmful radiations), almost every disruption in science has managed to activate certain paranoia among people. But humans have never stopped using technology in the past just because some of us initially feared them. Instead, we are adept at using and improving those “dangerous” inventions.

Such a disruptive technology is AI. It is making its way in almost all the industries. Some industries are fast to adopt it, and some are still paranoid. Financial sector comes in the first category. This industry did not show any sign of fear in the last decade with the adoption of AI, despite its high security and compliance requirements. If you believe a report by, the market size for AI in Fintech will rise to US$7.28 billion by 2023 with a CAGR of 33.8%.

Since the data predicts the strong future relations between AI and Fintech, it means that there is a large basket of opportunities for AI in money houses. Therefore, to understand how AI is getting converted into ROI by these financial institutions, let’s take a look at the following points:

Investment Management Sector

1. Robo-advisors

Soon you will not be talking to a human stock broker for your trading. The Robo-advisors will take their place. These Robo-advisors can recommend the best stocks available in the market for you based on your past decisions, choices, and other information. These platforms automate the transactions and manage the portfolio allocation.

Betterment, a digital investment startup which is expert in Robo-advising, has more than 2.5 lac customers and assets under management (AUM) worth US$10 billion. It shows that how RPA is changing the way people trade their stocks today.

2. Algo-trading

Algorithmic trading boosts the frequency of trading in the market. Pattern recognition and real-time big data analytics are the pillars of Algo-trading. Massive data corresponding to index prices, sentiment analysis, social media updates, and the latest news, etc. is used to draw patterns and insights to strategize the investments.

Kavout is one such company which provides scores and ranking of the stocks by analyzing big data from various sources in both structured and unstructured form. It has more than 2 22 Million active users and also has AUM of billions of dollars.

Banking Sector

1. Chatbots

The introduction of Natural language processing (NLP) in chatbots has changed the landscape of customer service in the banking sector. The AI chatbots provided by companies like Cleo work in a sophisticated way. HDFC Bank’s chatbot EVA is also one of the most popular virtual assistants who has handled millions of queries already.

The 24*7 availability and quick response of a chatbot take care of the customers and hence make the lives of banks easier. They can tell you about your bank balance, assess your spending, recommend the best ways to save money, and provide you other financial services based on their machine learning algorithms.

2. Voice Recognition

With a shift in banking from offline to digital, today, customers’ bank accounts are exposed to online threats more than ever. Preventive systems like PIN codes, passwords, and security questions are now easily dodged by the malicious software run by unethical hackers. Banks and customers both can face tremendous losses in such incidents.

AI presents you a different solution altogether to this problem. Now with the help of bio-metric voice recognition, banks can verify the customer identities. It streamlines the phone banking processes up to an extent. The customers are confirmed based on their voice prints recorded and stored with the help of this technology.

3. AI for Fraud detection

AI is helping the banks in curbing transactional frauds to a great extent. Location intelligence helps in identifying the anomalies in cross border transactions and proximity risk. AI consulting services provide a helping hand to the banks in following the compliance also. Automated metadata extraction on unstructured data at a massive scale gives the banks a heads up on the sensitive areas of agreement. Apart from that, with the help of NLP, now machines can read the legal documents. It can save so much time and employee costs for the legal departments of the banks.

Insurance Sector

1. Cognitive Algorithms

As an insurance company, the biggest threat to your revenue comes with false or wrong claims. Therefore, it takes time for insurance companies to verify and approve the requests. But this can frustrate the beneficiaries and hence, loss of brand value. But with the help of cognitive algorithms, you can monitor a customer’s repeat visits to the hospital, and smart marketing will also help you in suggesting suitable plans to them. It can speed up the whole insurance process. This will not only result in personalized care for the customer but also prevention of insurance companies from false claims.

2. Internet of Things

Smart wearable technologies are being used by the partnerships of insurance companies and safety gear manufacturers. These wearable are used to analyze and prevent possible injuries and deaths in the workplace. They generate real-time data and learns from past data. This helps in the reduction of future risks and addressing current safety issues.

Also, Telematics has become a buzz word in the insurance industry. It has started deciding the right insurance policies for the customers. For example, now, insurance companies can calculate premiums for the customers based on their driving styles, exposure to physical accidents, and other habits. Hence, if you want to pay fewer premium amounts on your insurance policies in the future, start driving responsibly.


Although FSI companies have started to develop a taste for AI in their operations, yet there are some road blockers in this journey. Traditional companies which have grown big are not agile enough to change their processes all of a sudden. It takes time for the giants to change the direction. Also, it is tough to find expert talent in this field due to high demand and low supply. And one last risk associated with highly automated systems is cyber-crime/hacking. We cannot be 100% sure about the security of any soft product. AI runs on human-made machines which can also be misused by men. Therefore, ride to complete smart banking is still bumpy.

This article has tried to highlight the current position of AI in financial institutions. The successful use-cases and evidence should be able to convince you that AI can increase the ROI of FSI companies.

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