Benefits and use cases of generative AI in banking

The banking industry is no stranger to technological disruption, and generative AI is the latest transformative force to emerge. Generative AI, an advanced machine learning technology capable of analyzing big amounts of data and producing unique insights and content from it. It allows bank managers to make informed decisions, provide personalized services, assess risks, and more by harnessing financial data at scale.

Depending on the adoption, generative AI can potentially save the banking sector up to $340 billion annually, according to McKinsey. The technology brings efficiency into banking operations, reducing human mistakes and saving operational costs. More importantly, generative AI development services allow banks to provide a personal touch to customer interaction without being over-reliant on human personnel. 

As the co-founder of Uptech, I lead a team at the forefront of generative AI. We’ve helped our clients embrace the deep learning model in digital products like Plai, Hamlet, and Angler AI. Besides, we’ve also launched, an app that uses generative AI to generate unique avatars from a selfie. Then, we repurpose Dyvo for businesses by enabling users to create studio-grade product photos with realistic AI-generated backgrounds.

In this article, I’ll share how generative AI applies to the banking industry and the challenges organizations must address when adopting the technology.

generative AI in banking

How AI is Transforming the Banking Sector 

Financial institutions compete in a matured marketplace where only a few factors differentiate one from another. The emergence of AI throws that race wide open. Banks are in a prime position to leverage AI to achieve a competitive advantage, provide attractive products, and strengthen their customer base. According to a survey, 77% of bankers agreed that the ability to unleash the full potential of AI is key to organizational survival in the banking industry. 

Maturing cloud infrastructure, storage solutions, and security technologies allows banks to implement strategies with a wide-ranging impact. I believe AI adoption's implications, particularly those powered by large language models, will revolve around enhancing customer experience. The Deutsche Bank, for example, uses AI to help customers manage investment portfolios by making relevant suggestions based on known risk factors.

Generative AI enables conversational banking by simplifying access to various banking products and services. The machine learning model powering such technologies is trained to understand and converse in natural human languages. This means banks can expand their product offering to different regions while maintaining information consistency and support to different geographical audiences. 

Banks will also benefit from the automated features that AI bring into the conventional banking workflow. With AI, banks can maintain a 24/7 presence on different channels to handle customer inquiries and resolve issues. This way, AI assists human support personnel in answering common questions, allowing the latter to focus on complex cases. 

AI use cases in banking

Popular Use Cases of Generative AI in Banking

Banking is an industry that involves numerous human interaction points. And this presents vast opportunities for generative AI to bring impactful changes. I share several practical use cases below.


Most banks are already using chatbots to deal with customer requests. Generative AI allows bankers to apply large language models capable of conversing like humans. Instead of navigating through a series of choices, customers can use phrases like ‘how much is my balance’, or ‘change my billing address’ to get prompt solutions. 

Portfolio management

Generative AI allows banks to adopt a more fine-grained approach when recommending portfolio strategies to customers. The deep learning model first trains on vast economic data. Then, bankers use the AI system to predict future trends based on various changing financial variables, including currency rates, inflation, and political dynamics, to devise a fitting portfolio. This happens without requiring customers to openly disclose their financial standings, which provides a more comfortable and private banking environment.

Compliance management

In a highly-regulated industry, banks face constant pressure to comply with stringent regulations. This involves monitoring transaction activities, consolidating relevant information, and submitting them to the respective departments on time. Using an AI system trained on such processes will lift the burden of compliance management. For example, bankers use generative AI to analyze customer data and ensure they comply with the Know Your Customer (KYC) Act before approving an account. 

Financial analysis and forecasting

Like other businesses, banks must strategize and maintain a strong position in evolving market conditions. With generative AI, they can run simulations, predict economic trends and adjust their positions accordingly. For example, banks can use  AI to forecast the inflation rate in the medium term and make appropriate adjustments to the interest rate. 

Financial advisor

Generative AI enables banks to dedicate equal and personalized engagement to each customer. The deep learning model analyzes the customer’s historical data, spending behaviors, and risk appetite before suggesting products that might interest them. This increases sign-up rates and also helps retain existing customers. 

AI-based fraud detection

Due to rampant data breaches, banks face regulatory pressure to safeguard customers’ interests and prevent fraudulent attempts. Generative AI can be trained to identify abnormal patterns in large volumes of financial transactions and raise alerts immediately. This allows banks to halt suspicious transactions and maintain customer trust. 

generative ai in banking

Loan score management

Bankers evaluate several criteria before approving or rejecting a loan application. Generative AI assists credit scoring by analyzing the applicant’s financial history and current data. For example, you can train the machine learning model to predict the likelihood of a default by assessing the applicant’s salary, age, occupation, home, and other credit indicators.  

Automation of back-office processes

Banks invest heavily in the workforce to operate back-office processes like document scanning, personnel identity verification, and securing networking infrastructure. Integrating generative AI into the workflow lifts some of the burden from operation staff. For example, they can use NLP software to scan, process and categorize physical documents in secure cloud storage. 

Financial report generation

Generative AI is built from machine learning models capable of presenting well-structured information. This allows banking AI systems to automatically generate financial statements on demand. For example, customers can request customized cash flow or income reports, which the AI compiles into files in seconds. 

Legacy software maintenance

Some banks are still using software developed from obsolete programming languages. Instead of rewriting the software from scratch, developers use generative AI and the underlying large language models to generate the code. This improves coding efficiency and reduces human errors when migrating the software to a newer programming framework. 

Benefits of AI in the Banking and Finance Industry

benefits of enerative ai in banking

Banks already notice substantial benefits after integrating AI into their customer and back-facing processes. Amongst them are:

  • Better decision-making. Bankers, fund managers, and other financial stakeholders support their decision with AI-enabled insights. They use AI systems to mitigate risks and maximize opportunities in volatile market conditions.
  • Personalized customer experiences. Customers are no longer served with generic product offers or must wait extensively for support. Instead, they are attended to promptly and get access to tailored information at all times on websites, apps, and other digital channels.
  • Improved efficiency. AI technologies allow financial institutions to scale operations and overcome bottlenecks hampering manual processes. They use AI systems to automate resource-intensive and repetitive tasks, allowing the banking staff to offer more value to customers. 
  • More robust security. Financial institutions use AI to secure customers from data risks. Simultaneously, AI allows banks to remain agile when responding to online threats by spotting and stopping fraudulent activities in real time. 
  • Improved risk management. AI helps banks mitigate risks better by analyzing the vast financial data available. Rather than rushing to a decision, bankers apply predictive insights to protect assets, navigate challenges and capitalize on market opportunities.
  • AI-assisted regulatory compliance. Banks use AI to continuously assess their commitment to meeting regulatory requirements. AI systems automate tasks previously carried out by bank officers, which helps institutions reduce costs and avoid hefty penalties. 
  • Improved privacy. Customers feel safer and more comfortable when discussing financial options with AI. They can choose not to divulge personal details and request the complete removal of conversational data after the session.

What Limits Generative AI in Finance and Banking 

There is no denying that generative AI can benefit banks and lending institutions. Yet, the industry must address specific concerns and approach generative AI cautiously for financial use cases. 

Data quality

Scaling generative AI applications for banks require access to large volumes of high-quality training data. Foundational models, or deep learning models, must be trained before they are fit to perform banking-specific tasks. If the training data is not relevant, accurate, complete, or large enough, the resulting AI system will not perform as intended. 

Privacy and security

Training generative AI models involve storing and moving a large amount of data on the network infrastructure. Financial regulations might restrict banks from using certain data usage to train deep learning models. Moreover, there are data risks that banks must address to protect customers’ privacy when training or deploying generative AI systems. 


Bias is a phenomenon that happens when the model lacks sufficient data to train on. A biased model cannot guarantee accurate results. For example, an AI credit scoring might reject an applicant because it doesn’t have enough data for a specific demographic. Such decisions are unfair and might jeopardize the bank’s reputation. 

Numerical accuracy

Generative AI is not designed for arithmetics calculations (at leaast for now). Calculators conform to specific rules when performing mathematical operations on existing operations. Meanwhile, generative AI models generate new answers, which might or might not be accurate. So, it’s sensible to enforce safeguards, such as human approval, to prevent erroneous results from affecting banking operations. 

generative ai in banking

Future of Generative AI in Banking 

Generative AI will transform how banks operate and engage their customers. Already, we’re seeing the impact of generative AI impacting the broader consumers – in the form of ChatGPT.

ChatGPT allows users to tell stories, write program codes, create lyrics, and more by entering specific prompts. Likewise, the banking industry will see similar use cases but repurposed for finance operations.

Both bankers and customers will benefit from the efficiency and personalization that generative AI brings when implemented across the institution. But first, banks need a solid AI implementation strategy to start with.

How can banks transform to become AI-first? 

Banks seeking to harness the full potential of generative AI must ensure they have the capabilities to scale the machine learning technology organization-wide. Instead of maintaining siloed operations, banks must consolidate different departments and prepare staff with skills, values, and mindsets to embrace AI. For example, banks hire AI specialists or reskill talents to stay up-to-date with emerging AI technologies. 

Then, banks must reexamine their technological capabilities and infrastructure to support AI systems. Training and deploying generative AI models require secure storage of large amounts of data. Banks must also consider the possibility of integrating in-house AI capabilities with external services and whether existing tech capabilities are sufficient.

With the infrastructure in place, banks embed generative AI models to support decision-making in different domains. Such actions should be pragmatic, with proper risk assessment and efforts to continuously improve the AI models. For example, they augment repetitive or human-intense processes with AI, such as loan approval or customer support. 

To fully impact customers with AI,  banks need to rethink their customer engagement strategies. Instead of focusing on specific products, banks should reimagine how AI can be roped in to add more value to customers. For example, Tally uses AI algorithms to help customers manage credit card payments and get out of debt fast.

Tips on using Generative AI in banking  

Despite the optimism, the path to implementing generative AI in banking use cases remains fraught with challenges. I share several ways to make such changes more manageable.

  1. Identify a specific banking process that benefits most from generative AI. Train, deploy, and test the AI system before scaling it to other use cases. For example, you train an AI chatbot to augment the customer support team before expanding the technology to critical use cases like credit scoring. 
  1. Ensure the appropriate data protection mechanisms are in place to safeguard customer privacy and comply with industry regulations. For example, encrypting data that generative AI uses prevents misuse if they are inadvertently leaked.
  1. The performance of the generative AI model is highly dependent on the quality of training datasets. So, make sure that the data are appropriately labeled and large enough to represent the target demographics. 
  1. Be wary about using generative AI for calculations, as they are not yet meant for precise arithmetic calculations. Put proper safeguards in place to prevent erroneous results from affecting customers.
  1. A generative AI model computes on prompts it receives. When using it to personalize the banking experience, prompt the model with specific customer financial information, such as past transactions, financial goals, and risk tolerance, to produce relevant results. 
generative ai in banking


Generative AI will change how banks engage customers with personalized and efficient services. It is also poised to save financial institutions substantial costs when implemented smoothly. Whether augmenting customer support with chatbots or detecting fraudulent transactions, generative AI has tremendous roles in the finance industry.

Yet, challenges remain in rolling up generative AI at scale, particularly in a tightly-regulated industry. Here, Uptech’s experience with AI comes into play. We’ve helped companies like Plai integrate generative AI into existing solutions. Our AI products, Dyvo and Dyvo for Business, are evidence of our technological capabilities to integrate AI into your banking business. 

Talk to our team to get started with AI in banking. 


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