Businesses often hesitate when it comes to AI integration. One of the main reasons is that they simply don’t know how exactly generative AI can improve their work processes. And they have a point. Generative AI is a new kid on the block, so it’s still unclear how businesses can achieve better results by adopting it.
Still, many companies, especially in finance, have already adopted Gen AI to serve their business needs. And the results were well worth it. From more personalized financial guidance to fraud detection being improved by 300%, AI helps financial companies in numerous ways, speeding up and streamlining their core processes. Generative AI is also transforming various sectors within the banking industry, including retail banking and investment banking, by enhancing customer service and driving innovation in financial services.
In this article, we’ll discuss the main use cases of Gen AI in finance. You’ll find real-life examples for each use case and see how financial services can be enhanced with Gen AI.
But before that, let’s quickly summarize what is Gen AI and why it’s worth considering and see its benefits for the finance industry in general.
Generative AI is a cutting-edge technology that is transforming the financial services industry. By analyzing large datasets and identifying patterns, generative AI enables the creation of novel content such as financial reports, risk assessments, and investment strategies. This technology has the potential to revolutionize various aspects of financial services, including risk management, fraud detection, customer service, and investment strategies.
Financial institutions are increasingly adopting generative AI to improve operational efficiency and enhance customer experience. For instance, AI-driven tools can automate repetitive tasks, allowing employees to focus on more strategic activities. Additionally, generative AI can provide personalized financial guidance, helping customers make informed decisions. By leveraging generative AI, financial institutions can gain a competitive edge in the market, offering innovative solutions that meet the evolving needs of their clients.
When talking about Gen AI benefits, the first thing you are likely to hear is that it can automate numerous low-value, repetitive tasks, resulting in less time and resources wasted. While true, the generative AI value extends far beyond this.
To outline the real value proposition of Gen AI, McKinsey provides a “4C’s” framework highlighting four areas of how businesses can benefit from AI:
To better understand how it works, let’s take a brief look at each category.
Gen AI can help analyze documents by extracting insights from vast amounts of data in seconds, detecting errors, validating information sources, and boosting productivity. Additionally, generative AI automates data analysis, enhancing efficiency and providing insights for decision-making.
Gen AI can speed up the creation of content, automatically drafting documents like contracts, NDAs, and reports, or writing creative content for social media and news.
AI-driven assistants automate client management, handling common client inquiries and guiding users through complex processes like applying for a personal loan.
Gen AI helps interpret, translate, and generate code, allowing financial companies to build new products faster or seamlessly migrate from legacy systems to modern platforms.
All these benefits sound great on paper, but what about real-life products? Let’s take the example of Aumico, a financial reporting product, and see how Gen AI can enhance its functionality.
Aumico is a SaaS reporting tool developed in collaboration with Modeso to help accountants prepare annual financial statements with just a few clicks, overcoming spreadsheet struggles and the need for manual reviews.
Aumico is a successful product with 4,000 recurring licenses and a growing customer base – but there’s a possibility to offer clients even more with the help of AI. How exactly? By building an AI chatbot that offers customers personalized financial guidance.
Rather than just building complex financial dashboards, an AI-powered chatbot can also serve as a personal assistant, helping users break down financial metrics, explaining where costs can be potentially optimized or cut, or comparing a company’s metrics and performance to others in the industry.
With Gen AI on board, Aumico can gain such new features:
Provide current financial insights
AI can answer user questions about the company’s current financial performance.
Analyze historical trends
The chatbot can examine the company’s performance over the past years to identify trends and patterns. By analyzing historical financial data, AI models can generate insights and improve financial forecasting and reporting.
Benchmark against peers
The solution can compare the company’s performance with other businesses in the same industry and region.
This is just one example of what Gen AI can do when implemented into a financial product. Now let’s explore the most popular use cases of Gen AI in finance.
Here’s a brief overview of the common Gen AI use cases in finance with real-life examples.
As we’ve mentioned above, generative AI can be applied to develop intelligent chatbots and virtual assistants. These AI-driven tools provide personalized, real-time support, enhancing user experience and reducing operational costs. The transformative potential of generative AI in fintech is significant, particularly in enhancing customer service through personalized financial services.
Fargo serves as a good example here. Built into the Wells Fargo Mobile app, it leverages AI to help users manage card limits, monitor subscriptions, search transactions by a specific business, date, or amount, and compare monthly spending patterns. This AI-driven tool alerts users about unusual activity, such as duplicate charges or changes in subscription amounts, and forecasts the amount needed to cover upcoming expenses.
Similarly to Aumico and Fargo, businesses can use large language models (LLMs) like GPT-4 for building solutions that offer personalized financial guidance. In our other article, we described how you can build an LLM app in a cost-effective way, so feel free to learn more.
Generative AI enhances fraud detection in finance by creating synthetic examples of fraudulent transactions and activities. AI-powered systems analyze vast amounts of transaction data to predict and identify emerging types of fraud, allowing banks and businesses to prevent new kinds of cyberattacks.
As an example, Mastercard has recently launched a generative AI model designed to help banks detect suspicious transactions on its network with much greater accuracy. According to Mastercard, this technology could improve fraud detection rates by up to 20%, and in some cases, by as much as 300%.
For businesses, AI-driven fraud detection solutions mean creating more secure systems and significantly reducing the costs associated with fraud. This not only makes transactions more secure but also builds trust with customers, ensuring their financial data is protected by advanced security measures.
Gen AI can help banks and financial institutions assess creditworthiness, set appropriate credit limits, and determine loan pricing based on individual risk profiles. Credit risk assessment plays a crucial role in managing financial risks and enhancing loan decision-making processes using generative AI. As a result, companies make more accurate and fair lending decisions, improving customer satisfaction.
One prominent example to highlight is Zest AI. Zest AI uses machine learning models to provide more accurate credit scoring by analyzing a broader set of data compared to traditional credit scoring methods. This includes non-traditional data such as utility payments and social media activity, offering a more comprehensive view of an applicant’s creditworthiness.
Zest AI can predict credit risk with higher precision, leading to better decision-making process. The technology also enables lenders to extend credit to a wider range of applicants who may have been overlooked by traditional scoring methods. This AI-powered capability enhances fairness in lending, helping financial companies increase approval rates without extra risks.
Taught on human-generated text, AI can replicate human tone of voice and style, making it a perfect fit for automated consulting services. For example, AI can give customers human-like explanations of why their loan application was denied.
Fujitsu has developed a system that uses Generative Adversarial Networks (GANs) to generate explanations for loan denials. The system educates applicants about why their application was denied and suggests actions to improve their chances of future approval. For instance, the system can identify if an applicant's loan is denied due to inconsistent loan payments and suggest maintaining a consistent record of timely loan payments to change this decision.
Such systems help applicants understand the reasons behind their loan denials and what they can do to improve their creditworthiness, resulting in better transparency and customer experience.
According to Goldman Sachs, two-thirds of the actual workload could be automated with the help of AI. Accurate and reliable data in financial services institutions is crucial, as poor data quality can lead to significant miscommunications or inaccuracies. Generative AI can help improve data quality and operational efficiency in these institutions.
Fargo and Aumico are good examples of how Gen AI can be implemented to reduce the number of manual tasks for financial service companies. Now, let’s see how AI can be applied to boost employee productivity.
OCBC Bank (Oversea-Chinese Banking Corporation), in collaboration with Microsoft’s Azure OpenAI, launched a generative AI chatbot to enhance productivity among its employees. This innovative tool, known as OCBC GPT, automates a variety of time-consuming tasks, such as drafting investment research reports, translating content into multiple languages, and creating customer responses.
The results of the trial were impressive, with participants reporting a 50% increase in productivity. With this AI-driven chatbot, OCBC significantly reduced the operational costs associated with the tasks that previously were performed manually.
Generative AI can be leveraged for trading and investment strategies to analyze large datasets, identify patterns, and make predictions about future market events. Additionally, generative AI can provide personalized financial planning advice, helping clients make informed decisions on investments, savings, and budgeting based on their individual goals and data. One of the solutions that applies Gen AI is TrendSpider, an industry-leading platform for asset analysis and stock trading.
TrendSpider offers features like dynamic price alerts, automated trendline detection, and multi-timeframe analysis, enabling traders to analyze complex market data in minutes. TrendSpider’s AI-driven tools also enable users to backtest trading strategies, ensuring their effectiveness before applying them in live markets. This reduces the time traders spend on manual chart analysis and helps them make more informed, data-driven decisions.
Let’s sum up the main Gen AI use cases in finance:
6 Gen AI use cases in financial services
But while generative AI holds great promise for the financial services industry, it also presents several challenges and limitations.
Here are some of the challenges associated with Gen AI:
Addressing these challenges is essential for the responsible and effective use of generative AI in financial services. Here’s how financial services should approach Gen AI implementation.
Implementing generative AI in financial services requires a strategic approach. Financial institutions need to identify specific use cases where generative AI can add value, such as risk management, fraud detection, and customer service. By focusing on these areas, institutions can maximize the benefits of AI.
A robust data strategy is essential to ensure access to high-quality data for training and validating AI models. Financial institutions should invest in the necessary talent and technology to support the development and deployment of generative AI. This includes hiring data scientists, AI specialists, and investing in advanced computing infrastructure.
Governance and regulatory frameworks are also crucial to ensure the responsible use of AI. Financial institutions must establish clear guidelines and policies to address issues such as transparency, bias, and data security. By taking a strategic and comprehensive approach, financial institutions can successfully implement generative AI and unlock its full potential.
As you can see, Gen AI can improve pretty much anything in the financial industry, be it fraud detection and trading or reporting and customer support. Generative AI for finance delivers personalized business analysis and tailored advice based on individual data and user behavior.
The future of generative AI in financial services is promising. As the technology continues to evolve, we can expect to see more advanced applications in areas such as risk management, investment strategies, and customer service. Financial institutions that adopt generative AI early will be able to gain a competitive edge and improve their operational efficiency.
If you want to tap into these opportunities and keep up with other companies, consider adopting AI today, as the competition is only getting more fierce. To achieve the best outcome with Gen AI adoption, it’s crucial to find a reliable technology partner who knows the intricacies of adopting AI.
Having extensive expertise in generative AI implementation, helps financial companies develop and implement tailored solutions that align with their specific needs. From enhancing decision-making processes to automating routine tasks, we ensure that our clients can fully adopt the potential of AI, maintaining a competitive edge in the financial sector.
Looking to turbocharge your financial services with Gen AI? Contact us and we’ll help you turn your idea into a reality.
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