Generative AI in Finance: Applications and Use Cases

Generative AI in Financial Services: Use Cases, Benefits, and Risks

For one thing, mathematical models trained on publicly available data without sufficient safeguards against plagiarism, copyright violations, and branding recognition risks infringing on intellectual property rights. You can foun additiona information about ai customer service and artificial intelligence and NLP. A virtual try-on application may produce biased representations of certain demographics because of limited or biased training data. Thus, significant human oversight is required for conceptual and strategic thinking specific to each company’s needs. Generative AI has revolutionized customer service in the financial sector through the implementation of chatbots and virtual assistants. These intelligent systems can handle a myriad of customer queries, providing instant responses and assistance. By automating routine interactions, financial institutions can enhance customer satisfaction, reduce response times, and allocate human resources to more complex tasks.

However, as generative AI continues to develop and mature, it has the potential to open wholly new frontiers in creativity and innovation. It has already expanded the possibilities of what AI overall can achieve (see sidebar “How we estimated the value potential of generative AI use cases”). But a full realization of the technology’s benefits will take time, and leaders in business and society still have considerable challenges to address. These include managing the risks inherent in generative AI, determining what new skills and capabilities the workforce will need, and rethinking core business processes such as retraining and developing new skills. Unlike traditional IVR systems, and even many basic AI voice solutions, which often frustrate members with inaccurate information and repetition loops, Olive offers a more personalized and intuitive experience.

Cross-industry Accenture research on AI found that just 1% of financial services firms are AI leaders. The median score for AI maturity in financial services is 27 on a scale — nine points lower than the overall median. We’ve reached an inflection point where cloud-based AI engines are surpassing human capabilities in some specialized skills and, crucially, anyone with an internet connection can access these solutions. This era of generative AI for everyone will create new opportunities to drive innovation, optimization and reinvention.

Our experts at IBM Consulting are taking a comprehensive look at generative AI for F&A and considering the need to balance risks (link resides outside ibm.com). Its insights guide portfolio managers in strategic asset reallocation, ensuring timely responses to fluctuations. The technology’s predictive prowess extends to interpreting global economic indicators and historical factors.

Benefits of Generative AI in Financial Services

Conventional investment techniques often rely on historical data, limiting their adaptability to rapidly changing market conditions and potentially hindering optimal returns. Creating accurate and insightful financial reports is a labor-intensive, time-consuming process. Analysts must gather data from various sources, perform complex calculations, and craft digestible narratives, often under strict deadlines. The use of technology leads to more informed decision-making, reducing potential losses for institutions. In cases of credit decisions, this also includes information on factors, including personal data that have influenced the applicant’s credit scoring. In certain jurisdictions, such as Poland, information should also be provided to the applicant on measures that the applicant can take to improve their creditworthiness.

Generative AI algorithms adhere to these regulations, ensuring that they operate within the legal framework. This commitment to compliance provides an additional layer of assurance that the technology is being used responsibly and ethically, aligning with industry standards and legal requirements. By automating these tasks, financial professionals can focus on more strategic aspects of their work, while also ensuring that communication is clear, concise, and tailored to the intended audience. The adaptability of Generative AI in learning from new data ensures that the system evolves alongside emerging threats, enhancing its efficacy in preventing financial fraud. Since the release of ChatGPT in November 2022, it’s been all over the headlines, and businesses are racing to capture its value.

The most successful banks have thrived not by launching isolated initiatives, but by equipping their existing teams with the required resources and embracing the necessary skills, talent, and processes that gen AI demands. Management teams with early success in scaling gen AI have started with a strategic view of where gen AI, AI, and advanced analytics more broadly could play a role in their business. This view can cover everything from highly transformative business model changes to more tactical economic improvements based on niche productivity initiatives. As a result, the institution is taking a more adaptive view of where to place its AI bets and how much to invest. For a long time, the finance industry has been combating fraud as it grows with technological advancements.

Helping clients meet their business challenges begins with an in-depth understanding of the industries in which they work. In fact, KPMG LLP was the first of the Big Four firms to organize itself along the same industry lines as clients. KPMG has market-leading alliances with many of the world’s leading software and services vendors. Leaders must acquire a deep personal understanding of gen AI, if they haven’t already. Investments in executive education will equip them to show employees precisely how the technology and the bank’s operations connect, thereby generating excitement and overcoming trepidation. Our in-depth understanding in technology and innovation can turn your aspiration into a business reality.

AI-driven intelligence also plays a significant role in monitoring market reactions. It evaluates the impact of financial trends, new items, and market changes on consumer attitudes. This insight helps businesses tailor their offerings and strategies to better meet demands. Portfolio managers optimize asset management, mitigating risks while seeking maximized returns. This approach translates to a competitive edge in the field, offering clients enhanced investment performance. Artificial intelligence is a game-changer for firms prioritizing informed decision-making and profitability.

Supercharge your Finance workforce with GenAI

Intelligent character recognition makes it possible to automate a variety of mundane, time-consuming tasks that used to take thousands of work hours and inflate payrolls. Contact Master of Code Global today and let’s explore how our customized solutions can revolutionize your financial operations. GAI enables businesses to capitalize on industry shifts with agility, maximizing returns and outpacing competitors. Fraud management powered by AI raises security standards, safeguards client assets, strengthens brand image, and reduces the operational strain on the investigation teams. Integrating GAI for report generation frees up expert’s time for strategic analysis, reduces errors for greater accuracy, and accelerates the identification of key recommendations for boosting agility.

They paint a tomorrow where artificial intelligence not only enhances existing conditions but also spawns unexplored forms of economic interaction and industry benchmarks. For companies, these improvements will be key to staying ahead of the curve in a rapidly developing digital economy. Thus, we observe a shift towards exploring innovative services and business models previously unfeasible. Predicting market directions with artificial intelligence will become increasingly sophisticated. The ability to foresee demand dynamics enables more strategic investment and operational decisions.

Automation of accounting functions

They can therefore accelerate time to market and broaden the types of products to which generative design can be applied. For now, however, foundation models lack the capabilities to help design products across all industries. While generative AI is an exciting and rapidly advancing technology, the other applications of AI discussed in our previous report continue to account for the majority of the overall potential value of AI. Traditional advanced-analytics and machine learning algorithms are highly effective at performing numerical and optimization tasks such as predictive modeling, and they continue to find new applications in a wide range of industries.

For the wide range of use cases of conversational AI for customer service operations, check our conversational AI for customer service article. Gen AI isn’t just a new technology buzzword — it’s a new way for businesses to create value. While gen AI is still in its early stages of deployment, it has the potential to revolutionize the way financial services institutions operate. As Generative AI rapidly advances, its implementation in finance brings some big hurdles and potential risks.

As an example of how this might play out in a specific occupation, consider postsecondary English language and literature teachers, whose detailed work activities include preparing tests and evaluating student work. With generative AI’s enhanced natural-language capabilities, more of these activities could be done by machines, perhaps initially to create a first draft that is edited by teachers but perhaps eventually with far less human editing required. This could free up time for these teachers to spend more time on other work activities, such as guiding class discussions or tutoring students who need extra assistance. Researchers start by mapping the patient cohort’s clinical events and medical histories—including potential diagnoses, prescribed medications, and performed procedures—from real-world data.

Generative AI could propel higher productivity growth

At times, customers need help with specific issues that aren’t pre-programmed into existing AI chatbots or covered by the knowledge bases that customer support agents use. With Generative AI, producing realistic and representative data for regulatory financial reporting also gets streamlined, making it easier for finance professionals to fulfill their reporting obligations accurately and quickly. Generative AI greatly contributes to fraud prevention efforts thanks to its ability to create synthetic data that mimics fraudulent patterns, allowing it to continually refine detection methods. The idea for Supio came about after Zhou and Lam left Avalara to pursue building a business that could, in Zhou’s words, “help understand complex data and identify critical connections within certain data.” Appriss Retail’s Incident + ORC Intelligence solution fills a gap in how law enforcement works with retailers on cases of ORC. The solution not only streamlines the investigative process but facilitates efficient prosecutions of ORC activities.

It’s essential to prevent fraud proactively so that it impacts the financial system. Gen AI excels in detecting fraudulent activity patterns in real-time transactions by https://chat.openai.com/ continuously monitoring financial stats and using encryption techniques. It enables finance businesses to address cybersecurity challenges and enhance data security.

Such capabilities not only streamline the retrieval of information but also significantly elevate client service efficiency. It is a testament to Morgan Stanley’s commitment to embracing Generative AI in banking. A chatbot, unlike an employee, is available 24/7, and customers have become increasingly comfortable using this software program to answer questions and handle many standard banking tasks that previously involved person-to-person interaction. False positives, commonly referred to as “false declines,” occur when businesses or financial institutions incorrectly reject requests for lawful financial transactions. It assesses a customer’s ability to pay and how likely they are to make plans to pay off debt.

A generative AI bot trained on proprietary knowledge such as policies, research, and customer interaction could provide always-on, deep technical support. Today, frontline spending is dedicated mostly to validating offers and interacting with clients, but giving frontline workers access to data as well could improve the customer experience. The technology could also monitor industries and clients and send alerts on semantic queries from public sources.

These systems are more than capable of analyzing and detecting unusual patterns that may indicate fraudulent activity. Machine learning models can learn from historical fraud data to predict and prevent future occurrences. Examining trends and flagging suspicious behavior, AI performs the role of an alert guard in securing financial transactions. Think of a volatile financial market, with AIs—instead of humans—at the height of affairs, managing trades and data analysis. AI in finance has already started to disrupt the sector, heralded for its ability to transform various operations from fraud detection to customer personalization and beyond.

It should be noted, however, that the risk of discrimination and unfair bias exists equally in traditional, manual credit rating mechanisms, where the human parameter could allow for conscious or unconscious biases. The use of the term AI in this note includes AI and its applications through ML models and the use of big data. As AI technology continues to evolve, its capacity to handle more sophisticated tasks is expected to grow, further transforming the landscape of the financial industry. The introduction of AI-driven automation into financial workflows results in a more agile and responsive environment. Employees are relieved from mundane tasks, leading to higher job satisfaction and productivity. Financial markets are rapidly utilising ML and AI technologies to make use of current data to identify trends and more accurately forecast impending threats.

Third, generative AI’s natural-language translation capabilities can optimize the integration and migration of legacy frameworks. Last, the tools can review code to identify defects and inefficiencies in computing. Our analysis of the potential use of generative AI in marketing doesn’t account for knock-on effects beyond the direct impacts on productivity. Generative AI–enabled synthesis could provide higher-quality data insights, leading to new ideas for marketing campaigns and better-targeted customer segments. Marketing functions could shift resources to producing higher-quality content for owned channels, potentially reducing spending on external channels and agencies. Generative AI has taken hold rapidly in marketing and sales functions, in which text-based communications and personalization at scale are driving forces.

Credit Scoring and Risk Management

Banks also need to evaluate their talent acquisition strategies regularly, to align with changing priorities. They should approach skill-based hiring, resource allocation, and upskilling programs comprehensively; many roles will need skills in AI, cloud engineering, data engineering, and other areas. Clear career development and advancement opportunities—and work that has meaning and value—matter a lot to the average tech practitioner. Finally, scaling up gen AI has unique talent-related challenges, whose magnitude will depend greatly on a bank’s talent base.

With iterative development, identifies issues that are addressed effectively by the team before it’s launched for the customers. Routine tasks such as data collection, updated data entry, book and amount reconciliation, and transaction classification in finance business accounting are time-consuming and mundane. Using Gen AI in finance, accounting-related tasks are automated without human intervention, reducing mistakes and ensuring financial accuracy in bookkeeping. For example, Bloomberg announced its finance fine-tuned generative model BloombergGPT, which is capable of making sentiment analysis, news classification and some other financial tasks, successfully passing the benchmarks.

For example, generative AI’s ability to personalize offerings could optimize marketing and sales activities already handled by existing AI solutions. Similarly, generative AI tools excel at data management and could support existing AI-driven pricing tools. Applying generative AI to such activities could be a step toward integrating applications across a full enterprise. By 2030, the adoption of AI in the financial services sector is expected to add $1.2 trillion in value, according to a report by McKinsey & Company.

If you’re looking forward to integrating conversational AI in your financial service or institution, request a demo with App0. Its AI-powered messaging solution integrates communication across multiple channels, thus streamlining workflows and fostering meaningful connections. As a result, generative AI is likely to have the biggest impact on knowledge work, particularly activities involving decision making and collaboration, which previously had the lowest potential for automation (Exhibit 10). Our estimate of the technical potential to automate the application of expertise jumped 34 percentage points, while the potential to automate management and develop talent increased from 16 percent in 2017 to 49 percent in 2023. The technical potential curve is quite steep because of the acceleration in generative AI’s natural-language capabilities.

However, it is crucial to recognize that we are currently deep in the hype cycle surrounding generative AI. Without understanding the limitations and potential consequences of using this technology, a company can quickly run their operations amuck if no training or vetting is put in place. Given this context, industry leaders must redirect their attention towards pinpointing the specific areas where this state-of-the-art technology can genuinely provide substantial commercial value to their businesses in the present. Generative AI systems in financial services can be vulnerable to cybersecurity threats, as they rely on large amounts of data that could be susceptible to hackers and malicious actors. Breaches in the security of these systems can lead to unauthorized access to sensitive financial information, financial fraud, and other cybersecurity risks. Robust cybersecurity measures and constant monitoring are necessary to protect their integrity.

AI helps us identify patterns and trends that might not be visible to human analysts. Whether it’s deciding which markets to invest in or identifying potential fraud, AI in finance supports our decision-making processes with a level of precision that significantly mitigates risk. As the IMF’s Gita Gopinath has noted, “AI must be guided as tools that can enhance, rather than undermine, human potential and ingenuity.” AI is expected to serve as a vehicle for customer-centric services in the finance industry. The financial industry is heavily regulated and customer-centric, and all the algorithmic decisions must be fully understood and approved by the institution. These AI-enabled toolkits look for outliers that demonstrate data bias and remove them from  the data flow. It’s also helpful to generate synthetic data by analysing clustered data points to increase the efficiency of the models involved.

This enables us to estimate how the current capabilities of generative AI could affect labor productivity across all work currently done by the global workforce. However, the real holy grail in banking will be using generative AI to radically reduce the cost of programming while dramatically improving the speed of development, testing and documenting code. Imagine if you could read the COBOL code inside of an old mainframe and quickly analyze, optimize and recompile it for a next-gen core. Uses like this could have a significant impact on bank expenses, as around 10% of the cost base of a bank today is related to technology, of which a sizable chunk goes into maintaining legacy applications and code.

Generative AI can be used for fraud detection in finance by generating synthetic examples of fraudulent transactions or activities. These generated examples can help train and augment machine learning algorithms to recognize and differentiate between legitimate and fraudulent patterns in financial generative ai finance use cases data. It excels in finding answers in large corpuses of data, summarizing them, and assisting customer agents or supporting existing AI chatbots. For example, in this video, we explore how gen AI can speed up credit card fraud resolution — a win-win for customers and customer service agents.

Banks with fewer AI experts on staff will need to enhance their capabilities through some mix of training and recruiting—not a small task. Connect with reliable AI services to prioritize AI goals and implement them strategically to push the boundaries with what’s feasible. The finance solution powered by Gen AI stays abreast with evolving finance trends and technological advancements and is continuously monitored.

In the financial services industry, new regulations emerge every year globally while existing rules change frequently, requiring a vast amount of manual or repetitive work to interpret new requirements and ensure compliance. Developers need to quickly understand the underlying regulatory or business change that will require them to change code, assist in automating and cross-checking coding changes against a code repository, and provide documentation. In this highly competitive financial sector, offering an individualized customer experience becomes essential if banks want to stand out. Generative AI plays a big role in helping finance professionals deliver personalized financial advice and tailor investment portfolios.

A conditional generative adversarial network (GAN), a generative AI variant, was used to generate user-friendly denial explanations. By organizing denial reasons hierarchically from simple to complex, two-level conditioning is employed to generate more understandable explanations for applicants (Figure 3). Financial services leaders are no longer just experimenting with gen AI, they are already way building and rolling out their most innovative ideas. For example, gen AI can help bank analysts accelerate report generation by researching and summarizing thousands of economic data or other statistics from around the globe. It can also help corporate bankers prepare for customer meetings by creating comprehensive and intuitive pitch books and other presentation materials that drive engaging conversations. Picking a single use case that solves a specific business problem is a great place to start.

Specialized transformer models help finance units in automating functions such as auditing, accounts payable including invoice capture and processing. With deep learning functions, GPT models specialized in accounting can achieve high rates of automation in most accounting tasks. First and foremost, gen AI represents a massive productivity and operational efficiency boost. Especially in financial services, where every service or product starts with a contract, terms of service, or other agreement.

However, depending on what type of data users input into the platform it can also risk exposing proprietary or sensitive data,” said Karl Triebes, Chief Product Officer at Forcepoint. With AlphaSense’s genAI technology, you can easily stay on top of more competitor earnings calls by quickly identifying the topics or content most relevant to your search. Our Q&A summaries make it simple to quickly spot trends in Chat GPT what questions are being asked and how competitors are responding—eliminating the useless fluff simultaneously. Often, inefficiencies in the due diligence process stem from challenges with leveraging past deal details siloed in CRMs, network drives, deal rooms, etc. Regardless of where this information is sourced or exists within your company’s intelligence base, this information silo impacts deal velocity.

To streamline processes, generative AI could automate key functions such as customer service, marketing and sales, and inventory and supply chain management. Technology has played an essential role in the retail and CPG industries for decades. Traditional AI and advanced analytics solutions have helped companies manage vast pools of data across large numbers of SKUs, expansive supply chain and warehousing networks, and complex product categories such as consumables. In addition, the industries are heavily customer facing, which offers opportunities for generative AI to complement previously existing artificial intelligence.

Treating computer languages as just another language opens new possibilities for software engineering. Software engineers can use generative AI in pair programming and to do augmented coding and train LLMs to develop applications that generate code when given a natural-language prompt describing what that code should do. We then estimated the potential annual value of these generative AI use cases if they were adopted across the entire economy.

Vigilance in compliance is key to preserving customer trust and guaranteeing the integrity of apps. The success is largely due to the advancements in artificial intelligence technology. It enabled swift responses to evolving fraudulent tactics and enhanced customer protection. The approach goes beyond traditional credit scoring, analyzing a broad spectrum of data points including transaction history, spending patterns, and even social data.

A May 2023 survey of around 75 CFOs at large organizations found that almost a quarter (22 percent) were actively investigating uses for gen AI within finance, while another 4 percent were pursuing pilots of the technology. Now that we know what business value the technology proposes, it’s time to move on to discussing the strategies to manage the challenges we identified initially. At Master of Code Global, as one of the leaders in Generative AI development solutions, we have extensive expertise in deploying such projects. AI frees up professionals to concentrate on more strategic initiatives that require critical thinking and analysis. It also leads to faster turnaround times, boosted performance across operations, and a profound understanding of complex financial details.

Our previously modeled adoption scenarios suggested that 50 percent of time spent on 2016 work activities would be automated sometime between 2035 and 2070, with a midpoint scenario around 2053. The modeled scenarios create a time range for the potential pace of automating current work activities. The “earliest” scenario flexes all parameters to the extremes of plausible assumptions, resulting in faster automation development and adoption, and the “latest” scenario flexes all parameters in the opposite direction. Banks have started to grasp the potential of generative AI in their front lines and in their software activities. Early adopters are harnessing solutions such as ChatGPT as well as industry-specific solutions, primarily for software and knowledge applications.

• With a conversational AI, the customer must enter his needs through voice or text commands.
• AI can help optimize contributions to a Roth account, considering factors like current income, tax implications, and long-term financial goals.
• By processing immense datasets, these algorithms can identify patterns and signals that might go unnoticed by human analysts.
• Get stock recommendations, portfolio guidance, and more from The Motley Fool’s premium services.
• Traditional trading strategies typically rely on technical and fundamental analysis, which can be time-consuming and limited in their ability to adapt to rapidly changing market conditions.

That kind of information won’t be easily available in the usual AI chatbots or knowledge libraries. As mentioned, generative AI relies on large, high-quality datasets to perform effectively. However, real financial data can be costly to obtain, fragmented across institutions, and restricted by privacy regulations, limiting the data available for training GenAI models.

We introduced a UI-driven exception management system and automated AI-driven responses for invalid documents. Gen AI is modernizing workflows tailored for banking systems, generating reference architectures like Terraform, and crafting detailed plans. It is powered by updated artificial intelligence technology, so it is not dependent upon predefined scripts and decision trees like traditional chatbots. Conversational AI in banking is an example of implementing AI technology in the industry. In this blog post, we will delve deeper into the use cases of conversational AI in banking, along with some real-life examples of its implementation. Call centers are regularly under pressure to clear backlogs while offering assistance continuously.

Next, we will navigate through various applications of Gen AI, demonstrating how these advantages manifest in real-world scenarios. That’s why professionals are trusting platforms like AlphaSense to deliver the research results they need while ensuring the privacy and security of their data. By submitting, you agree that KPMG LLP may process any personal information you provide pursuant to KPMG LLP’s Privacy Statement. © 2024 KPMG LLP, a Delaware limited liability partnership and a member firm of the KPMG global organization of independent member firms affiliated with KPMG International Limited, a private English company limited by guarantee.

Creating financial reports is quite a task as it involves collecting data from diverse sources and presenting them in a particular format. Gen AI makes it effortless by analyzing data collected from financial institutions, investors, and regulatory bodies. Hire AI developers to enable gen AI-powered financial report generation that is accurate and produced in less time. Moreover, generative AI models can be used to generate customized financial reports or visualizations tailored to specific user needs, making them even more valuable for businesses and financial professionals. In this article, we explain top generative AI finance use cases by providing real life examples. These examples illustrate how generative artificial intelligence is revolutionizing the field by automating routine tasks and analyzing historical finance data.

The use of generative AI solutions in financial services raises governance and regulatory compliance challenges. Institutions need to ensure that their actions comply with industry regulations and guidelines. This includes considerations such as transparency, explainability, and fairness in the decision-making processes of generative AI systems. Adhering to governance and regulatory requirements is crucial to maintain trust and mitigate potential legal and reputational risks. Individuals often seek customized financial advice based on economic trends and market conditions.

By leveraging its understanding of human language patterns and its ability to generate coherent, contextually relevant responses, generative AI can provide accurate and detailed answers to financial questions posed by users. In capital markets, gen AI tools can serve as research assistants for investment analysts. Sometimes, customers need help finding answers to a specific problem that’s unique and isn’t pre-programmed in existing AI chatbots or available in the knowledge libraries that customer support agents can use.

Biased data can perpetuate historical inequalities and lead to discriminatory practices. Let’s delve into grasping the holistic and strategic approach required for integrating Generative AI in financial services. Through a comprehensive understanding of systemic methodologies and partnering with a reliable development firm, businesses can effectively leverage Generative AI’s transformative potential to drive innovation and achieve their goals. Generative AI is highly advantageous in automating routine accounting tasks such as data entry, reconciliation, and categorization of financial transactions. With the acceleration in technical automation potential that generative AI enables, our scenarios for automation adoption have correspondingly accelerated. These scenarios encompass a wide range of outcomes, given that the pace at which solutions will be developed and adopted will vary based on decisions that will be made on investments, deployment, and regulation, among other factors.

And its performance will compete with the top 25 percent of people completing any and all of these tasks before 2040. The ideal partner not only offers specialized skills but also brings forward-looking strategies. They help maximize the ROI of your Generative AI project, ensuring its alignment with your business’s long-term objectives. Our calculator compares current costs with human agents to those with an AI solution. It considers characteristics like agent salaries, time spent per ticket, and service request volumes.