AI in Banking: Use Cases

AI recognition in finance started in 1980 already, but it became a strong trend relatively recently. Undoubtedly, AI already infiltrated most business spheres. But how can you, as a digital bank owner, executive, or an individual planning a neobank startup, put it to good use before competitors will? 

Well, for starters, let’s embrace the fact that 35% of companies already take advantage of AI, and with the CAGR of the AI market amounting to 23.37%, the proportion is likely to rise. So, the very first big idea here is that you should not only intend the adopt some AI functionalities as many did but rather take a leading role in exploiting the digitalization opportunities it has to offer. 

To gain the necessary knowledge and, desirable, a certain direction, let’s review 7 use cases of successful AI usage in real life and the flow for adoption of the smart technology. Additionally, you’ll discover AI trends in banking for the year 2024 and above. By the end of this post, you should come up with an understanding of how and in what way AI use in banking can be actually benefitial in your current business scenario. 

Benefits of AI in Banking

First, let’s get to know the state of AI in the banking market to understand where we actually are and where the situation is heading to:

Image sources: Hostinger.com, EmerGenReserach.com, FactMR.com

Let’s proceed with examining the potential value that one or another AI, depending on its purpose, can offer your business if successfully developed and implemented:

• Increased efficiency in operations
• Enhanced customer experience
• Reduced operational costs
• Improved risk management
• Faster and more accurate decision-making
• Enhanced security and fraud prevention
• Better compliance with regulatory standards

If you don’t have AI expertise in-house or need comprehensive help with determining the direction of your AI adoption strategy and its execution, the right call is to request help from professionals. 

Now, when the value of adopting AI in banking sector is revealed, let’s find out what real-life AI applications in banking are there. After all, if you actually intend to come up with a business idea that will make an impact on your business, you should understand extremely well what solutions are already there. 

7 Artificial Intelligence Use Cases in Banking

The businesses listed below have come a long way to actually start using AI in their business operations, despite already having large-scale infrastructure, a vast user base, and probably internal issues with the adoption of something new. Not to mention the risk of substantial financial and credibility losses in case of failed initiatives. For smaller companies, the adoption of AI can be relatively painless, but only if they manage to plan their strategy and handle resources in the right way. 

Here are the AI use cases in banking to examine thoroughly: 

#1 Fraud Detection: Use Case of JPMorgan Chase Implementing Advanced Fraud Detection Mechanisms

AI in fraud detection involves analyzing transaction patterns and user behaviors to identify anomalies that may indicate fraudulent activities and result in successful financial crimes if left unprevented.

Business description and intent:

JPMorgan Chase, a leading worldwide financial services corporation, is noted for its significant emphasis on technology and innovation in banking. It hoped to use AI to improve the security of their transaction processing. 

Use case details:

JPMorgan Chase adopted AI by incorporating deep learning models into its AI-based transaction monitoring systems. These algorithms are trained on past transaction data and may detect detailed patterns and abnormalities that indicate fraud.

Results achieved:

This comprehensive method to fraud detection has decreased false positives dramatically, increased the speed and accuracy of identifying fraudulent transactions, and improved the overall security of JPMorgan Chase’s financial services.

*You can additionally discover about this usage of smart technology from our post on utilizing machine learning against financial fraud. *

#2 Chatbots and Virtual Assistants: Use Case of Bank of America’s “Erica” for Customer Service

AI-powered chatbots and virtual assistants in the banking industry are designed to automate customer support, efficiently manage inquiries, and execute basic banking operations.

Business description and intent:

Bank of America, a leading bank in the United States, embarked on a mission to transform the landscape of banking customer service. Their research found that a significant majority, 84%, of consumers who interacted with virtual assistants reported satisfaction with their experiences.

Use case details:

Bank of America marked a significant advancement in banking with the launch of “Erica,” an AI-driven virtual assistant. Equipped with natural language processing and machine learning capabilities, “Erica” is adept at performing various customer support functions such as addressing transaction inquiries, assisting with bill payments, and understanding user commands via voice and text interactions.

Results achieved:

The use of “Erica” has considerably improved Bank of America’s customer service efficiency and quality. It has decreased the strain on human customer care representatives, delivered quick and accurate help, and increased overall customer happiness.

Discover more about how to use AI-powered ChatGPT chatbots and virtual assistance in customer service from another our blog post.

#3 Credit Scoring: Use Case of HSBC using AI for Improved Credit Scoring Models

AI in credit scoring involves using machine learning algorithms to more accurately assess creditworthiness than traditional models.

Business description and intent:

HSBC, a worldwide leader in banking, focused its efforts on harnessing artificial intelligence to refine its process for making credit decisions. 

Use case details:

HSBC crafted machine learning models to increase both the accuracy and the inclusiveness of credit assessments, especially aiding customers with sparse or non-traditional credit histories. The technology can analyze not only traditional credit history but also non-conventional data such as spending behaviors. ML models created use a combination of regression analysis and decision trees, aiming to more precisely predict the likelihood of loan defaults.

Results achieved:

HSBC’s adoption of AI in credit scoring has led to the provision of more customized credit solutions, a reduction in default risks, and the extension of credit access to a wider array of customers. This innovation in credit scoring practices has positioned HSBC as a more inclusive and progressive player in the financial services industry.

You can discover additionally about risk scoring and other AI implementations from another our blog post.

#4 Risk Management: Use Case of Goldman Sachs implementing AI Systems for Risk Assessment and Management

AI in risk management is used to analyze complex financial data and assess risks in banking operations.

Business description and intent:

Goldman Sachs, a leading global firm in investment banking and management, renowned for its expertise in securities and investments, aimed to enhance its risk management capabilities using artificial intelligence.

Use case details:

The organization implemented a sophisticated AI platform designed to simplify the processing of vital documents required for regulatory reporting and compliance. A key aspect of this initiative was the automation of Qualified Financial Contracts (QFCs) reviews, aiming to fulfill the rigorous requirements of the Dodd-Frank Act.

Results achieved:

The integration of AI technology allowed this company to effectively manage and process large volumes of documentation while ensuring high precision. This initiative not only met their immediate needs for regulatory compliance but also prepared them for upcoming challenges like the LIBOR cessation and other regulatory changes.‘

For additional information on AI in risk management, read another our blog post. 

#5 Personalized Banking Services: Use Case of Wells Fargo Using AI for Customized Financial Advice

AI in personalized banking services focuses on providing tailored financial advice and insights to customers based on their individual transaction histories and financial behaviors.

Business description and intent:

Wells Fargo, a prominent financial services firm with a significant presence in the United States, sought to improve its digital products and client experience through AI-driven customization.

Use case details:

Fargo, a virtual assistant powered by Google Cloud AI, was added to Wells Fargo’s mobile banking platform. Fargo’s AI system is capable of giving relevant financial advice and insights, tracking spending habits, identifying suspicious transactions, and assisting with budgeting.

Results achieved:

Fargo has dramatically improved Wells Fargo clients’ digital banking experience by providing them with easy and tailored financial information. This has improved not only customer happiness but also financial literacy and better financial decision-making among users.

#6 Automated Trading: Use Case of Morgan Stanley Applying AI In Algorithmic Trading

AI in algorithmic trading involves using advanced machine learning techniques to analyze market data, predict stock movements, and make automated trading decisions.

Business description and intent:

Morgan Stanley, a worldwide investment bank and financial services firm, has been investigating AI applications in a variety of business areas, including algorithmic trading.

Use case details:

Morgan Stanley has used artificial intelligence to improve its trading algorithms. This entails utilizing data analytics and machine learning to better analyze market conditions and anticipate stock price fluctuations. These improved algorithms are capable of processing massive volumes of financial data and executing trades based on prediction models.

Results achieved:

While particular outcomes of Morgan Stanley’s AI-enhanced algorithmic trading programs have not been made public, the use of AI in trading algorithms is intended to deliver more efficient market research, faster decision-making, and perhaps better trading outcomes. McMillan, chief analytics, told CNBC that the solution will allow their 16,000+ advisers to access the bank’s huge data collection. Users say that the system can estimate a basket of stocks for a one-year return of more than 16%. 

#7 Process Automation: Use Case of Citi Bank Using AI for Streamlining Back-Office Operations

AI is utilized in process automation to refine and improve back-office functions, boosting productivity and minimizing manual workload.

Business description and intent:

Citi Bank, a key player in the worldwide banking and finance arena, has emphasized the adoption of artificial intelligence (AI) to amplify operational effectiveness and enrich customer experiences. The bank’s strategy involves pinpointing and mechanizing repetitive and tedious tasks, thereby liberating human resources for more significant and value-added activities.

Use case details:

Citi Bank used Automated Process Discovery (APD), which analyzes and maps the structure and processes of daily business operations using AI and machine learning. APD may gather and classify data on how employees interact with different systems and apps, delivering useful insights for process optimization. This technique is intended to supplement traditional process analysis and mining.

Results achieved:

Citi Bank was able to more efficiently and fully examine the automation possibilities of its operations after implementing APD. This has resulted in increased internal process efficiency and enhanced client experiences in a variety of areas, including speedier loan processing and credit checks. APD has also aided in the identification of targets for robotic process automation, ensuring that the appropriate processes are optimized and automated for considerable ROI.

Discover other ways to utilize AI, in particular, computer vision, for business process automation.

What Is Needed to Adopt AI Within a Bank Successfully?

Source: TheFinancialBrand

The infographic above showcases the general components of digital transformation. Here at DashDevs we are well-familiar with varying implementations of AI in commercial banking. Let’s apply the flow of digital transformation to the scenario of AI adoption in banking and review clear steps for its success:

#1 Assess current technological infrastructure and data readiness

This involves a thorough evaluation of the existing IT infrastructure and data capabilities. Banks and other financial institutions need to determine:

• If their current systems can support AI technologies
• Whether the available data is sufficient and of high quality for AI applications

#2 Develop a clear AI strategy aligned with business objectives

Formulating a clear AI strategy is crucial. This strategy should align with the bank’s overall business goals and objectives. It should define how AI can address specific challenges or opportunities in:

• Banking operations
• Customer service
• Risk management

#3 Acquire AI talent and expertise

Success in AI implementation heavily relies on having the right talent in the team. This includes hiring or training:

• Data scientists
• AI and ML software engineers
• Personnel that will be operating with new AI-based solutions

#4 Invest in scalable AI technologies and platforms.

Investment should be made in AI technologies and platforms that are scalable and can grow with the bank’s needs. This includes both hardware and software that are capable of handling the increasing demands of AI applications.

You can access information on how to execute IT modernization in fintech from another DashDevs post.

#5 Develop or adopt an AI solution

Based on the strategic objectives, develop AI solutions tailored to specific needs. This could involve:

• Creating custom AI models from scratch or based on similar solutions on the market
• Reinforcing existing technologies with AI-driven integrations 

#6 Test an AI solution in controlled environments

Before full-scale implementation, it’s critical to test AI solutions in controlled environments. This helps in:

• Identifying any potential tech issues
• Ensuring the solution meets the required performance standards
• Gathering feedback from users in test groups

#7 Expand AI implementation

In the main success scenario, after testing and fine-tuning, the AI solution can be gradually expanded across different departments and functions within the bank. This should be done systematically to ensure smooth integration with an existing business process. Possible actions here include:

• Phased roll-out across departments involved 
• Integration with existing workflow systems
• Staff training and support
• Familiarizing users with the new functionality

#8 Continuously monitor, evaluate, and refine AI solution

AI is not a set-and-forget solution. Continuous monitoring and evaluation are necessary to ensure the AI system performs as intended. Regular refinement and updates should be made based on:

• Performance data
• Evolving banking needs
• Technological advancements
• Feedback gathered
• Market trends

You can discover more information about how to integrate AI and ML into fintech business, what applications and challenges there are, and what value-adding benefits it can bring from another DashDevs guide.

Trends of Artificial Intelligence Application in Banking Industry in 2024

While the above use cases refer to more well-established examples of artificial intelligence in banking, there are growing trends that will potentially evolve in the advancements of the banking sphere. The list includes:

• Advanced predictive analytics. Utilization of complex algorithms and data analysis to forecast future financial trends and customer behaviors with high accuracy.
• Enhanced personalization and gamification. The integration of AI in banking focused on customizing user experiences to align with individual customer preferences, incorporating elements similar to those found in games to enhance user engagement.
• Robotic Process Automation (RPA). The use of software robots to streamline and automate mundane, repetitive tasks in banking, thereby boosting efficiency and minimizing the likelihood of human errors. Over 40% of business leaders report enhanced productivity through AI automation. Can you harness the same benefits they already have?
• Voice and conversational AI. Integration of voice recognition and natural language processing technologies to facilitate human-like interactions with banking systems.
• AI-driven financial advisory. Utilization of AI algorithms to provide personalized financial advice and investment strategies based on individual customer data.
• Ethical AI and bias mitigation. Development of modified AI systems that are fair, transparent, and free from biases, ensuring ethical use of AI in banking customer service decisions.

It’s crucial to understand that the point is not in the necessity to adopt all of the listed in the post, but in determining what can work for your business model the best in your current financial and market situation. 

Final Take

Now you know the score. Artificial intelligence in banking and finance develops rapidly, as owners of existing banks or future startup founders have to go with the basic flow to stay ahead. The way to actually implement innovative tech can be as simple as picking the right AI solution to the right business model, finding development resources, and bringing the digital product to real life. But execution of the plan is the tricky part. 

Since the right call, in most cases, is to seek support from a provider of fintech development services, consider DashDevs as your trusted partner in digital transformation. With over 12 years on the market, more than 500 projects successfully delivered, 30 of which are in the banking niche, and expertise in AI in fintech, we can help you turn an emerging idea into business value.

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