Generative artificial intelligence in finance OECD Artificial Intelligence Papers

AI assistants provide executives with insights drawn from a vast data pool, including the web and proprietary sources. Moreover, chatbots driven by artificial intelligence app development can significantly improve customer assistance by simplifying or translating complex regulations and contracts. This help is invaluable for clients who may not be familiar with industry-specific jargon or for those who need quick access to precise information without the hassle of sifting through lengthy documentation. We share this view and believe it captures the essence of GenAI’s potential in the BFSI sector. So, in this article, we’ll explore the pivotal applications of generative AI in financial services, organized by these four critical categories, to uncover how they’re reshaping the industry. They use the technology to recognize patterns in historical data to identify root causes of past events or define trends for the future.

Generative AI can be employed by financial institutions to produce synthetic data that adheres to privacy regulations such as GDPR and CCPA. By learning patterns and relationships from real financial data, generative AI models are able to create synthetic datasets that closely resemble the original data while preserving data privacy. According to a 2023 KPMG survey, fraud detection came on top of the list of generative AI applications in finance, with 76% of the respondents saying the technology benefits this cause.

And Bloomberg recently released its BloombergGPT—a large language model that was trained on an enormous financial dataset containing 700 billion tokens. People can use this Gen AI model to search Bloomberg’s financial data and obtain summaries and financial insights. Another application of generative AI in finance is segmenting customers based on their financial status and demographics. Brokerage firms can use this division to produce recommendations tailored to customer groups.

As the field of AI advances, companies face increasingly sophisticated threats such as deepfake videos and voice generation scams. This is particularly challenging for businesses in the BFSI sector, where it is crucial to act quickly and decisively to protect customer trust and maintain security. Moreover, deploying internal AI in fintech rapidly delivers tangible benefits, including heightened efficiency and significant cost reductions in internal processes.

PayPal has announced new AI tools to streamline the checkout process, offer personalised cash-back deals, and strengthen fraud prevention. These tools use machine learning and graph technologies to analyse consumer data and merchant information, effectively enhancing payment authorisation rates and combating payment fraud. At NorthBay, we’re laser-focused on helping organizations leverage AWS AI services – including generative AI for maximum value. To learn more about offerings and successful financial services customer engagements. According to the KPMG survey of US executives, around 60% of the respondents mentioned they would need at least a year to implement their first Gen AI solution.

Gen AI can explain old code and frameworks, highlighting potential pitfalls and suggesting improvements. This empowers developers to make informed decisions when working with legacy code, leading to enhanced maintainability and more security. This helps reduce technical debt and enhances the overall performance and stability of the software systems. Automating repetitive tasks and suggesting best practices enables developers to focus on more critical aspects of their work. We believe that GenAI will have a significant impact on productivity in the areas of general communication, customer satisfaction and dealing with technical debt. To fully realise the potential and value of Gen AI, we see the need for financial institutions to upskill their organisations.

Generative AI Use Cases in Financial Services

The same holds for generative artificial intelligence (Gen AI), the deep-learning technology that can generate human-like text, images, videos, and audio, and even synthesize data for training other AI models. Formerly limited to physical establishments, banking has morphed into a completely digital realm, due in no small part to generative AI. However, implementing generative AI in fraud detection also comes with its challenges. Therefore, banks need to ensure that they have access to clean and reliable data to train their neural networks effectively.

Generative AI models, when fine-tuned properly, can generate various scenarios by simulating market conditions, macroeconomic factors, and other variables, providing valuable insights into potential risks and opportunities. By learning from historical financial data, generative AI models can capture complex patterns and relationships in the data, enabling them to make predictive analytics about future trends, asset prices, and economic indicators. In banking, generative AI offers many benefits, from enhancing customer interactions to revolutionising operational efficiency.

The banking industry was highlighted as among sectors that could see the biggest impact (as a percentage of their revenues) from generative AI. The technology “could deliver value equal to an additional $200 billion to $340 billion annually if the use cases were fully implemented,” says the report. As discussed in the previous section, the risk of overreliance on Gen AI and the trade-off between automation and human expertise is crucial. Quality control and code review should be done by other developers, and automated code review tools should be in the pipeline. Generative AI, depending on its complexity and the available computational power, may not always meet these high-performance demands. In times of high volatility or heavy transaction volumes, AI might slow down, causing delays and potentially significant financial losses.

Opportunities for AI in finance and accounting

In the context of conversational finance, generative AI models can be used to produce more natural and contextually relevant responses, as they are trained to understand and generate human-like language patterns. As a result, generative AI can significantly enhance the performance and user experience of financial conversational AI systems by providing more accurate, engaging, and nuanced interactions with users. For instance, Morgan Stanley employs OpenAI-powered chatbots to support financial advisors by utilizing the company’s internal collection of research and data as a knowledge resource.

Innovations in machine learning and the cloud, coupled with the viral popularity of publicly released applications, have propelled Generative AI into the zeitgeist. Generative AI is part of the new class of AI technologies that are underpinned by what is called a foundation model or large language model. These large language models are pre-trained on vast amounts of data and computation to perform what is called a prediction task.

Leading institutions such as Morgan Stanley, JPMorgan Chase & Co., Goldman Sachs, Broadridge, and Fidelity Investments are spearheading this wave of innovation. This capability not only simplifies the document preparation process but also diminishes the risk of human errors. Marketing in the finance sector is complex, aiming to sell financial products and services, connect with customers, and build brand loyalty in a highly competitive field – all at the same time. Companies need to deeply understand customer needs, navigate strict regulations, and innovate to stand out. The BFSI sector is characterized by the management and analysis of a vast amount of text-based documents. Many of its internal operations and client-facing tasks demand the sophisticated handling of natural language, an area where large language models and the broader spectrum of Generative AI excel.

For example, a conventional artificial intelligence model can tell you if an object in an image is a cat; a Gen AI model can generate a picture of a cat based on its knowledge base of other cat images. As AI becomes more integrated into financial institutions, there is a need to balance existing roles with new responsibilities. As the knowledge, familiarity and capability to interact with Gen AI tools increase, your organisation must consider what structural elements must be introduced to foster and govern the growth of Gen AI capabilities and threats. Due to the growth in misinformation, we see increased costs and resources needed to handle regulatory pressure and attack surface expansion. We believe we will see a new set of corporate leaders with specialised responsibilities and roles, such as Chief Data Officer and Chief Generative AI Officer. Financial institutions must define these roles and ensure they have the authority and resources to fulfil their responsibilities effectively.

A generative AI assistant that can hold a conversation with clients and can provide high-level guidance would reduce the routine servicing burden on insurance agents, financial advisors, and plan administrators. Expect more bank, brokerage and card firms to launch client-facing generative AI assistants in 2024. By the end of the year, these sectors will go from a handful of examples to more widespread adoption, creating strong competitive pressure for laggards to respond with their own generative AI assistant. AI, while not a panacea, is a valuable tool that necessitates judicious and responsible deployment, particularly within the fintech services and banking sectors. This article has highlighted several areas where AI is currently being used safely, delivering tangible benefits such as cost reductions and enhanced operational efficiencies.

By analyzing customer data and preferences, banks can generate personalized offers and promotions that are tailored to individual needs. By automating processes and analyzing large amounts of data, generative AI can significantly improve efficiency in banking operations. Tasks that were previously time-consuming and manual can now be automated, freeing up resources and reducing human error. This allows banks to streamline their operations and focus on more strategic initiatives.

Moreover, CBA’s AI model helps identify digital payment transactions containing harassing or offensive messages, aiding in preventing financial abuse. Wells Fargo leads the way in utilising generative AI through its virtual assistant app, Fargo. With over 20 million interactions since its launch in March 2023, Fargo, powered by Google’s PaLM 2 language model, assists customers with everyday banking tasks such as bill payments and fund transfers. Wells Fargo also employs open-source large language models (LLMs) for internal applications, including Meta’s Llama 2 model. Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee (“DTTL”), its network of member firms, and their related entities.

This automation not only streamlines the reporting process and reduces manual effort, but it also ensures consistency, accuracy, and timely delivery of reports. 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).

We’ll then discuss the “when” question in more detail and a possible timeline for when different financial services industries will start offering client-facing generative AI assistants. Many of the largest financial services firms have announced that they are working on internal and/or client-facing generative AI initiatives. As of February 2024, however, there have been only a limited number of financial services firms that have actually deployed a live ChatGPT-like generative AI assistant to support their client experience. Financial institutions’ mid-office, which plays a crucial role in managing risks, ensuring compliance, and processing transactions, are undergoing a transformational shift through automation.

The Importance of Ethical Considerations in Generative AI

These most promising generative AI use cases in banking, with some real-life examples, demonstrate the potential value arising from the technology. Generative AI works by using two neural networks — the generator and the discriminator — that compete against each other in a game-like setting. The generator’s role is to create new content, such as images or text, while the discriminator’s role is to distinguish between real and generated content. Through a process of trial and error, both networks improve their performance over time.

The 125 billion or so transactions that pass through the company’s card network annually provide the training data for the model. 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 data.

Now, every tax consultant has access to a ChatGPT tool residing within KPMG’s firewall. You can foun additiona information about ai customer service and artificial intelligence and NLP. The consultancy wants to incorporate ChatGPT into other products and services and expects as much as $12 billion in revenue from these initiatives. © 2024 KPMG LLP, a Delaware limited liability partnership and a member firm of the KPMG global organization of independent https://chat.openai.com/ member firms affiliated with KPMG International Limited, a private English company limited by guarantee. 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.

Generative AI Finance Use Cases in 2024

The integration of generative AI solutions into banking operations requires strategic planning and consideration. Each successive FinTech innovation that came along incrementally transformed banking across its multiple functions, one by one, until generative AI entered the scene to drastically reinvent the entire industry. Ethical considerations are particularly important in banking due to the sensitive nature Chat PG of financial transactions and customer information. Banks need to ensure that they have robust ethical frameworks in place to guide the use of generative AI and protect customer privacy. Explore more on how generative AI can contribute to software development and reduce technology costs, helping software maintenance. They will give feedback that engineers can use to refine the tool in further iterations.

Globally, institutions foresee a 5 to 10 year timeline for full automation harnessing, strategically investing in areas with immediate benefits, such as customer service and cost reduction. The average financial services chatbot struggles to explain financial concepts, cannot assist with financial planning and budgeting, and does not provide advice or help with investing. The industry’s chatbots are primarily designed to handle relatively straightforward customer support needs, and are not advanced enough to serve as a true assistant or advisor.

These immediate gains streamline operations and strengthen the organization’s competitive edge in the digital era. These AI-enhanced models aggregate and analyze data from specialized sources, offering dynamic, data-driven responses crucial for adapting to market changes. Chatbots powered by generative AI in financial services are revolutionizing accessibility to financial services, making them more efficient and inclusive. Let’s first understand the “4 C’s” value proposition framework proposed by McKinsey before we dive into specific use cases of Generative AI in financial services. This includes a clear management vision and strategy, commitment to resources, alignment of data and technology with the operating model, robust risk management, and effective change management.

Artificial Intelligence app development might be a real game-changer here, offering customization, efficiency, and deep insights that can transform traditional marketing into strategies that really focus on the customer. Generative AI in financial services is also redefining content creation, making it faster, more personalized, and incredibly efficient. There has never been a better time to seize the chance and gain a competitive edge while large-scale deployments remain nascent. To address these issues, it’s critical to integrate human expertise into Gen AI’s decision-making processes every step of the way. Such a human-in-the-loop approach is a sure-fire way to detect the model’s anomalies before they can impact the decision. Using generative AI to produce initial responses as a starting point and creating feedback loops can help the model reach 100% accuracy.

For example, Fujitsu and Hokuhoku Financial Group have launched joint trials to explore promising use cases for generative AI in banking operations. The companies envision using the technology to generate responses to internal inquiries, create and check various business documents, and build programs. However, implementing generative AI in banking comes with its challenges, including technical challenges, data privacy and security concerns, and ethical considerations. Banks need to invest in advanced technology infrastructure, implement robust data privacy and security measures, and have ethical guidelines in place to address these challenges effectively.

Each category has unique benefits and applications that can help enhance productivity and innovation. Banks want to save themselves from relying on archaic software and have ongoing efforts to modernize their software. Enterprise GenAI models can convert code from old software languages to modern ones and developers can validate the new software saving significant time. Visa actively engages in generative AI initiatives, offering practical insights and recommendations through its AI Advisory Practice. The company has allocated $100 million to foster innovation in generative AI in payments and commerce, emphasising its commitment to transformative technologies in the future of finance. From the real-life examples presented in this article, you can see that generative AI is a valuable tool for the financial sector.

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. If your focus is just banking, a subset of these use cases are listed in generative AI use cases in banking. Generative AI brings numerous benefits to the financial sector, from improving customer service to enhancing fraud detection. As adoption increases, financial organisations may face challenges, but the potential for transformative change is significant.

• AI, specifically Gen AI, has the potential to revolutionise communication in financial institutions, leading to improved customer satisfaction and increased business productivity.
• This matters because the financial services sector currently offers only very basic chatbot assistants running on outdated technology.
• The classic AI is mostly used for classification and prediction tasks, while Gen AI can deliver original content that looks like human creation.
• Synthesia’s new technology is impressive but raises big questions about a world where we increasingly can’t tell what’s real.
• It reached 100 million monthly active users in just two months after launch, surpassing even TikTok and Instagram in adoption speed, becoming the fastest-growing consumer application in history.

By integrating these advanced AI capabilities, BFSI companies can improve their ability to proactively identify and mitigate threats, ensuring a safer environment for their customers and operations. Generative AI in financial services can help companies identify and prioritize potential new customers by analyzing both public and private data, making marketing efforts more focused and effective. Virtual assistants can give personalized investment advice and suggest strategies, including tax optimization, to improve returns.

It can help articulate non-standard terms, compare contract conditions, produce summaries, and generate arguments for negotiating favorable terms. If you look at just a few of the Generative AI applications this model renders, it also becomes apparent why it has captivated the attention of both society and the business world across the spectrum of industries. Synthesia’s new technology is impressive but raises big questions about a world where we increasingly can’t tell what’s real.

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. 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. 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.

Despite these challenges, the game-changing potential of generative AI in banking cannot be ignored. As technology continues to advance, so does the potential for generative AI to transform the banking industry. By embracing generative AI, banks can stay ahead of the competition, improve customer experience, and drive innovation in the financial sector. Generative AI offers several benefits to the banking industry, including improved efficiency, enhanced customer experience, better fraud detection and prevention, and cost reduction. Since customer information is proprietary data for finance teams, it introduces some problems in terms of its use and regulation.

By leveraging generative AI, banks can automate processes, analyze large amounts of data, and make more informed decisions. Generative AI is a class of AI models that can generate new data by learning patterns from existing data, and generate human-like text based on the input provided. This capability is critical for finance professionals as it leverages the underlying training data to make a significant leap forward in areas like financial reporting and business unit leadership reports. The advent of generative AI in the banking industry is not about technology evolution—generative artificial intelligence is set to redefine the very essence of banking by shaping entirely new business models. The impact Gen AI has on the banking sector is immense across literally all banking functions, especially in terms of banking operations and decision-making. Another advantage of generative AI in banking is its ability to enhance fraud detection and prevention measures.

However, this technology isn’t without its drawbacks, especially in a sector as crucial and sensitive as finance. Here’s a closer look at the significant risks and disadvantages of deploying generative AI in financial services. Generative AI in financial services is a key driver of digital growth within organizations, primarily by optimizing internal processes such as IT support and human resources management. This technology enhances overall operational efficiency, ensuring smoother and more effective company-wide functions.

EY refers to the global organization, and may refer to one or more, of the member firms of Ernst & Young Global Limited, each of which is a separate legal entity. Ernst & Young Global Limited, a UK company limited by guarantee, does not provide services to clients. It is a matter of when, not “if,” and 2024 is shaping up to be the year generative AI arrives in financial services.

Despite some banks hesitating to adopt this technology, numerous success stories worldwide highlight its potential impact. Wide-scale adoption is slow because of the sensitive nature of financial institutions’ operations, data privacy, and the organizations’ fiduciary duty to protect customers from misinformation and deceptive output. The pioneering approach optimizes intricate financial strategies and decision-making processes, enhancing efficiency, accuracy, and adaptability in the dynamic world of finance.

One of the main ethical issues in generative AI is the creation of deepfakes, which are manipulated videos or images that appear real but are actually synthetic. Banks need to have ethical guidelines in place to prevent the creation and dissemination of deepfakes. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue generative ai in finance and a 9 digit valuation from 0 within 2 years. Cem’s work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month. You will also need to train your internal staff, who will work with generative AI-infused processes.

As a highly experienced generative AI company, ITRex can help you define the opportunities within your business and the sector for generative AI adoption. Think about modern infrastructure and systems capable of supporting Gen AI technologies. A good option would be hybrid infrastructure, which allows banks to work with private models for sensitive data while also leveraging the public cloud capabilities. The staff had reported a 50% increase in productivity rate during the trial period. So let us elaborate on how the traditional banking experience can be transformed into a highly differentiated, secure, and efficient service by the convergence of generative AI and banking.

On the downside, the customization options are limited, and your critical tasks are at the vendor’s mercy. Need more information on what makes Gen AI a revolutionary technology and how it can augment your processes? We’ve written an eBook that helps forward-thinking business leaders identify opportunities and proceed with implementation. Whether you are a seasoned executive or an emerging entrepreneur, this eBook, Generative AI for Business Leaders, will enable you to streamline operations and drive innovation. JPMorgan is developing its own Gen AI bot, IndexGPT, which will give customized investment advice by analyzing financial data and selecting securities tailored to individual customers and their risk tolerance. The classic AI is mostly used for classification and prediction tasks, while Gen AI can deliver original content that looks like human creation.

• Around 61% anticipate a profound impact on the value chain, enhancing efficiency and responsiveness.
• With generative AI for finance at the forefront, this new AI technology guides the path towards strategic integration while addressing the accompanying challenges, ultimately driving transformative growth.
• This unawareness can specifically affect finance processes and the overall finance function.
• It utilizes a powerful Retrieval-Augmented Generation architecture to turn large language models into potent business tools.
• BondGPT helps asset managers, hedge fund managers, and dealers to accelerate their bond selection and portfolio construction activities.
• Financial generative AI can learn to draft financial reports, such as financial statements, budget, risk, and compliance reports.

In addition to improving the model, this collaboration will increase AI acceptance in your company. After retraining a Gen AI model or deploying a ready-made solution as is, assess the tool for fairness and conduct regular audits to ensure the model’s outcome remains bias-free as it gains access to new datasets. Also, validate if the model can infer protected attributes or commit any other privacy violations. This opens the possibility for customization and superb performance, but you need to aggregate and clean the training dataset and supply a server that can handle the load. Check out our recent article on generative AI in banking if you are eager to explore more specialized banking applications.

Sentiment analysis, an approach within NLP, categorizes texts, images, or videos according to their emotional tone as negative, positive, or neutral. By gaining insights into customers’ emotions and opinions, companies can devise strategies to enhance their services or products based on these findings. 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. McKinsey predicts that generative AI could add $200–340 billion in annual value to the banking sector, which would mostly come from productivity increases. The consultancy says that Gen AI will change the way customers interact with financial institutions and how everyday tasks are approached.

For example, the technology can’t discover an early trend, devise a strategy on how to use it to a company’s advantage, and execute the strategy autonomously. Or craft a personalized customer investment portfolio and put it to action automatically without human verification. The Financial Services sector has undergone substantial digital transformation in the past two decades, enhancing convenience, efficiency, and security. Gen AI is now catalyzing a significant shift, with 78% of surveyed financial institutions implementing or planning Gen AI integration. Around 61% anticipate a profound impact on the value chain, enhancing efficiency and responsiveness.