Generative AI in Banking - Use Cases, Benefits, and Future

In the early 2023, Generative AI was a fairly new technology in the banking industry, and was treated with caution, with many financial institutions prohibiting the use of of it in their branches. However, according to the Gartner in 2024, 75% of banking leaders reported they have already deployed or were deploying GenAI, making it an integral part of many banks key business areas. In this article, we will explore the key GenAI use cases, benefits, challenges, and predictions for the future.

What is Generative AI in Banking?

Generative AI in banking refers to the Large Language Models (LLM) capable of analyzing vast amounts of financial data and learning patterns to create new content, automating banking processes, and delivering highly personalized experiences for clients. Generative AI can produce outputs such as personalized financial advise, automated anti-money laundering (AML) workflows, or even new banking applications, making it a versatile tool for modern banks.

For example, Generative AI can:

• automatically generate reports, reducing manual effort and minimizing errors,
• create personalized marketing content tailored to individual customer preferences,
• assist in creating new banking products and develop workflows based on natural language descriptions,
• help banking professionals offer tailored financial advice by analyzing customer needs and market trends,
• deliver personalized customer service 24/7.

By integrating Generative AI, banks can streamline their operations, improve efficiency, and increase customer satisfaction.

GenAI in Banking Use Cases

According to Gartner’s report, the most popular use cases for GenAI in banking include fraud detection, virtual banking customer assistant, and regulatory compliance. However, there’s much more that Generative AI can offer.

Here is a list of key AI in banking use cases:

Frontline AI Copilot

Frontline AI Copilots are transforming the way bank employees interact with customers, delivering faster, more personalized, and highly relevant experiences. These generative AI-powered tools act as real-time assistants for banking staff, offering insights and real-time recommendations regarding next best steps and next best actions.

Additionally, the AI Copilot automates routine tasks such as updating data, filling out forms, and preparing follow-up messages. For example, after a banking professional assists a customer with opening a savings account, they can use AI to automatically generate an email with a summary of the most important information and set a reminder for the next steps, such as document submissions.

Synthetic credit data generation

Generative AI can produce realistic, secure, and anonymized synthetic credit data with the same properties as original data to help banks simulate customer behaviors, test credit risk models, etc., without risking sensitive customer information. This approach helps banks with modeling and testing while adhering to strict data privacy regulations and security requirements. By using synthetic credit data, financial institutions can ensure their algorithms perform effectively in various scenarios and are able to accurately predict money laundering and fraud risks.

Code generation and conversion

Generative AI simplifies backend operations by automating code generation and converting legacy code, helping banks modernize their IT infrastructure and accelerate time-to-market for new products and services.

Gen AI can automatically generate code based on project’s specifications described in natural language, reducing the costs and time needed for development and delivery of new features or updates. It can also translate legacy code into modern programming languages, helping banks modernize their legacy systems. This ensures smooth transitions, reduces downtime during system upgrades, and enables seamless integration with new technologies and modern banking solutions.

Personalized marketing content

With generative AI, banks can craft highly targeted marketing campaigns tailored to individual preferences. By analyzing customer data such as demographics, preferences, and needs, AI generates personalized email content, advertisements, and product offers, increasing customer engagement and revenue.

Banking product recommendation

By using insights provided by generative AI, banking professionals can improve product recommendations, suggesting more tailored and relevant offers. Whether it's a new savings plan, loan, or investment opportunity, AI delivers insights to match products with customer needs, enhancing client satisfaction and increasing conversion rates.

Banking fraud prevention

One of the most widely used gen AI use cases is banking fraud prevention. Banks can use generative AI to track transactions and flag anomalies and fraudulent behavior that don’t fit the desired pattern. By leveraging historical patterns and real-time data, generative AI can even detect layered or complex methods of money laundering and fraud. This way, instead of combing through transaction data manually, banking professionals can receive notifications about suspicious activity, review them, and act on them. Additionally, generative AI can update its algorithms in real-time and accurately detect and respond to the latest fraud schemes.

New product development

Gen AI can analyze vast amounts of data including market trends, customer feedback, and prepare a competitive analysis to identify market gaps and customer needs. These insights can be used to generate ideas for new financial products and services that will accurately respond to the current market and customer requirements. Gen AI can also help banking professionals design, prototype and test new ideas, helping them develop and deploy new products faster and more efficiently. This way, gen AI helps banks drive growth and achieve competitive advantage in the ever-changing financial industry.

AI financial coach

An AI financial coach offers personalized financial advice to customers, helping them manage their budgets, investments, and savings more effectively. By analyzing an individual’s transaction history, risk profiles, spending habits, income levels, and financial goals, as well as market trends, and historical data, GenAI can prepare tailored recommendations, such as saving for retirement, optimizing spending, etc.

AML compliance and reporting

Generative AI automates the preparations of Anti-money laundering (AML) reports, known as Suspicious Activity Reports (SARs) by extracting all relevant data from transaction records, customer profiles, etc., and filling out required forms. This way, it helps banks stay compliant with industry regulations while reducing manual effort and increasing reporting accuracy. GenAI also monitors regulatory changes, so banking professionals can rest assured they stay aligned with the current industry regulations.

Debt collection and recovery assistant

Generative AI can aid banking professionals in debt collection by automating the communication with customers who are behind on payments. AI assistants can contact borrowers via email, SMS, or in-app messages to remind them of overdue payments and offer repayment plans tailored to individual customers’ cases. By handling routine communication, the AI allows human agents to focus on more complex cases. It also supports them in developing effective debt collection strategies by offering insights into customer situation, behavior, and preferences. All in all, generative AI improves recovery rates and helps build stronger customer relationships with timely and personalized communication.

Bank workflow Copilot

Generative AI workflow Copilots are intelligent assistants that help design, create, and optimize internal banking operations. They analyze data and idenitfy bottlenecks, redundancies and opportunities for improvement, providing banking professionals with suggestions on how to streamline workflows and task execution. Additionally, AI assistants can automatically update existing workflows to include new steps, rules, or regulatory requirements across multiple processes without disrupting the processes.

Generative AI can also create entirely new workflows, using just a simple description of desired outcomes. This means that instead of manually designing workflows or relying on complex programming, banking professionals can describe what they need in natural language, and GenAI will design a workflow of necessary tasks, including required steps, triggers, data validation points, regulatory checks, etc.

Document processing

Generative AI streamlines document processing within banks by automating tasks such as data extraction, document classification, and compliance checks. AI can scan and analyze documents such as loan applications, contracts, or regulatory filings from various sources, including emails and scanned documents, and extract relevant information. It can then categorize and route documents for further review, reducing the time human staff spends processing paperwork.

Banking contact center assistant

GenAI assistants powered by Natural Language Processing (NLP) provide human-like customer support in real-time 24/7. These virtual assistants can answer a wide range of customer inquiries, from checking account balances and transaction histories to offering product recommendations or resolving technical issues. They can respond to queries via chatbots, voice assistants, or email, ensuring that customers receive timely support. Generative AI assistants can also forward more complex cases to human customer service agents and help them resolve the issues faster with insights gathered from their interactions with the customers.

Content creation

Generative AI can assist banks in creating a variety of content, such as pitchbooks, customer-facing documents, financial reports, how-to guides, social media posts, etc. AI-driven tools can automatically generate compelling articles, blog posts, or product descriptions by analyzing customer preferences, market trends, and financial data. This helps banks scale their content marketing efforts while ensuring consistency and relevance across all channels.

Summarization and synthesis of information

Banking professionals have to deal with a vast amount of documents on a daily basis, and GenAI can significantly improve their productivity by synthesizing and summarizing important information. Generative AI can automatically analyze and summarize lengthy reports, regulatory filings, financial statements, customer communication histories, meeting transcripts, or news articles, highlighting the most important insights.

For example, generative AI can summarize quarterly financial reports for bank executives, providing an overview of key performance indicators, trends, and risks. This helps decision-makers quickly understand complex information and make data-driven decisions, without having to spend a lot of time reading through large volumes of raw data.

Benefits of Generative AI in Banking Industry

Generative AI offers multiple benefits for banks, including improved operational efficiency, higher customer satisfaction, and increased revenue.

Here are the key GenAI benefits in banking:

Improved operational efficiency

Generative AI reduces the manual workload of banking professionals and helps them increase their productivity. It automates routine tasks such as document and loan processing, data entry, report generation, etc, freeing employees time to deal with high-value activities like resolving complex customer issues, and building relationships with clients.

Improved customer experience

Generative AI helps banks improve their customers’ experience and satisfaction with 24/7 customer support, personalized communication, and tailored product recommendations. Thanks to the insights generated by genAI, banking professionals can better understand their clients and respond to their needs faster and more accurately.

Improved decision-making

Generative AI empowers bank executives to make more informed decisions by providing valuable insights, and anticipating trends and market shifts. Armed with this knowledge, decision-makers can better assess opportunities, mitigate risks, and allocate resources more effectively. This way, GenAI helps banks stay ahead of the competition, adapt to changing market conditions, and develop strategies that align with both short-term and long-term goals.

Increased revenue

Generative AI helps banks boost revenue by optimizing cross-selling and upselling efforts, personalizing product recommendations, and identifying new market opportunities. By delivering the right products to the right customers at the right time, and anticipating future clients’ needs, banks can significantly enhance their profitability and increase revenue growth.

According to the McKinsey Global Institute (MGI), gen AI could add between $200 billion and $340 billion in value annually in the banking industry, largely by increasing employee productivity.

Challenges in Generative AI for Banking

Generative AI may seem like a magic tool that will solve all the problems of the banking industry. However, it’s not without its faults, and it’s important that banking professionals are aware of potential issues and risk of leveraging generative AI.

Bias and unethical behavior

Generative AI uses historical and real-time data to generate new content and its effectiveness largely depends on the data quality. If the algorithm is trained and uses biased data, it can reflect and even magnify the biases. This can lead to unfair and discriminatory decisions. For example, GenAI models can be biased against certain customer types when assessing their creditworthiness and decline their applications based on factors that do not reflect their ability to repay.

Solution

To mitigate the risk of biased or unfair decisions, it’s crucial for banks to establish clear ethics and anti-bias policies and guidelines for the use of GenAI and regularly monitor and audit its performance. Additionally, employees should be aware that AI is just a tool to help them increase their productivity and that the final decision should always belong to the human professionals who can spot bias and correct it. Banks should also ensure that their AI models are trained on a diverse, unbiased and high-quality data that accurately reflect the demographics of their clients.

Hallucinations

Generative AI models can sometimes generate responses that appear to be correct and meaningful but are actually inaccurate, misleading, and plain wrong. These so-called “hallucinations” result from insufficient data or a lack of context and can occur because GenAI is trying to fill in the gaps. For example, it can provide incorrect client information for onboarding if some crucial data is missing.

Solution

To avoid hallucinations, it’s important to design precise prompts and instructions for the generative AI model to guide its responses and improve the quality of outputs. It’s also beneficial to incorporate human reviews into the process to provide oversight and constructive feedback. For data analysts who train models, it’s crucial to fine-tune the model after training it on large datasets and incorporate guidelines and quality controls to reduce the likelihood of hallucinations.

Regulatory violations

Generative AI can sometimes violate data privacy regulations such as GDPR and CCPA, so bankers need to be cautious not to include Personally Identifiable Information (PII) in the dataset available for the AI. Privacy violations can cost businesses even up to 4% of their annual revenue in the EU and with the new EU’s AI Act, the non-compliance fines are expected to reach even 6% of a firm's annual revenue or 30 million euros.

Solution

To mitigate the risk of regulatory violations, banks should train employees on privacy and AI regulations to ensure they understand the potential issues. They should also make sure that materials used to train GenAI don’t include PII or train AI models to recognize it and not incorporate it into its dataset.

Copyright violations

Generative AI has access to large datasets and can sometimes include materials protected by copyrights in its output. This can lead to potential lawsuits and loss of a bank's reputation.

Solution

Banks should introduce filters and governance to ensure the model is trained only on materials that are properly licensed. They can also implement internal and external audits to enhance compliance with copyright and intellectual property regulations.

Integrating AI into existing technology stack

Many banks still depend on legacy systems that might not be compatible with artificial intelligence models, making the integration of AI into the existing technology stack challenging. This can result in costly and time-consuming integration processes that will delay or even reduce the benefits of GenAI.

Solution

Financial institutions should consider migration to modern core banking and Financial Services CRM systems that offer native AI capabilities and can seamlessly integrate with third-party applications.

Future of Generative AI in Banking

According to Gartner, the banking industry is expected to invest over $28 billion in AI software by 2027, highlighting the growing reliance on AI-driven solutions to optimize operations, enhance customer experiences, and drive revenue. On average, CIOs and tech executives in the banking industry report their organization plans to increase the funding for GenAI by around 39% compared to the 2024 budgets. This surge in spending places banking among the leading industries adopting AI, which underscores the importance of the artificial intelligence technology in this industry.

Forrester's 2025 Predictions for the banking industry suggest that 2025 will mark a breakthrough year for conversational banking. Leading banks will leverage advanced AI capabilities to create smarter and more intuitive in-app conversational assistants to help customers navigate banking apps, access personalized financial guidance, and perform essential tasks such as disputing transactions, paying bills, and checking account balances.

To succeed, banks must focus on several critical factors including creating a well-designed AI assistant, implementing strong AI governance, and investing in rearchitected conversational AI systems to address implementation challenges.

According to Gartner, by 2026, 90% of all finance institutions will integrate at least one AI-empowered technology solution into their technology stack.

As banks continue to invest in and adopt Generative AI technologies, the focus will increasingly shift toward creating comprehensive, customer-centric solutions. As a result, the banking experience will become more personalized, accessible, and responsive, leading to greater customer engagement and satisfaction.

Creatio AI Solution for Banking

At the core of Creatio Financial Services CRM solution lies the unified AI architecture, designed to empower financial institutions with intelligent automation combining generative, predictive, and agentic AI. By automating routine tasks, providing actionable insights, and enabling personalized customer experiences, Creatio AI empowers banks to optimize workflows, reduce costs, and respond more effectively to evolving market demands.

Creatio AI provides powerful, AI Skills that enhance efficiency across numerous Financial Services workflows, such as customer onboarding, payment statement information requests, next best offer recommendations, account planning and reviews, audit management, and many more. Apart from these FinServ-specific capabilitites, Creatio offers also general-purpose features such as streamlining routine tasks including content preparation and localization, creating communication templates, and summarizing activities. Additionally, banking professionals can create and deploy new AI skills by describing desired outcomes in natural language. Example of Creatio AI skill - meeting summary

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The Real Life Example of Implementing Creatio AI

The Bank of Georgia recognized the importance of providing real-time assistance and implemented chatbots to support its internal teams and empower users to initiate conversations directly from their self-service portal. These chatbots were designed to handle common inquiries and guide users through various processes, eliminating the need for human intervention and manually preparing tailored content.

To further enhance the user experience, the bank upgraded its chatbots with advanced AI capabilities, enabling them to understand and respond to queries with greater accuracy and efficiency. By combining the power of no-code development with AI-driven technology, the chatbots now offer seamless access to the bank's extensive knowledge base, delivering comprehensive and instant responses. This innovation helped the Bank of Georgia improve user satisfaction and streamline internal operations.