FinTech is a sum of all the use cases of cutting-edge technology that happens to the financial industry. Let’s quickly explore AI applications in FinTech.

Applications of AI in Fintech

Artificial intelligence (AI) is making a paradigm shift in technological advancements. We are already witnessing a great deal of change in our lives and businesses due to AI. The finance industry is no exception. It seeks and adopts new technology at an early stage. The financial industry is the most curious of all the industries about technological advancement. They are one of the early adopters of new disruptive technology. For example, JP Morgan was one of the early adopters of Blockchain. FinTech is a sum of all the use cases of cutting-edge technology that happens to the financial industry. Let’s quickly explore some of the applications of AI in FinTech.

Transactional bots as financial advisor

Transactional bots are one of the popular use cases of AI in FinTech. This is probably because the range of their applications is quite broad. These bots act on behalf of humans, play the role of a personal assistant, interact with external systems to complete transactions, etc. Since the use cases are plenty, they can be customized to meet the precise requirements of the business.

In the finance industry, these transactional bots can be used to offer coaching, advise, or recommendations on financial services such as plans, savings, or expenditures. This kind of service by the bots increases user engagement and experience with the finance product/service.

These bot assistants are built using Natural Language Processing (NLP). This type of AI model processes data in the form of human language. The virtual assistant named Ella, launched by Sun Life is a good example of this application.


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Profiling clients

One of the most crucial aspects of running a successful bank or finance business is to weed out fraudulent or high-risk customer profiles. They do this based on the client’s risk score. With the use of AI, businesses can automate the process of categorizing clients based on the risk profile from high to low. Using this categorization, financial advisors can make decisions on which financial products to be associated with each risk profile. And then using this information, they can then offer the respective products to the clients in the form of product recommendations. This too can be automated by the help of AI.

Artificial Neural Network models are trained on historical customer information and the pre-defined information provided by the financial advisors.

Automate search tasks

Chatbots can be used to retrieve information related to transactional data. Banks can give bots access to banking transactions. These bots use Natural Language Processing (NLP) to understand the meaning of the customer’s query. These queries do not require analytical skills (hence they are called transactional). For example, account balance, spendings and savings, etc. The chatbot retrieves the required information and responds to the customer with the results.

Bank of America

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For example, Erica is a bot built by Bank of America. It serves as a financial digital assistant for the bank’s customers. The bot was an instant success among the customers. It provides transaction search in a user-friendly way. This enables users to easily look for specific transaction with a particular vendor in their historical data. This way they can get rid of the hassle of manually going through their bank statements for the information they require.

Credit risk assessment and underwriting services

Insurance companies can implement an AI-enabled platform that assesses clients’ credit risk. Based on this information, the financial advisor can then recommend the best offer or plan to the client. With the help of AI, the efficiency of these services is enhanced, and financial institutions are able to deliver great customer experiences. It also reduces the turnaround time of such processes and operations greatly.


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For example, Lemonade is an insurance company that is able to process claims for its customers with the help of AI and a chatbot named Jim. The user explains the situation to Jim through Lemonade‘s messaging app. The app then uses AI to verify the claim by matching the description to the one in the database. If the claim is verified to be legitimate, the company automatically approved the customer’s claim.

Such applications of AI are not limited to insurance alone and can be used on credit scoring for loans as well.

Analyze contract

Analyzing contracts is a monotonous and internal task in the finance industry. And it only makes sense to handover this to AI. Optical Character Recognition or OCR is a process that can use to convert hard copy of documents into digital copies. An NLP layer with business logic can be added on top of OCR to not just digitalize the documents but also interpret and correct contracts at high speed. The model can be trained using existing contracts so that it learns how to respond when presented with such content.

For example, JP Morgan used AI to precise solve this problem. As a result, they were able to free their employees from hours of repetitive work. This enabled the employees to focus on more complex tasks and improved productivity.

Predicting customer churn

Minimizing customer churn rate is one of the key goals or metrics for any organization or industry. It is crucial for companies to retain their customers if they wish to earn revenue or grow their business. One of the ways in which this can be done is by predicting beforehand which customers are most likely to churn and nurture them to stay longer with the company.

Artificial intelligence can assist business heads by identifying clients who are showing signs of cancelling the policy. The customer success team can then quickly prioritize these customers and provide them with a higher level of service as part of retaining them. The AI model is able to do that based on certain behavior triggers or explainer variables such as unsubscribing from product newsletters, etc. The model is trained on historical data of customers who have stopped doing business with the company.

Closing thoughts

This is the first round-up of some of the more popular applications of artificial intelligence in FinTech. We’re sure that as the technology grows and develops there will be newer applications of this technology. We’ll follow this post up with another round up in the upcoming weeks. Stay tuned!

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