When you think of potential use cases for generative AI, many things may spring to mind, from songwriting to coding, but using the technology in the financial sector most likely isn’t the first of them. However, just like Chat GPT in healthcare, GenAI has many applications in finance and banking.

In fact, we should go one step further to say that generative AI in finance is a dynamically developing field that has created an equally expanding market. It is estimated to grow at a CAGR of 28.1%, reaching a total value of almost $9.5 billion by 2032. The steadily growing valuation proves that GenAI is widely recognized as a vital technology worth investing in.

However, there are many other reasons why financial institutions can’t afford to ignore generative AI in banking. In this article, we’ll look into the advantages of GenAI and the challenges it helps address in the finance and banking industry.

What’s Generative AI in finance and banking?

As a quick reminder, generative AI, or GenAI, refers to AI-based solutions that use deep learning algorithms to mimic human-like creativity and produce new content. The output can range from text and images to music, programming code, and other formats.

GenAI has found ample applications in virtually every industry thanks to its capabilities.

  • Generative AI is used for content production and generating ideas in marketing and creative industries.
  • In IT, GenAI automates and expedites code writing.
  • Generative AI also aids healthcare professionals in medical imaging, drug discovery, and personalized treatment.
  • Additionally, the technology serves general purposes in any sector, such as administrative tasks, customer support, and text processing.

And what about the role of generative AI in finance? In the banking sector, its applications range from industry-specific to generic. GenAI is used mainly to help financial institutions overcome existing challenges and advance their services by analyzing financial data and producing reliable outputs faster than human employees could. But more about it in a moment.

Overcoming banking challenges with generative AI in finance

Belonging to a heavily decision-driven industry, financial institutions have always sought solutions that could lower the risk and margin of error. As one such solution, AI has a long history of use in the banking industry, going back to the ‘80s. But what challenges has it helped address since?

Fraud detection and prevention

Credit card information, personal records, bank account details—there’s no shortage of vulnerable data in finance, which makes the sector one of the primary targets for cyberattacks. Data protection is among the top priorities for financial institutions, and generative AI helps them achieve it.

Here, GenAI tools are used in tandem with fraud detection algorithms. These algorithms use machine learning (ML) to self-train on past fraud attempt data, but when faced with ever-evolving techniques, they often struggle to keep up.

Generative AI finds and replicates patterns in fraud data to create large volumes of synthetic “anomalies”. Synthetic data is then used to refine detection algorithms, allowing them to stay ahead of fraudsters. Consequently, cybersecurity algorithms require less supervision, allowing for a higher level of automation and better efficiency at identifying cyber attack attempts. As a result, the combination of GenAI and fraud detection algorithms prevents financial losses and boosts customer trust.

Personalized financial services and support

According to J.D. Power’s 2022 U.S. Retail Banking Satisfaction Study, 78% of consumers expect personalized support from their bank. However, only 44% of them felt that they received it. Personalized services and support can be an important differentiating factor, reflected by the fact that personalization at scale can lead to a 10% increase in yearly revenue.

Currently, scaling up proves challenging for financial institutions. Providing personalized support and services requires processing vast amounts of customer data: transaction history, spending preferences, financial products, saving goals, and more. Generative AI can use that information to develop custom-made recommendations quickly and offers for each customer, creating cross-selling opportunities for companies and boosting customer satisfaction.

Moreover, generative AI for finance enables better self-service through intelligent virtual assistants or automated form submission. Thanks to GenAI, these solutions can faster process data and generate human-like answers, mimicking real customer care agents. With that, customers can receive the help they need much faster, while banks reduce operational costs and improve customer engagement.

Risk assessment and credit scoring

Financial institutions need to evaluate the creditworthiness of each customer and assess the potential risks before making lending decisions. Credit scoring is an integral part of the process and involves assigning credit scores to individuals or businesses based on their credit history and financial information. 

Traditional credit scoring and risk assessment methods use historical data and predefined rules. However, these methods are inflexible and may fail to capture credit risk complexity and dynamic nature. Additionally, due to a near-zero margin of error, both processes require constant monitoring and thorough data analysis, which makes them very time-consuming.

Here, GenAI is used similarly for fraud detection. Generative algorithms create synthetic data that closely resembles accurate financial data for possible scenarios. Next, it’s compiled with actual data to create datasets for training predictive analytics tools. A more diverse example base refines the analytics engine, making its predictions more accurate.

At the same time, GenAI can factor in large volumes of dynamic data, resulting in more reliable and truthful credit scores without excessive manual effort.

Operational Efficiency

As with all forms of Intelligent Automation, generative AI in finance can save banks thousands of working hours by streamlining routine tasks. Insider Intelligence estimates that AI-based applications can save financial institutions $447 billion.

In large part, these savings are generated through reducing human error. GenAI can process large volumes of financial data without overlooking details and produce consistent reports.

GenAI can also serve finance professionals as a personal, intelligent assistant thanks to its language interpreting and content creation capabilities.

How can generative AI support bankers

Relaying tedious but necessary daily tasks to GenAI, bankers can devote more time to working directly with clients. As a result, operational efficiency and customer satisfaction increase, generating savings and driving additional revenue.

Compliance and Regulatory Challenges

Regulatory compliance is an essential banking activity linked to risk assessment and human error. Financial institutions are legally obliged to follow regulations covering operations, confidentiality, security, and best practices. Meeting these criteria requires thorough data collection, extensive analyses, and reporting, all of which are prone to errors and highly time-consuming.

Banks can offload many of these tasks to GenAI.

  • Generative AI in finance can produce synthetic data to boost the accuracy of internal compliance controls and quality assurance.
  • GenAI’s data processing and generating capabilities enable fast, consistent, and error-free compliance reporting.
  • Generative AI can monitor compliance continuously and send automated notifications to compliance teams when a violation occurs.

Overall, generative AI in banking helps financial institutions avoid costly fines, reduce manual effort, and build their reputation as safe and compliant organizations.

Market and Investment Analysis

Financial analysis involves working with large datasets, including market trends, reports, event transcripts, estimates, company filings, etc. To keep up with the shifting financial landscape and spot investment opportunities on time, analysts must monitor all that data continuously, which takes significant time and effort.

GenAI algorithms can easily go through vast historical records to identify patterns and anomalies that could go unnoticed by human analysts. Based on automated analysis, generative AI generates insights and creates trading parameters such as optimal entry and exit points for specific financial assets, stop-loss levels, and position sizing. This gives banks a competitive advantage, a better understanding of market conditions, and enables data-driven strategizing.

GenAI and traditional banking processes

How do financial institutions use generative AI in finance?

Organizations within the finance industry are starting to recognize the benefits of generative AI in banking. Here are some real-life examples of how they do it.

Morgan Stanley — supporting financial decisions

As a major player in the wealth management sector, Morgan Stanley stands at the forefront of innovation in finance. In March 2023, it announced a strategic partnership with OpenAI, granting Morgan Stanley early access to new solutions developed by the GenAI firm.

It’s the next step toward wider GenAI adoption, which started beforehand with internally built solutions like Next Best Action. The engine was trained on 100,000 company documents to support Morgan Stanley’s financial advisors and clients through customized financial advice and answers to questions on investment, markets, and internal processes.

Deutsche Bank — operational improvements across the board

In another big partnership, Deutsche Bank is working with Google to develop generative AI engines and large language models that could provide insights to financial analysts and improve operational efficiency and execution speed.

Deutsche Bank’s goals are far-reaching. With the help of GenAI, the bank hopes to expedite processes and financial analysis, increase employee productivity, enhance customer data privacy, and improve overall system security.

Mastercard — your personal shopping assistant

Mastercard plans to use a GenAI-based shopping recommendation engine for a more customer-oriented example. Shopping Muse will offer personalized product suggestions by analyzing the user’s shopping habits, history, on-site behavior, and retailer’s product catalog.

Shopping Muse’s most innovative feature is how it interacts with customers. Its advanced language interpretation functionalities allow shoppers to ask the tool questions in colloquial language to receive personalized shopping advice.

Though this example stands closer to the retail industry, financial institutions can use it as an inspiration for enhancing customer experience, support, and engagement through generative AI in banking.

Reshaping the industry with generative AI in banking

With well-established AI systems in the banking and finance sector, it’s time to take it one step further. From personalized care to operational improvements, generative AI creates new opportunities for financial institutions.

Now, if your organization needs help in adopting generative AI in finance, you’re in the right place. Just get in touch, and let’s discuss the future of your business.

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Michał Rejman

Chief Marketing Officer at Flobotics. Communication strategy consultant for tech and process automation buff. Remote work evangelist, surfer, and doggo lover.

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