Top 5 AI use cases for fintech in 2024

Top 5 AI use cases for fintech in 2024

Top 5 AI use cases for fintech in 2024

AI is the real deal behind the rapid development of firms in the fintech industry. As per the stats from McKinsey, 56% of fintech firms have already implemented AI in at least one of their business functions in 2023. These AI-assisted operations might include everything from better customer service to automation.

Let’s delve into this blog and understand the role of AI in the transformation of the Fintech Industry through innovation and advanced use of AI applications. And explore what are the top 5 AI Use cases for fintech in 2024.

Top 5 AI Use Cases in Fintech, which you may not aware of!

Detection and Prevention of Fraud

AI applications can indeed enable financial institutions to proactively recognize and negate fraud. By processing vast amounts of data, integrated AI systems are frequently useful in recognizing unexpected patterns and abnormalities. Besides that, it has a massive capability for processing massive amounts of data, recognizing trends, and adapting to emerging threats.

Example: Two of the leading fintech companies that use AI for fraud prevention and detection are Square and Feedzai. As per the research, both of these companies utilize AI algorithms in real-time to catch anomalies and suspicious activities. Thanks to the valuable integration of artificial intelligence technology, they have been proactive in protecting their merchants and consumers using their payment system.

Aside from that, these platforms also evolve, learn from recent data, and reinforce themselves to ensure adaptive and robust fraud prevention.

The core precepts that help fintech applications that use AI to help build a solid fraud prevention system are, firstly, machine learning and secondly, predictive analysis. These systems can detect potentially fraudulent activity by analyzing historical data and accurately detecting irregularities.

Fraud detection AI systems also employ different learning methods, such as rule-based systems and predictive machine learning, to continuously analyze data streams. The results are enhanced accuracy, quicker detection, and a decrease in false positives.

Not just that, real-life examples have also proven that AI is effective in identifying and mitigating online fraud. Nevertheless, top fintech companies have in no time implemented AI-powered solutions into their systems, ensuring that the fintech industry continues to upgrade and issue security to financial transactions. Consequently, the fintech industry will remain strong and resilient in tackling the fraud challenges with the help of our friend Integrated AI.

Credit Scoring: The Transforming Lending Function

Traditional credit scoring methods have long used rule-based, inflexible, and limited-scope measures that often failed to incorporate the vast volume of available data about potential borrowers. With the advent of AI, the credit scoring landscape is about to change massively by utilizing advanced data analytics and machine learning to transform the credit evaluation process.

Using AI to enhance credit scoring, fintech organizations were able to enhance credit scoring processes and evaluate the accurate financial health of potential borrowers.

Example: AI also impacts credit scoring tools, which help lenders devise specific loan products based on individual borrowers’ profiles. Such data included credit history, financial goals, and spending patterns.

One certain example is the company named Upstart. Upstart utilized machine learning and AI to evaluate creditworthiness by taking into consideration alternative data, such as banking activity or education history.

Aside from that, AI credit scoring systems have been implemented in e-commerce platforms, rental property management corporations, and insurance companies, demonstrating AI’s disruptive potential in examining creditworthiness across industries.

Algorithmic Trading: Evolution of Investment Strategies with AI

AI-based algorithmic trading entails more than just evaluating vast volumes of data in milliseconds and allowing decision-makers to make split-second judgments to maximize revenue and increase trade operations efficiency. It is, nonetheless, crucial for forecasting market volatility and changing trading approaches in real time.

This predictive capability can help traders minimize losses and take advantage of favorable emerging market trends, helping in the fast-paced world of financial trading and enhancing their competitive edge.

Many established firms from a fintech background, including start-ups, are using AI heavily for algorithmic trading.  Not only are they improving trading strategies, managing risks, and streamlining financial improvisations with AI, but more than that, they are setting the benchmark for encouraging the widespread use of algorithmic trading.

Example: We present Citadel Securities and Jane Street Capital as a case study here. They have been highly successful in utilizing algorithmic trading and mathematical techniques in their trading platforms to minimize the losses of their clients.

With the growth of AI, algorithmic trading is continuously transforming financial markets and bringing new prospects for traders.

AI in Risk Assessment: Changing Fintech Practices

The disruption of AI in fintech has also in many ways influenced the risk assessment in the business. Now what we have is advanced predictive analysis and data analytics that identify potential risks and improve risk management processes.

Improved data analytics and AI-driven systems analyze large chunks of data, find patterns, and detect anomalies that human analysts may fail to catch. Fintech can use AI to implement risk mitigation strategies and make informed decisions. With the help of automation, they can also save resources and time, effectively managing the risk.

Example: Virtu Financial Inc. is a financial trading company that uses AI risk assessment in algorithmic trading and portfolios. They were able to enable their traders to prevent losses with the help of AI risk assessment.

Customer Support and Personalization With Chatbots

Modern finance companies frequently use virtual assistants to interact with their customers. AI has also penetrated every business CRM and customer support function. Customer engagement and satisfaction are being improved by AI-powered chatbots in financial businesses. 

Fintech companies can now offer personalized guidance, help customers make crucial financial choices, and improve customer experiences, driving business growth.

Beyond that, AI technology in these applications can offer personalized financial advice and services for customers. Machine learning algorithms are also helpful in identifying consumer behavior to help the financial business in decision-making. They also offer financial recommendations, lending choices, and strategies for investment tailored to each customer’s personal needs. This will also help companies enrich customer experiences and promote customer loyalty.

Example: Credit Karma is a top fintech company that uses AI to provide personalized credit scores and recommendations based on users’ credit history and financial behavior.

Like Credit Karma, other banking and insurance companies are also using AI-powered chatbots and virtual assistants to enhance their customized services and improve consumer support. Using these real-life samples, you now know how AI is helping companies from diverse sectors of business.


With the rising demand for better consumer experiences and the need to deliver more secure, efficient, and personalized services, fintech services will continue depending on integrated AI.

AI trends will keep influencing the Fintech industry and give rise to personalized, secure, and data analytics-oriented approaches in 2024.

However, new technology creates new problems. The data privacy problems, ethical concerns, and possibility of bias originating from widespread AI adoption are problems that need to be addressed for the responsible and transparent use of AI.

Therefore, for AI to fulfill its complete potential, it must ensure customer convenience, data security, and efficiency along with ethical integration and responsibility.

Modern businesses need AI; they must stay competitive and enhance their financial services. Companies are evolving, setting new benchmarks in customer support, and tackling modern challenges. AI will usher in the new age, ensure the success of the fintech industry, and deliver growth and prosperity., an Artificial Intelligence Services company in Abudhabi provides advanced AI solutions to accelerate digital transformation in fintech sector. Saal has developed AI use cases to prevent fraudulent activities, predict possible breaches and moreover utilize financial data to make favourable decisions for your fintech business. Get in touch with us to understand our AI offerings in the financial sector at