Generative AI in Banking: Practical Use Cases and Future Potential

Dmytro Ivanov
Daria Iaskova

As artificial intelligence (AI) penetrates operations, streamlines decision-making, and reinvents every facet of customer interactions across multiple industries, it’s also having a transformative impact on banking and finance.

The numbers speak for themselves. McKinsey estimates that across the global banking sector, AI and generative AI in particular could add up to $340 billion or 4.7% of total industry revenues annually.

Generative AI use cases in banking are diverse and impactful, including enhanced customer service, fraud detection, regulatory compliance, and predictive analytics. At the same time, AI solutions often come with privacy risks that companies should take seriously from the outset. 

So, how far can AI in banking and finance take businesses, and how to implement the technology in practice considering existing limitations, specific business constraints, and the changing market landscape? Let’s explore below.

What is AI in banking?

Before diving into practical use cases, let’s first define AI in banking and financial services. AI in this sector usually refers to the use of advanced algorithms and machine learning techniques to automate processes, boost decision-making accuracy, enrich customer engagement, and refine operational workflows within financial institutions.


Representing a blend of cutting-edge technology algorithms and tools, artificial intelligence in banks often encompasses:

  • generative AI
  • conversational AI
  • machine learning 
  • predictive analytics 
  • natural language processing (NLP)
  • robotic process automation (RPA) 
  • computer vision

Productizing these technologies and making them part of digital banking solutions unlocks numerous benefits, driving innovation and enhancing efficiency within modern BFSI organizations. Today, this is something businesses mostly take for granted. But how did we get here?

The evolution of AI in banking

For the past ten years, machine learning and AI in banking have undergone a myriad of changes. 

Initially, machine learning and natural language processing were employed to automate routine tasks and enhance back-office operations—this phase saw the development of AI-powered chatbots for customer service, automated document processing for loan approvals, and algorithmic trading systems for financial markets.

As AI matured, financial institutions started leveraging more sophisticated AI applications to improve decision-making processes. Advanced predictive analytics and data-driven insights enabled banks to assess credit risk, detect fraudulent activities, and optimize investment strategies. 

The adoption of AI in banking accelerated further with the integration of big data analytics and cloud computing technologies. Banks started harnessing vast amounts of data from internal and external sources to gain deeper insights into customer behavior, market trends, and regulatory compliance. AI-driven recommendation engines personalized product offerings, while automated wealth management platforms provided tailored financial advice to clients.

Moreover, the rise of regulatory technology (RegTech) solutions powered by AI helped banks navigate increasingly complex regulatory landscapes more efficiently. AI algorithms deployed to monitor transactions for compliance violations, ensure data privacy, and enhance cybersecurity measures bolstered customer trust and loyalty as digital banking was gaining traction.

Looking ahead, AI continues to drive innovation in banking, positioning businesses at the forefront of digital transformation and customer-centric financial services.

Benefits of AI in financial services

While McKinsey predicts that the largest value AI can bring to finance and banking lies in improved productivity, the extensive practice of global financial corporations proves that the benefits technology can bring to the sector are far more diverse and impactful.

Simplified banking operations

AI revolutionizes banking operations by automating repetitive tasks such as transaction processing, customer inquiries, and document verification. This automation reduces manual effort, accelerates processes, improves service availability, and enhances operational efficiency. 

Reduced costs

AI-driven automation optimizes resource allocation and reduces dependency on human intervention in routine tasks, leading to significant cost savings for financial institutions. By automating back-office processes like data entry and compliance checks, AI minimizes operational expenses and frees up human resources to focus on more strategic initiatives. 

Faster and more accurate decision-making

By analyzing large volumes of data at high speeds, AI algorithms provide actionable insights that enable faster and more informed decision-making. For instance, AI-powered risk assessment models can swiftly evaluate creditworthiness and detect fraudulent activities, reducing decision-making time and enhancing accuracy. 

Enhanced risk management

AI-powered risk models continuously monitor transaction patterns, market trends, and regulatory changes to detect anomalies and mitigate risks in real-time. This proactive approach improves compliance with regulatory requirements and enhances overall risk mitigation strategies, safeguarding the financial stability of institutions and increasing trust among stakeholders.

Improved financial well-being

AI-driven personalized financial services cater to individual customer needs by offering tailored recommendations and solutions. By analyzing customer data and behavior patterns, AI algorithms provide insights into spending habits, savings goals, and investment opportunities. This personalized approach helps customers make informed financial decisions, achieve their financial goals, and improve their overall financial well-being.

Generative AI: a disruptor to financial services 

While traditional machine learning and artificial intelligence have demonstrated efficiency across various aspects of financial management and banking, generative AI stands out as a true game changer for the industry.

Its capability to generate unique and meaningful outputs from human language inputs has made this technology particularly invaluable for streamlined customer service, financial report generation, personalized investment advice, and more.

According to Statista, the banking sector's investment in generative AI is expected to reach $85 billion by 2030, growing at an impressive annual rate of over 55%.


There is a common misconception that generative AI applications in banking boil down to implementing conversational chatbots into customer service. While AI chatbots are indeed a common use case in the sector, there is much more behind the technology, and a number of large market players are already taking advantage of this promising potential.

Intelligent virtual assistants

Generative AI powers advanced a new era of chatbots that handle customer inquiries with accurate human-like responses. These virtual assistants can understand and generate natural language, offer personalized support, resolve issues, and provide 24/7 support, significantly improving customer satisfaction and operational efficiency. They also reduce the workload on human agents, allowing them to focus on more complex tasks, and can be integrated across various platforms such as mobile apps, websites, and messaging services, ensuring a seamless customer experience.

For example, an online bank might deploy a virtual assistant that uses generative AI to help customers with tasks such as checking account balances, transferring money, and providing personalized financial advice. 

Financial report generation and tax automation

Generative AI-powered tools automate the creation of comprehensive financial reports by analyzing vast amounts of data and generating detailed narratives. For instance, a bank might use AI to interpret commercial loan agreements and generate financial summaries. This application saves time, reduces human error, and ensures that stakeholders receive accurate and timely financial insights, allowing financial analysts to focus on more strategic tasks.

Also utilizing generative AI helps to automate tax administration including tax form processing, return filing and submission, enabling secure and compliant business continuity in wealth management, insurance underwriting, asset management, and retail banking.

Automated wealth management made possible with intelligent tax bots
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Personalized investment advice

Making part of an integrated solution, generative AI helps to analyze individual customer profiles, market trends, and historical data to offer tailored investment advice. Dedicated algorithms can simulate various financial scenarios and generate personalized recommendations, helping clients make informed investment decisions and enhancing portfolio management. 

For example, a wealth management firm could implement AI to provide tailored investment strategies and portfolio management for their clients. This personalized approach not only improves client satisfaction but also builds trust and loyalty, as customers feel their unique needs and goals are being addressed.

AI in banking fraud detection and prevention

AI-enabled banking solutions detect unusual patterns and potentially fraudulent activities by analyzing transaction data in real-time. This application reduces the incidence of false positives, improves the accuracy of fraud detection, and enhances overall security, protecting both the institution and its customers from financial losses.

A credit card company, for instance, might use AI to monitor and analyze millions of transactions daily, identifying and flagging suspicious transaction patterns and unauthorized charges. By generating alerts and providing actionable insights, such AI-driven systems help prevent fraud and mitigate risks effectively.

Risk management and compliance

AI reinforces risk management by generating predictive models capable of identifying potential risks and compliance issues. With its ability to stimulate various risk scenarios, generative AI can be used to develop mitigation strategies and ensure adherence to regulatory requirements. This allows businesses to reduce the burden on compliance officers, improve accuracy, and ensure timely reporting, thus avoiding costly fines and reputational damage.

For example, a commercial bank might use AI to monitor transactions for signs of money laundering and other financial crimes. In this case, the technology allows to analyze transaction patterns and generate alerts for suspicious activities, helping the bank comply with regulatory requirements and improve overall risk management strategies.

Automated document processing

Generative AI streamlines document processing by extracting relevant information from unstructured data sources, such as emails and scanned documents. This way, financial businesses can accelerate KYC (Know Your Customer) procedures and other documentation tasks, ensure data accuracy and consistency, enhance regulatory compliance, and improve the customer onboarding experience.

For instance, a mortgage brokerage firm could use AI to automatically extract and verify information from loan applications, reducing manual processing time, minimizing errors, and accelerating workflows related to loan approvals and compliance checks.

Learn how you can streamline enterprise document management using OpenAI’s model

Loan and credit scoring

AI helps to refine loan and credit scoring processes by generating detailed risk profiles for potential borrowers. Used in combination with data analysis tools and dedicated machine learning, it helps lenders make more accurate credit decisions and offer personalized loan terms. 

For example, a credit union might use AI to analyze a wide range of data points, helping lenders make their credit decisions and benefit from the best loan terms. This leads to better risk management, reduced default rates, and increased access to credit for customers who may have been overlooked by traditional scoring methods.

Algorithmic trading

Machine learning and AI enhance algorithmic trading strategies by optimizing trading algorithms based on market data. By continuously learning from market trends and performance metrics, AI can adapt strategies in real-time, maximizing profitability and minimizing losses.

For instance, a hedge fund might use AI to develop sophisticated trading algorithms that adapt in real-time to market conditions. This allows for more sophisticated trading decisions, better risk management, and improved returns on investment.

Enhanced financial forecasting

Generative AI models can handle data extraction tasks that are essential for building financial forecasting solutions. Using these solutions leads to more resilient planning and allows financial businesses to identify emerging opportunities or threats in the market, providing a competitive edge.

A financial services firm, for example, might use AI to enhance its economic forecasting models. This would help them make better strategic decisions, optimize resource allocation, and anticipate market movements, leading to more resilient financial planning and identifying emerging opportunities or threats.

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Marketing and customer engagement

Last but not least, generative AI algorithms can analyze customer data and preferences to create personalized marketing content and campaigns. For example, a digital bank could generate targeted marketing messages, offers, and recommendations based on customer behavior and predict future needs to deliver more relevant and timely communications, increasing conversion rates and customer loyalty.

Real-life generative AI use cases in banking

As the applications of generative AI in banking industry are gaining traction, more widely known global brands are integrating the technology into the core of their digital solutions.



In February 2024, Mastercard launched a cutting-edge generative AI model designed to enhance banks' ability to identify suspicious transactions across its network. The technology called Decision Intelligence Pro is projected to bolster fraud detection rates by up to 20%, with some institutions experiencing increases as high as 300%. 

Drawing insights from approximately 125 billion transactions processed annually through its card network, Mastercard leverages this vast dataset to train and refine the AI model.

J.P. Morgan Chase

In March 2024, J.P. Morgan Chase & Co. announced the launch of IndexGPT, an AI-powered tool designed to provide investment advice to retail clients in Latin America. This cloud-based service uses advanced AI to analyze and select financial assets tailored to each client's needs, democratizing access to sophisticated investment tools. 

With IndexGPT, J.P. Morgan aims to revolutionize financial decision-making and enhance outcomes for individual investors in the region. 

Morgan Stanley

Morgan Stanley also introduced an AI assistant powered by OpenAI’s GPT-4, enabling its 16,000 financial advisors to access a repository of approximately 100,000 research reports and documents instantly. The AI model is designed to assist advisors in efficiently locating and synthesizing information for investment and financial inquiries, providing tailored and immediate insights.

ING Bank

As a major player in the Dutch banking sector, ING used to handle 85,000 customer interactions weekly, but their existing chatbot could only resolve 40-45% of these, leaving 16,500 customers requiring live assistance.

To improve customer experience and enhance their support capacity, the bank collaborated with McKinsey to develop a generative AI chatbot capable of providing immediate and tailored assistance. After a soft launch in September 2023, this new AI-powered assistant quickly demonstrated its superiority over the traditional chatbot by assisting 20% more customers, reducing wait times, and improving overall customer satisfaction. 

OCBC bank 

OCBC Bank in Singapore has recently reported that a six-month generative AI chatbot trial brought them a 50% efficiency lift, streamlining writing, translation, and research activities. In the past, when the company utilized technology to assist employees in developing code, summarizing documents, transcribing calls, and building an internal knowledge base, they achieved a similar productivity boost.

Currently, OCBC Bank is expecting this in-house AI-based solution to help their 30,000 employees make risk management, customer service, and sales decisions. 


Recently, Citigroup leveraged generative AI to assess the impact of new US capital regulations. The bank's risk and compliance team utilized the technology to efficiently analyze and summarize 1,089 pages of newly released capital rules from federal regulators. 

Additionally, Citigroup plans to employ large language models (LLMs) to interpret legislation and regulations in various countries where they operate, ensuring compliance with local regulations in each jurisdiction.

What does the future hold for generative AI in banking industry?

Beyond any doubt, the use of generative AI in banking is poised to bring both expected and surprising changes, leading to an evolution and expansion of AI's role in the sector. However, significant changes from generative AI in banking will require some time. 

Detect hidden business opportunities to come prepared for an AI-first future

Banks are expected to continue investing in generative AI models and testing them over the next 2-5 years. In the short term, banks will likely focus on incremental innovations—small efficiency gains and improvements based on specific business needs. Employees will maintain an oversight role to ensure accuracy, precision, and compliance as the technology matures.

In line with approaching generative AI for innovation, banks are expected to utilize the technology to improve efficiency in existing and older AI applications. Just like that, automating customer-facing processes creates digital data records that generative AI can use to refine services and internal workflows. These records can enhance risk management, automate data collection, and streamline reporting, leading to further digitalization, end-to-end customization, better client segmentation, and retention.

All in all, the development of generative AI capabilities in banks will depend on their scale and investment capacity, ranging from in-house solution development to fine-tuning existing models. But regardless of these constraints, here are some key areas where generative AI could make a significant impact over the next years:

  • Personalized financial products

Utilizing generative AI allows financial companies to create tailored financial products based on individual customer profiles and behaviors, leading to higher customer engagement and satisfaction. Banks can integrate the technology into their digital solutions to analyze customer data and market trends and develop innovative and highly personalized financial products.

  • Enhanced financial forecasting

Making part of dedicated digital assets, generative AI algorithms can improve financial forecasting by analyzing historical data and current market conditions, providing more accurate and timely predictions. Financial institutions can leverage such tools for strategic planning processes and continuously train AI models with the latest data to ensure relevance and accuracy in predictions.

  • Employee augmentation

AI can assist employees by providing instant access to information, automating routine tasks, and generating insights, allowing them to focus on more strategic activities. In the future, banks should adopt a hybrid approach where AI tools augment human capabilities and implement training programs to help employees effectively use AI tools and understand their outputs.

Although the significant transformation that generative AI is likely to bring to banking and financial services will take some time, coming prepared for the changes remains a number one priority for businesses aiming to maintain their competitive advantage in the market. Below, we’ll explain what it takes to approach generative AI in practice.

How to approach AI and generative AI development in banking?

In many aspects, adopting generative AI in banking is akin to any other software development project: it involves gathering requirements, conducting research, selecting optimal solutions, deploying, testing, and iterating for improvement. 

However, the banking sector presents unique challenges due to numerous risks and limitations, especially concerning privacy concerns inherent to generative AI technology. Therefore, before diving into implementation details, it is crucial to understand these risks and limitations in full. This way, organizations can ensure that the deployment of generative AI not only enhances efficiency and innovation but also prioritizes security and regulatory compliance.

Risks and challenges that come with AI adoption

So, below we highlight several significant risks and challenges that financial institutions must carefully navigate to achieve success with AI in banking and finance.

  • Data privacy and security. Protecting sensitive customer information and preventing data breaches is critical. AI systems rely on extensive data, and ensuring robust cybersecurity measures is essential to maintain customer trust and comply with regulations.
  • Ethical considerations. AI algorithms can unintentionally propagate biases present in training data, leading to unfair outcomes like discriminatory practices in lending or decision-making. Banks must implement ethical guidelines and audits to mitigate bias risks.
  • Regulatory compliance. Banking operates within stringent regulatory frameworks. Ensuring AI adoption aligns with data protection laws, consumer rights regulations, and financial transaction laws is crucial to avoid legal repercussions.
  • Reliability and accuracy. To aid financial companies and their customers in making informed decisions, AI models must produce reliable and accurate results. Maintaining this accuracy requires ensuring transparency in AI operations and continuously monitoring model performance.
  • Operational integration and complexity. Integrating AI into existing banking systems can be complex and resource-intensive. Banks need to ensure seamless integration with legacy systems, minimal disruption to operations, and effective change management strategies.
  • Scalability and maintenance. Scaling AI solutions across an organization requires ongoing maintenance, updates, and support. That’s why banks must allocate resources for continuous monitoring, improvement, and adaptation of AI models to evolving business needs.
  • Public perception and trust. Building and maintaining public trust in AI-driven banking services is crucial. Banks must be transparent about how AI is used, protect customer data, and communicate clearly about the benefits and risks of AI adoption to maintain customer confidence.
Learn how AI-enhanced risk management helps to build stronger resilience and compliance

Steps to get started with generative AI in banking and finance industry

Considering the challenges and limitations described above, the integration of generative AI solutions into financial operations requires thorough strategic planning. Moreover, with each business case being unique and sophisticated, the decisions related to AI enablement as well as the results expected from technology adoption always make a difference.

At the same time, the general flow for developing and successfully deploying a generative AI solution in production often consists of 5 foundational steps described below.


Step 1: Develop an AI strategy

Start by formulating a comprehensive AI strategy aligned with the bank's goals and regulatory requirements. Define clear objectives for integrating generative AI, identifying key stakeholders, and establishing governance frameworks.

Step 2: Assess the current data state

Evaluate the quality, security, and reliability of existing data repositories. Ensure adequate storage capacity and data accuracy necessary for developing and training AI solutions. Address any gaps in data infrastructure to support the implementation of generative AI technologies effectively.

Step 3: Prototyping and testing

Choose an appropriate generative AI model and adapt it according to the defined objectives. Develop prototypes to validate AI algorithms and assess their feasibility in real-world banking applications. Conduct thorough testing and validation to refine the AI model based on performance metrics and user feedback.

Step 4: Deployment and scaling 

Deploy validated AI solutions into operational environments, starting with pilot implementations to mitigate risks and optimize performance. Scale AI initiatives gradually across different banking functions, ensuring seamless integration with existing workflows and systems.

Step 5: Monitoring and optimization

Establish continuous monitoring mechanisms to track AI performance, data quality, and regulatory compliance post-deployment. Implement iterative improvements based on insights gained from operational feedback and evolving business needs.

Note that making generative AI initiatives drive measurable outcomes requires financial companies not only to consider all possible risks and limitations but also develop a strategic roadmap focused on achieving value in the areas that hold the most promising potential.

At Trinetix, we provide comprehensive technology guidance and end-to-end AI implementation support, so that financial companies can focus on their business priorities and scale market impact. If you are inspired by successful generative AI use cases in banking, let’s chatand schedule a discovery session where we could discuss potential applications and limitations for your specific scenario.


Artificial intelligence in banks is mostly used for enhancing customer service through chatbots, automating fraud detection, streamlining credit scoring, and improving personalized financial recommendations, thereby increasing efficiency and customer satisfaction.
Financial companies can benefit from AI in banking and payments by streamlining transaction processes, enhancing customer service through chatbots and virtual assistants, improving fraud prevention, and more. AI can also optimize credit scoring, reduce operational costs through automation, and provide deeper insights into customer behavior and market trends.
Generative AI creates new content, such as financial reports or predictive models, while conversational AI enables human-like interactions, such as chatbots and virtual assistants for customer service. In banking, generative AI is used for content creation and analysis, whereas conversational AI enhances customer engagement and support.
Generative AI in investment banking can be used to create predictive models for market trends, generate investment strategies, and automate the drafting of financial reports, enhancing decision-making and operational efficiency.
AI in banking risk management can be used for fraud detection, credit risk assessment, regulatory compliance monitoring, and predictive analytics to identify potential risks and mitigate them proactively.
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