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Artificial Intelligence Use Cases in FinTech

Almost all predictions, analysis, and forecasting reports show that the use of artificial intelligence (AI) is on the rise. The banking industry has taken a particular interest in the technology.

Financial institutions are evaluating and adopting AI for protection against threats, fraud analysis, and investigation intelligence. With the rising popularity of AI for other purposes, such as enhancing customer support and algorithmic trading, the field of financial technology is set to get revolutionized in the coming years.

Let’s take a look at some use cases of AI in FinTech to understand how it is already making a difference today.


Quill is an automation platform developed by a Chicago-based company Narrative Science. Quill helps in financial reporting with natural language generation (NLG), an AI technology that converts your data into plain English to make you understand the insights in simple words, just like a human data analyst would do in your company. Quill also understands the most relevant part of a report from past records and tries to give the reports in similar words, sentence length, and structure to make it legible for non-technical people. It saves significant time for the enterprises analyzing data and creating hand-written summaries and reports.

Quill can be integrated with data visualization tools such as Tableau, Microsoft PowerBI, and Microstrategy to create reports based on the representations shown in these tools. The company claims Delloite, USAA, and Mastercard as users of its tool.

Narrative Science has also assisted banks with the generation of automatic suspicious activity reports. Therefore, Quill is useful for financial institutions in reducing risks and maintaining compliance. You can read more about Quill’s overview here.


MasterCard aims to make the transfer of money more convenient for the world. But as this convenience increases, so do the risks of fraud and cybercrime. And due to increasing threats, over-secured systems have started flagging genuine transactions as fraudulent.

To reduce these errors, MasterCard has come up with their Design Intelligence platform through which fraud detection will be more accurate and false declines will reduce. With the acquisition of AI company Brighterion, MasterCard is trying to make all payment card swipes and online payments fraud-free. Since the technology provided by Brighterion is based on self-teaching algorithms, it makes better decisions over time.


We all know the importance of predictive analytics when we are trading in stock options and assets -- even if you make your own predictions by doing your own research, data collection, sentiment observations, and study of past records before buying a single share. And what if all this is done with the help of software?

If you visit Stocksrank, you will find a feature called Kai Score. Kai basically provides a numerical rank/score to a stock, which gives a fair idea of its future performance on the basis of millions of data points. Apart from the analysis of structured financial data, Kai also observes sentiments of the market through natural language processing and studies unstructured data such as news, blogs, and social media using Big Data. Therefore, it is a perfect example of algorithmic trading.


Personatics, a well-established artificial intelligence company, offers conversational AI software called Assist that is used by various banks and financial institutions to develop chatbots to provide better customer service. These chatbots can listen or read the queries of customers and process them using NLP. With the help of AI, the context of the query is understood and eventually the issue is addressed.

The deployment of AI chatbots can help banks in increasing their operational efficiency and customer satisfaction.


AI is making a huge impact in the banking and finance sector. The future of AI in this industry has even more to offer, from robotic clerks in banks to super-safe verbal transactions. In such a scenario of fast growth, AI consulting companies  will come at the doors of financial institutions every day with new products and offers.

But one thing which should be kept in mind is that AI is not that easy to implement. It will take your time, money, and energy to integrate a machine that learns with your pre-existing systems. Also, these tools do not work with the approach of “one size fits all”. Every company, institution, or bank has to analyze its own needs and culture before using AI to solve their problems. But don’t worry! Just like AI, you can also learn more about implementation with time and experience.

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