Top tips is a weekly column where we highlight what’s trending in the tech world today and list out ways to explore these trends. This week, we’re examining four use cases for AI in the ever-growing FinTech sector.

The FinTech sector has transformed the discussion around the financial services industry from top to bottom. It has spearheaded a shift towards more streamlined operations for financial institutions and has also made otherwise troublesome and frustrating processes so much more convenient for the end user.

Since it first burst on to the scene in the early 2000s, the FinTech sector has grown rapidly and has seen a host of innovations over the years. With the global tech landscape being dominated by conversations revolving around AI, it was only a matter of time before it became a talking point in the FinTech sector, as well.

Here are four use cases for AI in the FinTech sector

1. Fraud detection

Fraud is always going to be a major risk faced by financial institutions. Fraudsters are devising increasingly complex and convincing methods of committing fraud, and financial institutions need to ensure they are constantly upgrading their detection mechanisms to keep up with these advancements. 

Various steps have been taken over the years to combat financial fraud. FinTech platforms, for example, enable service providers to monitor transactions and user activity. However, traditional methods of fraud detection are prone to false positives, which may result in the blockage or cancellation of legitimate transactions.

Integrating AI models can enable FinTech platforms to leverage capabilities such as machine learning, predictive analysis, and behavioral and pattern recognition to create more accurate fraud detection measures while also minimizing false positives.

2. Generative-AI-driven customer service

FinTech companies can use generative AI models like GPT-4 to provide customers with conversational customer service. The NLP capabilities of these AI models enable the user to have almost human-like and personalized interactions around the clock with an AI agent. The services provided by these agents can vary depending on the nature of the request: They could help with simple, repetitive incidents or service requests, only escalating to human customer service agents in special circumstances.

3. Smart financial recommendations (robo-advisors)

Robo-advisors are a burgeoning AI-driven innovation in FinTech. These advisors are either discrete FinTech platforms of their own or virtual assistants included in other FinTech platforms. Robo-advisors use algorithms to analyze data gathered directly from the user (usually in the form of a survey when setting up their account) and their investment goals and/or historical investment data. This data is then used to provide personalized investment advice and recommendations. These tools can also help with decision-making and be used to improve a user’s financial literacy. 

This AI-driven technology is extremely cost-effective for both the service provider and customer since it can greatly reduce the need for human investment experts, especially in the case of simple, straightforward, and more traditional investment methods like stock trading. These tools can also make use of generative AI to provide more conversational services, as described in the previous point. However, one thing to note about robo-advisors is that they won’t completely replace human investment experts as they aren’t yet equipped to deal with unconventional investments or complex services like estate planning or complicated tax management.

4. Algorithmic trading

Algorithmic trading is an automated, rule-based approach to financial trading. This method of trading involves using highly complex algorithms to automatically execute the best possible trade using the available data, both current and historical (and sometimes even future data gathered from predictive models). A key focus of algorithmic trading is to execute trades at high speeds, much quicker than humanly possible.

Complex AI and machine learning algorithms can also be used to analyze vast datasets relating to financial markets to further streamline this process. These AI models can be used to identify current and future patterns in the market and make accurate predictions, adding another layer of accuracy and reliability to ensure the most efficient execution of the trade possible.

AI integration is the future of FinTech

AI paves the way for FinTech to become more efficient, enabling platforms to provide instant, more personalized and secure service to users, as demonstrated by these use cases.

And following the trends we’re seeing in other sectors, AI promises to further transform the FinTech sector, enabling financial institutions to better prepare themselves for the challenges that lie ahead. We’re only just beginning to scratch the surface of AI’s potential to transform the wider financial services industry as a whole.