How AI is affecting Fintech


By FintechNews staff

The FinTech industry is distinguished by its high degree of innovation within a complex ecosystem that includes, among others, banks, financial service providers, and start-ups.

Not surprisingly, then, in recent years, it has relied heavily on artificial intelligence (AI) and machine learning (ML) for strategic decision making, customer insights, understanding consumer purchasing behaviour, and improving the digital transaction experience.

Now, let’s examine some use cases of FinTech innovation driven by AI, and the main benefits FinTech companies can gain from this technology.

Increased Security

As the number and complexity of cyberattacks increase, artificial intelligence (AI) is assisting under-resourced security management strategists in staying abreast of the latest developments.

Diagnostic Capabilities: AI technologies offer rapid insights to cut through all the clamour of everyday alarm, creating significant diagnostic capabilities that analyse massive quantities of information and highlight hidden patterns and anomalies.

Processing Sensitive Financial Data: Fraud is one of the most pressing problems the finance industry faces nowadays. According to Javelin, users and businesses experienced a 56 billion dollar loss in 2020 due to fraud.

Thanks to its ability to discern patterns and suspect behaviours, artificial intelligence is being used to spot fraudulent activities, questionable transactions, and generally offer a boost to processing sensitive financial documentation – all with a lower chance of security risk. Identifying new fraud signals is where businesses see one of the highest rates of return.

Improved Customer Service

There are many use cases when AI can improve customer experience and customer service. A couple of examples include:

Chatbots in FinTech: AI-powered chatbots can minimize the workload placed on call centers as they tackle the most typical and frequent user problems.

With the help of chatbots, some banking institutions can even grow their customer network. Erica, in the Bank of America app, is one of the most successful chatbot examples. With access to the user’s balance and transactions, it offers transaction search and overview, bill reminders, and notifications about duplicate charges, and gives users hints on their financial health. 

AI-Powered Personalized Banking Apps: Many banking apps offer personalized financial advice to help users achieve their financial goals, track their income and expenses, and more.

This personalization is possible primarily due to AI-powered FinTech innovations. For instance, Bank of America offers an app that helps users plan their expenses via an AI-powered, personalized approach to each customer.

Risk score profiling

Categorizing clients based on their risk score is a vital part of the manager’s routine. Or not. From now on, this task can be passed to AI algorithms.

Using Artificial Neural Networks (ANNs), developers can train technologies on the user’s historical data and then classify their profile from low to high risk level. Above that, technologies also can provide clients with service recommendations based on their risk score.


-The real pain point of all financial companies, including fintechs, is compliance regulation. Financial services providers should act with regulations in mind — and not only existing ones but also considering what’s coming ahead. 

RegTech (Regulatory Technology) is a way to manage regulatory compliance with the help of AI algorithms. It is a complex term that includes client identification, transaction monitoring, regulatory analysis, and reporting. 


Let us know what kinds of IoT, AI, and digital transformation content to share!

User Behavior Analysis

Artificial Intelligence in FinTech can predict a user’s behavior with the help of AI APIs, which can also be leveraged to banks’ and FinTech companies’ benefit. Let’s say, for example, that the user requests data about their expenses in the last month – a single request. On the server-side, with the help of AI, you predict their follow-up request (e.g., income in the previous month) and provide this information in the same response. As a result, you minimize the number of requests and the load on your system accordingly. The user also benefits, as the system works faster if the predictive analysis is correct.

Fraud Detection

Fraud is one of the most pressing problems the finance industry faces nowadays. According to Javelin, users and businesses experienced a 56 billion dollar loss in 2020 due to fraud. What’s more, fraud’s effects don’t begin and end at financial losses. It also hampers the companies’ reputation and the customer experience, which can, in turn, cost even more.

So it comes as no surprise that banks, enterprises, and financial institutions try all available means of fraud prevention. AI is one such method, as it allows blocking a user’s request or even accessing their account if the system detects potentially fraudulent activity. As a result, AI reacts to suspicious activity before the fraud happens.

AI in FinTech: Wrap-Up

AI in FinTech is used for a wide array of purposes: lending decision making, customer support, fraud detection, credit risk assessment, insurance, wealth management, and much more. Modern FinTech companies adopt AI for enhanced efficiency, improvised precision levels, and high-speed query resolution.

AI in FinTech drives innovation, leading to personalized, fast, and secure services with higher customer satisfaction and global reach. So artificial intelligence in financial markets is here to stay!

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