The 18 Top Use Cases of Artificial Intelligence in Banks

Today over 60 marketplaces, 20 regulators, and 160+ market participants are powered by the technology in 65 countries, making Nasdaq the world industry leader. It involves a thorough examination of a site’s geographical features, climate conditions, historical background, and legal constraints. This information is usually collected in various formats like images, videos, and texts and is stored in Building Information Modeling (BIM) systems.

• She has a keen interest in topics like Blockchain, NFTs, Defis, etc., and is currently working with 101 Blockchains as a content writer and customer relationship specialist.
• Generative AI, powered by advanced machine learning techniques, has emerged as a transformative technology with profound implications for businesses across various industries.
• The bank uses machine learning to identify patterns in customer data that may indicate fraudulent activity.
• As discussed earlier, generative AI in financial services and banking empowers financial planners with insightful data.

The company has developed an AI-based decision-making platform to give customers personalized investment advice. Facial recognition is a form of biometric authentication that uses AI algorithms to analyze unique facial features to identify an individual. Banks can use this technology to verify the identity of customers when opening an account, accessing their account information, or conducting financial transactions.

AI in Personalized Banking

With artificial intelligence already making considerable strides in customer support for banks and fintech businesses, customers are growing accustomed to receiving prompt replies at any time of day. To facilitate transactions and answer questions, financial institutions must be accessible around-the-clock, every day of the week. The fintech sector can save billions of dollars in resources, labor costs, and capital using AI-powered solutions. Given the labor cost, manual processes frequently take longer and cost more money. As AI technology answers most questions, customer service teams spend less on hiring new employees.

With over 20 years of proven experience in data management and AI/ML, Kanerika offers robust, end-to-end solutions that are ethically sound and compliant with emerging regulations. Our team of 100+ skilled professionals is well-versed in cloud, BI, AI/ML, and generative AI, and has integrated AI-driven solutions across the financial spectrum, ensuring institutions harness AI’s full potential. Gen AI is modernizing workflows tailored for banking systems, generating reference architectures like Terraform, and crafting detailed plans.

Q. How is AI improving customer experiences in banking?

ML models can find anomalies in user behavior and quickly spot any fraud attempts, as well as questionable transactions. For instance, by learning from historical fraud cases, ML algorithms can detect unusual patterns, such as sudden high-value transactions or transactions in unusual locations, that may indicate fraudulent activities. In the realm of site analysis, it’s often the design team that takes the lead. To gather crucial insights that will pave the way for a meticulously crafted project plan.

Customer onboarding is a critical process for banks to attract and retain customers, but it can be time-consuming and complex. ChatGPT can assist banks in improving customer onboarding by automating much of the process, reducing wait times, and streamlining the customer experience. ChatGPT can help banks comply with regulatory requirements by monitoring bank transactions and identifying potential compliance violations. This can help banks avoid costly fines and penalties and protect their reputation.

Top 10 Biggest US Banks by Assets in 2023

AI and Machine Learning solutions can provide an array of tools to improve customer satisfaction. Similarly, banks are using AI-based systems to help make more informed, safer and profitable loan and credit decisions. Currently, many banks are still too confined to the use of credit scores, credit history, customer references and banking transactions to determine whether or not an individual or company is creditworthy. Dataiku built a full Generative AI Use Case Collection using experience gained from working with our more than 500 customers. From risk management to customer service, this collection of use cases goes beyond the boundaries of traditional AI-powered chatbot functions. Anti–money laundering (AML) efforts represent a critical area where AI and ML are necessary.

This is why automation has paved the way to make life easier for customers in the banking industry. Most of such routine activities can be automated and streamlined using conversational AI in banking. Finally, the numerical accuracy of generative AI in banking is a limitation to be aware of.

Capital One has also integrated AI into its credit card fraud detection service, using predictive analytics and machine learning to identify suspicious activities. Finally, cost and return on investment (ROI) remain significant barriers to adopting AI in the finance and banking industry. While AI systems can increase operational efficiency and reduce costs, the initial investment required can be significant. Banks must carefully assess the costs and benefits of implementing AI systems to ensure a positive ROI. Artificial intelligence is transforming the banking industry using Robotic Process Automation (RPA) algorithms to automate repetitive tasks and increase operational efficiency.

For instance, Gartner believes the majority of companies going forward will be using AI and chatbots in the years ahead. Indeed, one report even suggested 85% of banks would be using bots by the end of 2021. This is why many of the CCaaS systems and tools available for CX today focus on engaging, connecting, and aligning teams, so they can better serve clients. With competition fierce in the financial services company, a strong CX strategy can help to align staff members, so they can offer support and guidance faster, and more effectively than ever. Additionally, by modeling numerous market scenarios, generative AI in banking enhances risk management and enables banks to make wise decisions and safeguard their portfolios. Additionally, it is essential to algorithmic trading and investment techniques because it helps to improve algorithms and make better investment choices.

Conversational AI Use Cases in Banking to Streamline Operations

The amount of information generated is enormous, so collecting and registering it, as well as determining the relationship between the collected data, becomes a difficult task. A Keboola client working in e-commerce suspected fraudsters infiltrated their platform. Rohlik, the e-commerce unicorn, helps connect food producers to retail consumers via its food delivery platform. From self-writing tweets to generated visual images, AI can be your in-house production team assistant, serving you content at scale with a couple of keyboard strokes. Machine learning techniques can be used to improve a wide range of sales practices. Despite the optimism, the path to implementing generative AI in banking use cases remains fraught with challenges.

It offers tremendous opportunities for enterprise synchronization, breaking down the silos of operations and data for risk, finance, regulations, customer support, and more. Consolidating datasets in one place enables advanced analytics for integrated analysis. Companies can work entirely in the cloud or partially with an on-premises system. Leading cloud providers offer innovative products-as-a-services that help drive business and operating models to increase revenue, boost customer understanding, contain costs, and deliver relevant products faster.

When you use the right technology to map your customer journey, you can provide a consistent level of excellent customer service, no matter which touchpoint your client is using. Fail to understand the end-to-end customer journey and implement tools for loyalty, and you risk losing your entire community. Quantitative trading refers to employing large data sets to determine the patterns that can help to make strategic trades. AI-powered tools can help financial institutions to analyze any amount of complex data sets faster and more efficiently. This algorithmic trading process can thereby help to automate the trades and save valuable time and resources for the services provider.

• In fact, there may be a drift from passwords, usernames, and security questions in the coming years in favor of more seamless and accurate fraud prevention techniques.
• The back and middle offices of the investment industry and other financial services also benefit from the technology.
• Time is money in the finance world, but risk can be deadly if not given the proper attention.
• Scaling generative AI applications for banks require access to large volumes of high-quality training data.
• This volatility makes it difficult to establish a stable crop production plan, much like trying to build a house of cards in the wind.
• For example, a banker considering a loan to a small business might use AI to access data on the business’s financial history, industry, and local economy.

Technological advances and changing consumer habits are introducing new challenges, and even questioning how banks can maintain trust with diminishing human interactions, and physical currencies. They are present 24 hours a day and are also capable of giving a customized experience to the user accordingly. In this way, many complex tasks have become easier by implementing Artificial Intelligence.

This provides valuable insights into customer preferences and sentiments, enabling organizations to proactively address customer concerns and improve service quality. In the past, banks have relied on manual processes to detect and prevent fraud. However, these processes are often time-consuming and labor-intensive, and they can’t always keep up with the ever-changing landscape of cyber threats. AI-powered fraud detection systems can help banks keep pace with the latest threats, and they can do it accurately. The banking industry is amidst a digital transformation to meet customer expectations. Banks are investing heavily in enhancing the digital capabilities of their businesses.

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