Artificial intelligence is one of the trending topics in the world of technology. Apart from the mainstream media attention, public discourse around AI has been gaining momentum. The different use cases of artificial intelligence, such as chatbots, predictive analytics, self-checkout stores, and self-driving cars, have grabbed the attention of businesses and the general public worldwide. Interestingly, the use cases of artificial intelligence in fintech have become one of the most noticeable topics of discussion among experts. According to a survey by market research firm McKinsey, around 56% of organizations use AI in one of their business functions. Therefore, it is reasonable to assume that fintech companies could also use AI and capitalize on its broad assortment of benefits.

How many of you thought that artificial intelligence is a new technology in the domain of fintech? As a matter of fact, AI, machine learning, big data analytics, and neural networks have helped fintech companies for almost two decades. Most recently, the discussions about the future of AI in fintech have gained traction due to the emergence of generative AI

Disruptive technologies such as blockchains and IoT have sparked the transformation of conventional finance to fintech. However, the impact of AI in the fintech industry could open new doors to innovation. Let us find more details about the different use cases of AI in fintech to understand the importance of AI.

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Importance of AI in Fintech 

Before you discover the use cases of AI in fintech, it is important to learn about the relationship between AI and fintech. How could AI help fintech apps? The top fintech AI use cases provide a clear impression of the ways in which artificial intelligence helps fintech apps deal with large volumes of data records. 

AI could help in the automation of repetitive tasks, thereby freeing up the time and resources for focusing on high-level objectives. Artificial intelligence could also emerge as a powerful tool for supporting customer service management tasks. In addition, AI could also reduce the risk of fraud alongside offering flexibility for implementation in back-office tasks as well as front-end activities. 

The role of AI in improving fintech serves as the motivation for financial institutions to increase their investments in the development and acquisition of AI-powered fintech solutions. Market research predictions have suggested that fintech AI use cases could garner investment of $22.6 billion annually by 2025. The power of AI in fintech could also bring many other noticeable advantages in terms of savings. 

For example, AI chatbots could help the fintech industry in saving almost $7.3 billion in 2023. On top of it, fintech use cases in the field of insurance claims management could save around $1.2 billion for companies in 2023. At the same time, the potential of new disciplines of AI, such as generative AI, have the capabilities to revolutionize artificial intelligence. Apparently, generative AI could contribute around $7 trillion to the global GDP within a decade.

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Use Cases of AI in Fintech 

Fintech startups have accumulated more than $14 billion as funding within the first quarter of 2023. The continuously expanding scale of innovation in the world of fintech has enabled the introduction of new technologies. The answers to “What is the use case of AI in fintech?” would point towards the impressive potential of AI. 

Artificial intelligence could improve automation, efficiency, speed, and accuracy in decision-making. How can you ensure that artificial intelligence delivers better value to fintech users? For example, generative AI has emerged as a promising tool for enabling mainstream adoption of AI in fintech. Here is an overview of the top use cases of AI in fintech, alongside their important implications. 

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  • Improvements in Security 

The foremost use case of artificial intelligence in the domain of fintech points to the improvements in data security. Banks and different types of fintech companies use AI-powered chatbots to offer customer support services. Interestingly, these use cases would define the future of AI in fintech through the development of better models for improving security. 

For example, chatbots can be trained for tasks such as allowing additional access privileges or resetting lost passwords. Artificial intelligence could also improve security in fintech through the use of fingerprint, speech, and facial recognition. The additional layer of security creates formidable challenges for hackers to exploit the application as compared to the standard passwords. As a matter of fact, AI-based security solutions would become the trusted alternative to usernames and passwords for facilitating security.

The impact of AI on fintech use cases would also reflect on the possibilities for leveraging generative AI. Fintech companies can use generative AI to safeguard their digital infrastructure against a broad assortment of vulnerabilities and threats. The applications of AI in strengthening security within fintech would focus on cybersecurity, PKI-based identity, DNS security, blockchain security, and DDoS protection. 

With the help of generative AI, companies could strengthen their defenses, which would ensure the confidentiality and integrity of sensitive data. In addition, generative AI could serve as a promising tool for improving user authentication optimization of communication interfaces by leveraging two-factor authentication. 

The effectiveness of AI use cases in the domain of fintech also points to the possibilities of leveraging biometric security. As a matter of fact, the top fintech AI use cases revolve around biometric security, which has become an essential addition to the fintech industry. With correct implementation, biometric security could streamline the customer experience while ensuring better speed and efficiency. 

For example, the Japan-based Seven Bank has planned to replace existing ATMs with ATMs featuring QR code scanners and biometric authentication technology. With biometric technology, along with facial recognition features, Bluetooth functionalities, and QR code scanning, fintech companies could offer seamless banking experiences.

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  • Fraud Detection

The next important addition among the use cases of AI for fintech points to improvements in fraud detection. Malicious financial behavior has skyrocketed by huge margins in the last few years. You must have come across news headlines on credit card scams or loan application scams alongside illegal wire transfers and fraudulent insurance claims. 

How could fintech AI use cases help in such scenarios? Fraudulent banking transactions lead to losses of millions of dollars for individuals and corporations. Apart from financial losses, fintech companies would also have to deal with damage to their reputation and unpleasant customer experiences. 

Most of the venture capitalist funding for AI in the fintech industry focuses on targeting cybersecurity and fraud. The burden of monitoring multiple financial transactions in a day could create setbacks for accurate analysis of each transaction. Manual identification of potentially suspicious activities is a challenging task. 

On the other hand, fintech use cases with AI could provide real-time monitoring of financial transactions. AI algorithms could help in discovering strange patterns and abnormalities that do not align with the daily spending habits and behavior of users. Fintech companies could ensure faster and more accurate responses to potentially fraudulent transactions with AI. 

Artificial intelligence is a revolutionary technology in fintech for fighting against financial fraud. Machine learning algorithms could help in analyzing millions of data points within sectors to identify discrepancies in transaction patterns. Upon isolation of the discrepancies, it is easier to find out whether they were errors or clear signs of fraud.

Mastercard has launched its latest Decision Intelligence technology for analysis of historical payment data for each customer. It could help in real-time detection and prevention of credit card fraud. The applications of artificial intelligence in fintech for improving fraud detection would also help in detecting new types of fraud. For example, AI could help in detecting instances of exploitation of sign-up bonuses for creating new credit card accounts.

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  • Improvements in Customer Service 

Almost every user has developed the preferences for obtaining faster responses to their queries. Financial institutions should be available for their users 24/7 throughout the year to offer answers to user questions. If a customer cannot find a solution to a problem with fintech services, you should be prepared to solve it as quickly as possible. 

The responses to “What is the use case of AI in fintech?” would show that artificial intelligence could improve the availability of fintech companies for customers. Rather than employing human agents, AI-powered chatbots could always be at the service of customers. As a result, fintech companies could avoid the risks of losing customers to their competitors. 

AI-powered chatbots, interfaces, and virtual assistants could reduce the workload of customer service for fintech companies. Artificial intelligence could help in managing common user concerns and addressing the general questions of users. The discussions about the future of AI in fintech with such use cases prove how artificial intelligence could reduce workload. 

Customer service teams could take a break from answering the same questions throughout the day and use their skills to address complex concerns. In a way, artificial intelligence helps customer service teams in becoming more responsive to the needs of customers. 

Artificial intelligence could also offer promising improvements in customer service in the domain of fintech through advanced sentiment analysis. The advanced sentiment analysis functions focus on the analysis of the client’s experience alongside their capabilities for detecting gaps. 

AI-based technologies remove the gaps in communication between users and the fintech industry. The outline of top fintech AI use cases also highlights the impact of personalization, which increases brand loyalty. AI systems could collect and evaluate customer data from fintech apps for developing personalized services for customers. 

Some of the examples of personalized services include custom financial advice and pre-approved items. The functionalities of AI in fintech could also serve simple improvements in customer services, such as real-time tracking of financial expenses and goals.

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  • Robotic Process Automation 

The review of fintech AI use cases focuses primarily on the front-end experiences of AI for fintech users. Did you know that AI could help in empowering fintech companies? Fintech companies could utilize AI to streamline the complexities associated with the management of general back-end tasks. Robotic Process Automation or RPA involves the use of ‘smart chatbots’ for end-to-end automation and streamlining of back-end office procedures. 

For instance, RPA could help in the automation of different tasks like security checks and onboarding for new customers. In addition, RPA could also support the automation of inbound calls for general queries and processing mortgages, credit cards, and account closures. RPA could also help in simplifying the trade finance operations and loan application processes.

One of the most notable examples of RPA in fintech is the COiN chatbot by JP Morgan Chase. The chatbot showed the power of artificial intelligence in fintech by completing around 360,000 hours of financial work within seconds. In the long run, RPA software would help banks with cost-effective, efficient, and faster operations.

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  • Predictive Analytics

The use cases of artificial intelligence in the domain of fintech also revolve around the potential of predictive analytics. Accurate forecasts are an important requirement in the fintech industry for timely and accurate decisions for investing or borrowing. On the contrary, the future of AI in fintech would depend on the limitations of internal ERPs. Predictive analytics utilize machine learning, statistical modeling, and data mining to predict future events. Artificial intelligence could pick up the hidden patterns that are not visible to humans. 

AI could help in making the most of emerging business trends in fintech for optimizing business models and obtaining valuable insights. It is also important to identify the effectiveness of predictive analytics in drawing accurate forecasts for a company’s activities.

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Final Words 

The outline of the important use cases of AI in fintech shows that artificial intelligence could transform the fintech industry. Users should notice how artificial intelligence helps improve customer service, fraud detection, security, personalization, and prediction accuracy. The foremost reason to learn about the top fintech AI use cases is the capability of artificial intelligence to process massive volumes of data within seconds. 

AI could detect patterns that are practically invisible to the human eye. At the same time, you must notice how the use cases of AI in fintech could offer user-centric advantages. Learn more about the fundamentals of fintech and how artificial intelligence could improve fintech now.

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*Disclaimer: The article should not be taken as, and is not intended to provide any investment advice. Claims made in this article do not constitute investment advice and should not be taken as such. 101 Blockchains shall not be responsible for any loss sustained by any person who relies on this article. Do your own research!