By: Flaka Ismaili    September 22, 2023

Generative AI Use Cases in Finance and Banking

This accessibility simplifies banking tasks and fits seamlessly into customers’ busy lives. Enhancing customer experience is at the forefront of AI’s impact in banking and finance. AI-powered systems are adept at tailoring recommendations, content, and services to individual customer preferences, providing a level of personalization that was previously unimaginable. This not only elevates customer satisfaction but also introduces innovative value propositions.

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.

RBR Data Services

Our teams are experienced in DWH architecture, ETL processes, aggregation, data migration, database maintenance, and retirement of legacy applications. Integration of AI technology in banking apps will help in making the application a regulatory complaint. AI’s deep learning and NLP techniques will track and identify the new data privacy and regularity rules that apply to their businesses. It will improve the efficiencies of compliance systems and make the data compliant with the rules and regulations. Driven by its intelligent capabilities and a range of automation abilities, AI adoption in banking and financial services sector is on the rise. According to the market research reports, the global market value of artificial intelligence (AI) in banking industry is expected to reach USD 293 billion by 2030 from USD 90 billion in 2021.

Your customers will thank you, and your competitors will wonder how you did it. Your customers will appreciate the simplicity and efficiency of Voice banking when it comes to resolving issues with their cards. Imagine the delight your customers will experience when they realize they can have instant access to information. These applications, known or Internet bots, are programmed to process automated tasks.

Inventory Monitoring and Management

It strengthens the mobile banking facility by managing basic banking services. They get notification instantly for any suspicious transaction as per their usual patterns. Secondly, it is easy for a banking app integrated with AI-related features to show services, offers, and insights in line with the user’s behavior. What’s more, the app handles the advice and communication part by analyzing the user’s data. Banks can give online wealth management services and other services by integrating AI advancements into the app. Millennials rely heavily on mobile banking, which means that AI-powered banking mobile apps can attract them.

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. To learn more about predictive analytics, check out Apexon’s Advanced Analytics and AI/ML services or get in touch with us directly using the form below. Recent statistics show the importance of AI in the financial services industry with fraud detection ranking as the most important use case of AI among respondents. It’s no wonder with 2,527 cyber attacks worldwide in the financial industry in 2021. Continuing to dominate concerns are credit card fraud, financial breaches, and money laundering.

Customers can enjoy uninterrupted and efficient service around-the-clock with robots replacing front-office workers. Bank unlocks and analyzes all relevant data on customers via deep learning to help identify bad actors. It’s been using this technology for anti-money laundering and, according to an Insider Intelligence report, has doubled the output compared with the prior systems’ traditional capabilities. Eno launched in 2017 and was the first natural language SMS text-based assistant offered by a US bank. Eno generates insights and anticipates customer needs throughover 12 proactive capabilities, such as alerting customers about suspected fraud or  price hikes in subscription services.

Prior to the pandemic, the U.K.-based Bennett said she could be in a different country every day for work. Her credit card company’s fraud detection had gotten so good that her card was never declined as she traveled from one geography to another. The one instance when there was fraud — someone tried to buy a computer as she was buying cheese in Madrid — she was contacted immediately. To improve customer service across all its branches, Bank of America decided to implement a virtual AI-driven assistant.

One example of a successful chatbot is Erica, Bank of America’s virtual financial assistant which launched in 2018. Since that time, Erica’s interactions with BOA clients have exceeded the 1 billion mark. TQ Tezos leverages blockchain technology to create new tools on Tezos blockchain, working with global partners to launch organizations and software designed for public use. TQ Tezos aims to ensure that organizations have the tools they need to bring ideas to life across industries like fintech, healthcare and more.

For example, banks can use  AI to forecast the inflation rate in the medium term and make appropriate adjustments to the interest rate. Banks will also benefit from the automated features that AI bring into the conventional banking workflow. With AI, banks can maintain a 24/7 presence on different channels to handle customer inquiries and resolve issues. This way, AI assists human support personnel in answering common questions, allowing the latter to focus on complex cases. There are numerous ways that AI could be used to enhance risk management practices (see table 4). Yet, poor deployment of AI could equally lead to reputational and operational risks that could be detrimental to our view of a bank’s risk position.

This will, in turn, help banks manage cybersecurity threats and robust execution of operations. To avoid calamities, banks should offer an appropriate level of explainability for all decisions and recommendations presented by AI models. Banks need to understand, validate, and explain how the model makes decisions. Banks need structured and quality data for training and validation before deploying a full-scale AI-based banking solution. Quality data is required to ensure the algorithm applies to real-life situations. The wide implementation of high-end technology like AI is not without challenges.

This automation empowers banks to streamline their processes, cut operational costs, and ultimately bolster their bottom line. In the finance industry, including banking, AI transforms operations by optimizing decision-making, elevating customer experiences, boosting efficiency, and fortifying security. This technology reduces costs through streamlined processes and ensures a competitive edge in the dynamic digital landscape.

FAQs- Top 25 Fintech AI Use Cases

As automation increases, maintaining a personalized touch in customer interactions remains vital to fostering trust and strong customer relationships. To demonstrate this, let’s look at some of the most prominent examples of AI in banking in the real world. With biometric authentication like voice recognition, customers can be confident that their transactions are secure and protected from unauthorized access.

Read more about Top 7 Use Cases of AI For Banks here.

Flaka Ismaili

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