GenAI in Banking: 5 Transformative Use Cases

Within the last two years, generative artificial intelligence (GenAI) has gone from a hyped technology to a game-changing force in the banking industry. Today, financial institutions have moved beyond contemplating its potential to actively implementing and scaling use cases, eager to capture the immense value it promises.

The impact of GenAI in banking is far-reaching, with the potential to add between $200 billion and $340 billion in value on an annual basis. About 75% of generative AI’s potential value is concentrated in four key domains: customer operations, marketing and sales, software engineering, and R&D. With the capability to automate up to 70% of employees’ current tasks, it’s clear that the implementation of AI-powered platforms and tools brings not just an incremental improvement, but a paradigm shift in banking operations.

The transformative potential of GenAI is comprehensive, extending across the entire banking value chain, from back-office operations to customer-facing roles. It’s not just about streamlining processes and reducing costs, but also about enabling banks to offer more sophisticated, personalized services. GenAI is empowering financial institutions to make data-driven decisions faster, manage risks more effectively, and create innovative products that meet evolving customer needs.

As we delve deeper into this article, we’ll explore the multifaceted applications of generative AI in banking and the opportunities it presents for financial institutions in this rapidly evolving landscape.

Key takeaways:

• Generative AI is transforming banking across multiple domains, leading to improved efficiency and customer experience.
• AI-powered systems can automate activities that currently take 60-70% of work time, freeing employees to focus on more strategic tasks.
• GenAI enables highly personalized product recommendations by analyzing customer transaction history and behavior, allowing banks to offer relevant financial products and services.

Practical applications of GenAI in banking services

Artificial intelligence has long been a cornerstone of innovation in banking, enhancing data analysis, fraud detection, and customer engagement. However, the rise of generative AI unlocked a new era of possibilities. Powered by advanced machine learning (ML) models, this technology identifies relationships in big pools of human-created content and then uses the learned patterns to generate new media. 

GenAI in banking can perform a wide array of tasks, including:

• summarization 
• Q&A 
• classification 
• sentiment analysis 
• anomaly detection
• predictive modeling 
• and content personalization. 

Furthermore, it excels at natural language processing, enabling more sophisticated chatbots and virtual assistants. Its ability to analyze and interpret complex financial data sets is invaluable for risk assessment and market trend prediction in the banking sector.

Let’s take a closer look at some of the use cases of GenAI in the financial services industry.

Transaction categorization 

AI-powered algorithms can automatically categorize financial transactions, giving customers detailed insights into their spending habits. This feature enables better budgeting and financial management while helping banks understand customer behavior and tailor their services accordingly.

Transaction categorization solutions leverage machine learning, natural language processing (NLP), and deep learning techniques to automatically classify all customer payments into meaningful categories. They gather transaction data from various sources, including banks, payment processors, and financial apps. The AI then preprocesses this data, cleaning and normalizing it into a structured format that the models can understand and learn from.

Semantic search

GenAI helps maximize the potential of banking applications by introducing semantic search. This functionality goes beyond traditional keyword matching as it can comprehend the context and intent behind user queries. 

The search process begins with query parsing. At this stage, the customer’s inquiry is broken down into elementary entities, which are then mapped and labeled according to their roles. This helps the search engine truly “understand” the query by analyzing the context and leveraging attributes to interpret user requests accurately.

For instance, when a customer asks, “Show me my transactions concerning education,” the AI analyzes the semantic meaning, recognizing that the user is seeking education-related expenses. It then goes through all available banking data, including transaction metadata, account activity, balances, fund transfers, bill payments, and customer interaction history, to identify relevant entries. The AI systems categorize the results, grouping transactions such as tuition payments, textbook purchases, or student loan disbursements to provide a clear and organized overview of the customer’s educational expenses.

Client onboarding

The introduction of GenAI in the banking industry has transformed customer onboarding from a tedious, paper-driven process into a streamlined, efficient, and client-friendly experience. By automating document processing, providing intelligent assistance, and personalizing services, banks can now offer superior digital onboarding that sets the stage for lasting client relationships. 

AI-powered systems can quickly scale to handle increased onboarding volumes without compromising quality or speed, enabling banks to grow their customer base efficiently. In addition, GenAI tools minimize human errors that often occur during manual handling of complex financial information by automating data entry and document processing. This helps to improve accuracy throughout the process and ensures the integrity of client data. 

AI implementation not only enhances the customer experience but also improves operational efficiency, making it a game-changing technology in modern banking. The following are key examples of how artificial intelligence can be applied to transform various aspects of banking services:

• Automated document processing. GenAI excels at extracting and interpreting information from various document types. It can automatically populate onboarding forms by analyzing documents submitted by clients, significantly reducing manual data entry and associated errors.
• Regulatory compliance. Generative AI can ensure that customers meet all the requirements, including KYC (know your customer), anti-money laundering, ID verifications, and automatically flagging any missing information or potential compliance issues.
• Streamlined customer interactions. AI-powered chatbots can guide clients through the onboarding process. These virtual assistants can answer questions, provide explanations, and offer round-the-clock guidance, enhancing the client experience and reducing the likelihood of incomplete submissions.
• Predictive analytics. Generative AI can analyze historical data to predict potential issues or additional requirements for specific client profiles, allowing banks to address these concerns proactively.
• Real-time verification. The AI can perform real-time verification of client information against various databases, enhancing security and reducing fraud risks.

Automated reporting

Generative AI is changing the way banks process and present their financial data. It can streamline the complex task of generating comprehensive financial reports, enhancing accuracy, speed, and insight delivery.

The process begins with collecting information from various sources within the bank, including accounting systems, transaction databases, and risk management platforms. Generative AI then analyzes this vast amount of data, employing sophisticated algorithms to identify patterns, key indicators, and tendencies that might escape human analysis.

Using this processed data, the system creates detailed financial reports that include all the necessary elements, such as balance sheet data, financial results, profitability ratios, and risk analyses. The AI ensures that all regulatory requirements are met while presenting the information in a clear, coherent manner.

One of the most powerful aspects of this innovative technology is its ability to generate insightful conclusions and recommendations automatically. By analyzing the financial data holistically, the AI can identify future trends, potential risks, and opportunities, providing valuable input for financial decisions and business strategies.

This automated approach significantly reduces the time and resources traditionally required for financial reporting. It minimizes errors associated with manual data entry and calculations, freeing banking professionals to focus on more strategic tasks. Moreover, the AI can tailor reports to the specific needs of different stakeholders, highlighting the most relevant financial metrics for each group.

Personalized product recommendations

Generative AI enables banks to proactively identify and suggest relevant products for their customers by leveraging advanced data analysis, natural language processing, and machine learning algorithms. 

The process begins with the bank’s ability to monitor and analyze the customer’s transaction history. First, the system examines the context of the transaction, considering factors like the payment amount, location, and the nature of the purchase. By drawing insights from the customer’s broader transaction history, the AI can detect potential needs arising from this situation.

For example, when a customer buys airline tickets for an international trip, the AI system recognizes that the customer may benefit from travel-related insurance products. Drawing upon its extensive knowledge base, it generates personalized suggestions for voyager-type insurance policies that offer protection during the trip, including coverage for health emergencies, baggage loss, and other travel-related risks.

This level of contextual awareness and predictive capability is a hallmark of Generative AI’s application in banking product recommendations. The system provides more than just generic, one-size-fits-all suggestions. It leverages the customer’s unique financial behavior and life events to curate a tailored set of product options that are highly relevant and valuable to the individual.

Moreover, the AI’s recommendations go beyond just identifying potential needs. It also analyzes the customer’s existing product portfolio, risk profile, and financial goals to ensure the suggested products seamlessly integrate with the customer’s overall banking and financial plan.

Neontri: Harnessing the power of GenAI for banking innovation

Recognizing the immense potential of this cutting-edge technology, Neontri has strategically positioned itself at the forefront of GenAI innovation. Our team is pioneering solutions that redefine the customer experience and streamline critical banking operations. 

With a deep understanding of the unique challenges faced by financial institutions, Neontri seamlessly implements GenAI into solutions we develop for our clients, consistently pushing the boundaries of what’s possible in the realm of banking technology. 

Let’s take a look at some examples of AI-enabled projects we have done recently.

Snowdrop integration

Snowdrop is a powerful data enrichment engine that automatically pulls and aggregates a wealth of transaction-related information, enhancing the data available to bank clients. Whenever a customer makes a purchase, the system sources details such as the merchant’s official name, logo, website, physical address, and contact information. It also categorizes the business and pinpoints the transaction’s GPS location.

By integrating Snowdrop into its banking platforms, Neontri has empowered financial institutions to enhance their clients’ experiences. Customers now have access to a more comprehensive and intuitive understanding of their financial data, allowing them to navigate and make sense of their transactions effortlessly.

The true power of this integration lies in the adaptive nature of Neontri’s GenAI solutions. Drawing insights from user interactions, the system continuously learns and refines its capabilities, becoming increasingly adept at anticipating customer needs and providing more accurate, personalized services. This self-improving functionality transforms Snowdrop into an invaluable asset within modern digital banking platforms, constantly enhancing its ability to assist customers in managing their finances efficiently.

Chargeback abandonment

Chargeback abandonment is an innovative application of GenAI in the banking sector designed to reduce the frequency and impact of chargebacks. This advanced AI-driven system employs machine learning, predictive analytics, and natural language processing to identify potential chargebacks early and take measures to resolve disputes before they escalate.

The solution developed by Neontri uses data enrichment to reduce the number of erroneous chargeback reports. This technology powered by GenAI adds layers of contextual information to each card transaction, including store names, product categories, prices, and transaction dates.

By providing customers with this detailed transaction data, chargeback abandonment enables them to better recall their purchases, reducing the likelihood of erroneous chargeback reports. This improves customer satisfaction and streamlines internal workflows, as banks can more accurately categorize chargebacks and comply with agreements with payment providers.

It also helps financial institutions identify and address friendly fraud, where customers mistakenly file chargebacks due to a lack of transaction context. This targeted approach has significantly reduced the overall number of chargeback cases, resulting in cost savings and improved operational efficiency.

Final thoughts

The future of banking is undoubtedly intertwined with the advancement of GenAI. By automating routine tasks and facilitating data-driven decisions, this technology allows banks to become increasingly focused on improving customer satisfaction and developing innovative financial services.

GenAI is revolutionizing various aspects of banking, from client onboarding and transaction categorization to advanced search capabilities and personalized product recommendations. By leveraging AI capabilities, banks can offer more accurate, timely, and tailored services, meeting the evolving demands of today’s tech-savvy consumers. Moreover, the implementation of GenAI in regulatory compliance and financial reporting demonstrates its potential to improve efficiency and accuracy in critical back-office functions, further solidifying its importance in the banking industry.

Contact Neontri today to learn more about implementing these cutting-edge solutions in your organization and start your journey toward a more efficient, innovative, and customer-centric banking future.