Challenges and Use Cases of Generative AI in Finance

Generative AI is no longer an emerging innovation in the financial industry. It’s already changing how experts work, reducing time spent on processes like financial planning and analysis. As it improves efficiency, professionals can focus on higher-value strategic work.

Despite Gen AI’s impact, its adoption in the finance industry is without challenges. Data privacy concerns, strict regulations, and the need for transparency make its adoption a complex issue. 

So, how can financial professionals navigate this dilemma of efficiency and strict regulation? In this article, we’ll explore the use cases of generative AI in finance, the challenges, and strategies to balance its adoption with regulatory and customer concerns. By the end, you’ll be able to make informed decisions that optimize your AI adoption for success.

Trends and Statistics Driving Generative AI Adoption in Finance

The finance industry is known as a strictly regulated industry that often lags in adopting new technologies. Think of blockchain technology and e-signatures—gaining significant adoption across other industries—and how they haven’t been universally implemented in the banking sector. Today, there are certain high-value transactions and legal agreements where traditional signatures, wet ink, and other forms of authentication are still required.

However, the case is different with Gen AI adoption – a new technology of less than 5 years of adoption popularity – in the finance space. Why is this so? 

Let’s see some of the major trends and statistical insights speeding up Gen AI adoption in finance.

• The return on investment in AI is impressive, showing an extra $2.5X on every $1 a company invests in AI. (Microsoft)
• 50% of organizations are predicted to replace time-consuming forecasting approaches with AI by 2028 (Gartner).
• 46% of financial institutions using artificial intelligence in their processes are experiencing improved customer experience. (NVIDIA)
• 36% of clients say they are interested in using Gen AI to manage their finances, with the number increasing to over 50% for individuals younger than 50 years old. (Marqeta)
• AI technologies could generate up to $1 trillion of more value yearly globally. (McKinsey)

All of the highlighted trends show that there’s a massive opportunity, such as the need for automation, customer satisfaction, cost reduction on processes, and simplification of complex analysis by adopting AI in the finance sectors. 

However, taking advantage of these opportunities would have been impossible without the availability of emerging tools like big data and advancement in machine learning. This is where demand meets the required resources to facilitate the change in the finance industry.

Top 5 Use Cases of Generative AI in the Financial Sector

Generative AI adoption is growing quickly, and its applications are diverse. In this section, we will uncover the top 5 use cases of its application in the financial sector with notable examples.

Risk Management and Analysis

Data accuracy is the principal factor in deciding whether or not financial risk can be managed. But when the tools required to transform vast amounts of information into useful data rely on manual and full human intervention, what’s to be expected? Potential error and data manipulation will significantly affect accurate analysis, leading to the inability to make the right decisions. This is synonymous with reading a bad scale.

Now, with Gen AI, experts are working smartly and focusing on the strategic aspect as machine learning handles data compilation from different sources. These data are compiled based on automated set rules, which require less human intervention. This way, organizations can calculate risks based on the available data. 

As an example of generative AI in finance, MercadoLibre, a financial service company, leverages Gen AI’s capability to offer speedy loan services to customers. Rather than having finance experts manually review customers’ documents to determine the risk of lending, AI does the heavy lifting, allowing them to assess credit risk and provide immediate financial solutions. 

Financial Documentation and Reporting Analysis

Before now, employees complained about spending 25% of their productive 40 hours of work on searching for relevant documents. Although these claimed employees use digital document management systems, collating data manually from different folders and files was time-consuming.

Thanks to Gen AI tools like BloombergGPT, a financial expert can quickly access a customized report summary, risk analysis, project returns, and investment recommendations after feeding the AI with a client’s portfolio. This process reduces hours spent on labor-intensive documentation processes and reporting, allowing the advisor to focus on strategic decision-making and personalized client interactions.

Today, many financial institutions are embracing the power of Gen AI to improve employees’ productivity. VanEck, a popular money manager, saw the opportunities in GenAI and invested in FinChat. According to one of the investors, with this adoption, presentation deck creation, data compilation, and reports that would take 30 hours now can be achieved within 30 minutes with the right prompts.

Personalized Customer Experience

While businesses are in charge of operational controls, customers/clients are the most powerful stakeholders in shaping business strategy. According to Marqeta’s report shared earlier, customers are urging financial institutions to incorporate GenAI into their workflow for personal finance management. According to a study from MX, these customers also request quick, convenient, easy, and personalized banking experiences. 

Responding to these expectations at an individual level is impossible, but adopting AI into the process makes a significant impact. Imagine having to respond to 10 clients within an hour to offer them personalized financial advice. Logically, this requires reviewing all their data individually and analyzing them for the right suggestions. However, with the power of machine learning, optical character recognition, LLM models, and natural language processing in Gen AI, customers can get personalized information in real time.  

Let’s look at another example of generative AI applications in finance. Wells Fargo tapped into Google’s AI capabilities to offer users a personalized banking experience. This AI assistant alerts users about unusual activities, such as duplicate charges, thereby improving customer satisfaction and engagement.

Fraud Detection

Criminals’ strategies are evolving, and so are their fraud methodologies. Therefore, the rule-based, static fraud detection processes used in the financial industry can’t meet up anymore. 

With new technologies like machine learning, Gen AI is able to learn from historical data and detect anomalies without human intervention. Likewise, AI-based fraud detection is smarter and more reliable in handling a large volume of data sets and can detect complex fraud schemes even with multiple transactions and models. This process is mostly automated to reduce human intervention, which may spike errors or cause inconsistency.

With double-edged and industry-leading cybersecurity measures mixed with the power of Gen AI, banks and other finance sectors can outsmart unscrupulous acts. That’s the approach that Mastercard took when it became obvious that cybercriminals were also leveraging Gen AI’s capability to attack. As a leading Fintech, Mastercard adopted AI into its system to double the detection rate of compromised cards and improve security for cardholders. 

Investment and Portfolio Management

Compared to the traditional investment management approach, which involves manual analysis of historical data, market trends, and clients’ risk tolerance to allocate assets, Gen AI is using machine learning models to analyze massive datasets and generate optimal investment strategies. Instead of the human approach, which involves allocating assets to clients based on manual reviewing of clients’ profiles, Gen AI analyzes investor preferences, transaction history, and risk tolerance to generate personalized strategies.

This is a time-saving process that supports investment managers in making informed choices and adapting fast to market shifts. Having Gen AI in financial workflow improves finance specialists’ efficiency, which explains the projected 40.1% growth in its adoption over the next five years. 

Many Gen AI tools for investment management exist today, such as BlackRock’s Aladdin AI, BloombergGPT, and JPMorgan’s Moneyball AI.

Key Challenges in Integrating Generative AI into Finance

Without a doubt, Gen AI in the finance industry is more beneficial than problematic, considering the top 5 use cases highlighted and many more unmentioned. However, it’s still important to highlight those few challenges that may impede the successful adoption of Gen AI and learn how to address them. 

• Integration with legacy technologies

Legacy tech stacks are not built with the framework to handle modern technologies like AI models. As a result, financial institutions seeking to adopt Gen AI technology into their processes need to evaluate their current tech solutions for compatibility. Once you find out, you need an expert to guide you through implementing middleware solutions to avoid experiencing data silos and other bottlenecks.

Tech companies like GAP offer legacy system modernization services that have helped many organizations, like OPEXUS, scale up quickly. Although legacy tech tools are not problematic, their limitations become evident as you scale, just as with OPEXUS. You may check out their story to see if it’s similar to yours. 

A sneak peek of the challenges OPEXUS faced when scaling and how GAP supported them

Read more about OPEXUS’s successful scalability with GAP.

• Data privacy and security concerns

Owing to the sensitivity of finance data and the strict regulations, quality data availability is a major concern. Because of the sheer volume of data that Gen AI might have access to, financial institutions are hesitant to integrate it with their internal tools. This is a valid concern, considering that data is the new oil, and mining is also possible.

The solution to this is to implement updated cybersecurity solutions and work with AI companies with proven success stories to develop a custom solution that factors in strict security measures.

• Shortage in skill gaps and talent

An AI report by EY Financial Services shows an alarming gap in AI adoption and organizational readiness in terms of human resource capabilities. Over 90% of firms surveyed do not have employees with the skill sets required to adopt AI successfully into their business, leading to an inability to scale faster. 

However, there are Gen AI consultants who provide IT staff augmentation services that can support your internal teams. This takes off the burden of having inexperienced employees work on your Gen AI project.

• Lack of internal infrastructure

Implementing Gen AI in financial services is resource-intensive, requiring a vast amount of high-quality data and powerful computing resources. Most often, many financial firms don’t have the required cloud-based or on-premises infrastructure to store and process this data efficiently. 

One of the cost-effective ways to approach this challenge is to contact Gen AI consulting firms. This is because they often have the required infrastructure and technical expertise to support your needs. 

• Ethical considerations & AI regulation

Specific regulations about AI usage in the finance industries are yet to be established, leading to unresolved concerns about fairness, transparency, privacy, and bias. 

Until there’s a unified and clear regulation, financial institutions need to ensure strict privacy and security rules and conduct rigorous audits and ethical reviews. 

Invest in the Right Finance Generative AI Strategy

While the use of generative AI in finance has come to stay, there is no one-size-fits-all approach to its implementation. Therefore, the difference between successful Gen AI adoption and failure lies in strategic implementation. 

At GAP, we specialize in building AI-powered software solutions that are tailored to different business growth goals. We provide modernization solutions designed to meet the banking industry’s regulatory standards and security concerns. With 18 years of experience in operation, we bring the expertise required to get the most out of generative AI benefits.

Choose a Gen AI consulting firm that understands your industry’s unique challenges. Partner with GAP today to optimize operations, enhance insights, and drive lasting impact. Get our free Gen AI strategy today, and let’s guide you through a successful AI adoption in financial services.