Generative AI in banking and financial services

In the past year, organizations using AI most often hired data engineers, machine learning engineers, and Al data scientists—all roles that respondents commonly reported hiring in the previous survey. But a much smaller share of respondents report hiring AI-related-software engineers—the most-hired role last year—than in the previous survey (28 percent in the latest survey, down from 39 percent). Roles in prompt engineering have recently emerged, as the need for that skill set rises alongside gen AI adoption, with 7 percent of respondents whose organizations have adopted AI reporting those hires in the past year.

In 2023 alone, IBM Consulting has interacted with more than 100 clients and completed dozens of engagements infusing generative AI alongside classical machine learning AI strategies. Explore more posts in this blog series, The Future of Finance with Generative AI, to learn more about how to streamline and enhance critical F&A functions and improve your finance operation’s efficiency with generative AI. This involves not only tailoring the technology to enhance current processes without major disruption but also preparing teams for the change. Take, for instance, a supplier in the manufacturing industry leveraging generative AI to optimize its invoicing and collections processes.

You can start implementing these use cases using Google Cloud’s Vertex AI Search and Conversation as their core component. With Vertex AI Search and Conversation, even early career developers can rapidly build and deploy chatbots and search applications in minutes. For example, today, developers need to make a wide range of coding changes to meet Basel III international banking regulation requirements that include thousands of pages of documents. Gen AI could summarize a relevant area of Basel III to help a developer understand the context, identify the parts of the framework that require changes in code, and cross check the code with a Basel III coding repository.

Much of gen AI’s near-term value is closely tied to its ability to help people do their current jobs better. This means companies should be focusing on where copilot technology can have the biggest impact on their priority programs. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI’s enormous potential value is harder than expected. Voya Investment Management already has implemented a virtual analyst that can monitor stocks for potential risks, complementing the $331bn manager’s human research staff. Portfolio managers have access to a dashboard in which a human analyst’s review of securities can be viewed alongside AI feedback, such as red-flagging a stock.

But, the adoption of generative AI in finance functions entails challenges, including accuracy and data security and privacy. To overcome the obstacles and stay ahead of the adoption curve, now is the time for CFOs to learn about the applications of generative AI in finance functions that will have the most impact and prepare to capitalize on emerging capabilities. And finally, banks should invest in hiring new talent and training current employees to spot, stop, and report AI-assisted fraud. For many banks, these investments will be expensive and difficult; they’re coming at a time when some bank leaders are prioritizing managing costs. Banks can also focus on developing new fraud detection software using internal engineering teams, third-party vendors, and contract employees, which can help foster a culture of continuous learning and adaptation. For teams developing gen AI solutions, squad composition will be similar to AI teams but with data engineers and data scientists with gen AI experience and more contributors from risk management, compliance, and legal functions.

With this knowledge, the models can predict word sequences sequentially, one word at a time. Publicly available GenAI systems pose significant privacy challenges for financial institutions wishing to incorporate their capabilities into their operations. Several GenAI systems often explicitly state that they cannot ensure the security and confidentiality of the information and data provided by users. AI/ML systems raise several well-known privacy concerns, and they must be addressed when AI/ML is used in the highly regulated financial sector. These concerns are at the heart of ongoing efforts to improve AI/ML privacy and update the legal and regulatory framework that requires AI/ML systems and related data sources to adhere to enhanced privacy standards.

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Generative AI can be used to process, summarize, and extract valuable information from large volumes of financial documents, such as annual reports, financial statements, and earnings calls, facilitating more efficient analysis and decision-making. The promise of generative AI in F&A is grand, as indicated in a recent IBM study that found that executives expect that 48% of the staff across their organizations (including 34% of finance staff) will use generative AI to augment their daily tasks in the next year. The GLUE (General Language Understanding Evaluation) benchmark is a set of nine task databases designed to evaluate and score a model’s language understanding. The essence of prompt engineering lies in the deliberate configuration of input structure and content, which is orchestrated to guide and shape the model’s output qualitatively. The merits of prompt engineering are manifold—notably, enhancing the precision of the generated text, exercising some degree of control over the output, and, crucially, mitigating inherent bias.

The survey found that generative AI is perceived as the key to unlocking competitiveness. 57% of BFM CEOs surveyed stated that gaining a competitive advantage in the sector will depend on who has the most advanced generative AI. Customer lifetime value and employee experience are among the modern KPIs that are increasingly shared organization-wide, he said. Synthesia’s new technology is impressive but raises big questions about a world where we increasingly can’t tell what’s real.

The data sets themselves first need to be rigorously processed and curated, just as data scientists prepare data lakes for advanced analytics and analytical AI. Artificial intelligence (AI) has enormous transformative power and holds profound implications for the world’s societies and economies. Generative artificial intelligence (AI) applications like ChatGPT have captured the headlines and imagination of the public. Generative AI is a type of AI that can create new content and ideas, including conversations, stories, images, videos, and music.

All of this is backed by IBM’s long-standing commitment to trust, transparency, responsibility, inclusivity and service. GenAI explainability will be a challenge for the financial sector’s GenAI adoption. Research is ongoing to develop solutions that could improve GenAI explainability (see, for example, Ullah and others 2020). Indeed, because of the ingestion of the massive data and the complexity of the algorithms and the architecture of LLM, explainability or interpretability in GenAI systems continues to be a challenge for the research community. Some techniques have been proposed recently to provide insight on the outcome of those models, but the result remains unsatisfactory. This problem persists; thus, the adoption of those models in the financial sector requires more scrutiny.

59% of surveyed BFM CEOs stated that cultural change within a business is more important than overcoming technical challenges when becoming a data-driven business, with 65% also believing success with AI will depend more on people’s adoption than the technology itself. As a fine-tuned generative model for finance, it outperformed other models by succeeding in sentiment analysis. 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.

For instance, internal audit functions can be greatly enhanced by generative AI through automated analysis and reporting. 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. IBM Consulting’s F&A practitioners can partner with you as you roll out this technology, sharing valuable insights and best practices along the way.

Accordingly, the proper domain of GenAI is recommendations, advice, or analysis, where human actors should make decisions and assume the responsibility for them. The nuance is that financial institutions need to understand the reasons for their actions, and where these actions are based on outputs generated by GenAI, these institutions should be able to understand the generative process and its limitations. Brendan Maggiore is a senior manager in Deloitte Risk and Financial Advisory, where he has built AI-driven fraud monitoring programs at major financial institutions, technology/fintech organizations, and Fortune 100 companies. He has shaped the views of clients, including CxOs and law firm partners, on leveraging and building trust in AI and machine learning for fraud risks across the customer journey and for a variety of products. Additionally, he is a frequent speaker in the areas of fraud and AI, and has been featured in numerous industry publications. For businesses from every sector, the current challenge is to separate the hype that accompanies any new technology from the real and lasting value it may bring.

practical use cases for the financial services industry

The enhancements will empower finance professionals to make more informed strategic decisions, leading to improved operational efficiency and effectiveness. Brian is the US Audit & Assurance Trustworthy AI leader with diverse experience providing audit and advisory services to Fortune 500 companies. A leader who brings strong technical, risk management, communication, and organizational skills, he focuses on providing audit, accounting, and advisory services to public and private companies in the financial services sector.

(It’s also worth noting that Musk, who cofounded OpenAI but left in 2018, is embroiled in a long-running feud with the company and has even launched a rival AI firm called X.AI). Bedi believes that when we look back on this moment in time, some of the generative AI use cases that businesses pursued will look comically simple. “If you zoom out and fast forward 12 to 18 months, it’s going to become unthinkable for people not to have gen AI infused in their work,” Bedi says. The ascendance of AI helped ServiceNow leap into the Fortune 500 for the first time in 2023. Last month, the company announced a bunch of new generative AI capabilities, including the integration of two generative AI assistants, ServiceNow Now Assist and Microsoft Copilot, that would allow users in Microsoft Teams to ask workplace questions to Now Assist. “We decided to let me spend 100% of my time with customers to make sure they are getting the most out of the platform,” says Bedi, who handed over the CDIO reins to Kellie Romack, ServiceNow’s SVP of digital technology.

Ruben is a Capital Markets Specialist with focus on Data Architecture, Analytics, Machine Learning & AI. Previously Ruben was a Director with UBS Investment Bank and also spent time as a management consultant. Ruben has a Computer Science degree from Brandeis University and an MBA from UC Berkeley. While existing Machine Learning (ML) tools are well suited to predict the marketing or sales offers for specific customer segments based on available parameters, it’s not always easy to quickly operationalize those insights. In capital markets, gen AI tools can serve as research assistants for investment analysts. With such a vast array of applications and customizable capabilities, Generative AI can serve as a powerful tool for finance leaders to address key agenda items and realize strategic priorities and objectives for finance and controllership.

It’s also doing so in an environment where the stakes are higher, and collaboration with customers is needed to alleviate common data/privacy concerns (more on that later). Initially garnering attention for its speculative promise, this technology has rapidly evolved into a powerful force for innovation, seamlessly integrating into various sectors. Financial management and strategic planning stand out as prime examples where generative AI can provide significant advantages to organizations and financial leaders—provided it’s adopted thoughtfully. CFOs typically aren’t software engineers, let alone practiced experts in predictive language models. Their first step should be to try out the technology to get a feel for what it can do—and where its limits are at the moment.

By gaining insights into customers’ emotions and opinions, companies can devise strategies to enhance their services or products based on these findings. These models can simulate different market conditions, economic environments, and events to better understand the potential impacts on portfolio performance. This allows financial professionals to develop and fine-tune their investment strategies, optimize risk-adjusted returns, and make more informed decisions about managing their portfolios. This ultimately leads to improved financial outcomes for their clients or institutions. For more on conversational finance, you can check our article on the use cases of conversational AI in the financial services industry.

The US Consumer Financial Protection Bureau is closely examining and monitoring GenAI’s potential risks to the financial sector, including from bias or misleading information. Calls in the European parliament have been made to augment the proposed “European AI Act” with specific provisions for GenAI. Gen AI certainly has the potential to create significant value for banks and other financial institutions by improving their productivity. But scaling up is always hard, and it’s still unclear how effectively banks will bring gen AI solutions to market and persuade employees and customers to fully embrace them. Only by following a plan that engages all of the relevant hurdles, complications, and opportunities will banks tap the enormous promise of gen AI long into the future.

Operations

The use of synthetic data in the context of AI systems has accelerated in recent years. Synthetic data are algorithm-created with a statistical distribution that mimics real data via deep learning model simulation. Synthetic data are used primarily to train AI/ML and for testing model robustness (Box 2). Synthetic data have emerged as a viable alternative to real data primarily because of their ability to alleviate privacy and confidentiality concerns—coupled with their cost-effectiveness. Nevertheless, the use of synthetic data poses several challenges, notably issues pertaining to data quality along with the potential for replication of inherent real-world biases and gaps in the generated data sets.

• Generative Al’s large language models applied to the financial realm marks a significant leap forward.
• This automation not only streamlines the reporting process and reduces manual effort, but it also ensures consistency, accuracy, and timely delivery of reports.
• Brian is also leading Deloitte’s efforts in the Algo/AI assurance area as emerging technologies continue to impact clients and the marketplace.
• Up until now, it hasn’t been feasible to incorporate this vast amount of data into a single model due to limited computing resources and less complex/low-parameter models.

GenAI raises privacy issues that are similar to those of AI/ML, but it also raises new, unique concerns. This note explores the risks posed by using GenAI systems in the financial sector. These risks include those inherent in the technology (data privacy and embedded bias), those related to its performance (robustness, synthetic data, and explainability), new cybersecurity threats posed by GenAI, and broader risks to financial stability. This note builds on the 2021 IMF Paper that assessed AI/ML risks for the financial sector by examining the characteristics that differentiate GenAI from AI/ML and the new risks that unique aspects may raise (Boukherouaa and Shabsigh 2021). The wide-ranging appeal of GenAI technology combined with its new complex risks will have broad systemic implications for the financial sector.

AI is promoted from back-office duties to investment decisions

GenAI could be susceptible to bias generated by search engine optimization (SEO) tools (see, for example, Atreides 2023). To improve their visibility in internet search engines (for example, Google, Bing, and others), websites use SEO techniques. SEO is primarily used, at present, for marketing products and services or disseminating information. As the use of GenAI applications spreads, SEO tools will very likely be geared toward influencing the training of GenAI models—possibly skewing the models output and introducing new layers of biased data that could be difficult to detect. In this blog, we focus on a handful of generative AI use cases for the financial services industry, how AWS enables customers to quickly build and deploy generative AI applications at scale, and how to get started with generative AI at AWS.

Competitive pressures have fueled rapid adoption of AI/ML in the financial sector in recent years by facilitating gains in efficiency and cost savings, reshaping client interfaces, enhancing forecasting accuracy, and improving risk management and compliance. GenAI could also deliver to cybersecurity benefits ranging from implementing predictive models for faster threat detection to improved incident response. Financial service providers have been quick to explore the capabilities of GenAI and how it can be adapted to a broad range of applications (Box 1).

To do so, they should improve communication with public and private sector stakeholders as well as collaborate with jurisdictions at the regional and international levels. GenAI technologies hold great promise for financial sector applications but should be approached with caution. GenAI could drive significant efficiency, improve customer experience, and strengthen risk management and compliance. However, the intrinsic risks in GenAI could pose material risks for financial sector reputation and soundness—and, ultimately, could undermine public trust.

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. For example, leaders at a wealth management firm recognized the potential for gen AI to change how to deliver advice to clients, and how it could influence the wider industry ecosystem of operating platforms, relationships, partnerships, and economics. As a result, the institution is taking a more adaptive view of where to place its AI bets and how much to invest.

Since its debut in 2011, Apple has pushed to make the voice assistant an integral part of all its operating systems. But ServiceNow has also asked Wall Street for patience before it sees a lift to the top line from the new generative AI products. Even after launching a bunch of new generative AI tools, ServiceNow stuck with a prior 2026 sales outlook of $15 billion in subscription revenue.

He leads the development of our thought leadership initiatives in the industry, coordinating our various research efforts and helping to differentiate Deloitte in the marketplace. The findings offer further evidence that even high performers haven’t mastered best practices regarding AI adoption, such as machine-learning-operations (MLOps) approaches, though they are much more likely than others to do so. Snowflake has said hackers were targeting some of the cloud storage firm’s customers’ accounts and attempting to steal customer data for those that didn’t have multifactor authentication.

Organizations continue to see returns in the business areas in which they are using AI, and

they plan to increase investment in the years ahead. We see a majority of respondents reporting AI-related revenue increases within each business function using AI. And looking ahead, more than two-thirds expect their organizations to increase their AI investment over the next three years. The goal of this specific work is the creation of intelligence systems that allow robots to swap different tools to perform different tasks.

Sentiment analysis

The industry’s already extensive—and growing—use of digital tools makes it particularly likely to be affected by technology advances. This MIT Technology Review Insights report examines the early impact of generative AI within the financial sector, where it is starting to be applied, and the barriers that need to be overcome in the Chat GPT long run for its successful deployment. The learning process can take two to three months to get to a decent level of competence because of the complexities in learning what various LLMs can and can’t do and how best to use them. The coders need to gain experience building software, testing, and validating answers, for example.

• Artificial intelligence (AI) has enormous transformative power and holds profound implications for the world’s societies and economies.
• The second factor is that scaling gen AI complicates an operating dynamic that had been nearly resolved for most financial institutions.
• While developing Lilli, our team had its mind on scale when it created an open plug-in architecture and setting standards for how APIs should function and be built.
• By leveraging AI to automate repetitive tasks and augment human capabilities, businesses can unlock newfound agility and productivity.
• But a much smaller share of respondents report hiring AI-related-software engineers—the most-hired role last year—than in the previous survey (28 percent in the latest survey, down from 39 percent).

To fully understand global markets and risk, investment firms must analyze diverse company filings, transcripts, reports, and complex data in multiple formats, and quickly and effectively query the data to fill their knowledge bases. 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. Foundational models, such as Large Language Models (LLMs), are trained on text or language and have a contextual understanding of human language and conversations. These capabilities can be particularly helpful in speeding up, automating, scaling, and improving the customer service, marketing, sales, and compliance domains. First and foremost, gen AI represents a massive productivity and operational efficiency boost.

Generative artificial intelligence (AI) has captured the imagination and interest of a diverse set of stakeholders, including industry, government, and consumers. For the housing finance system, the transformative potential of generative AI extends beyond technological advancement. Generative AI presents an opportunity to promote a housing finance system that is transparent, fair, equitable, and inclusive and fosters sustainable homeownership.

The tool evaluates transcript statements based on terms from Lee’s prompt including “CAGR,” “growth strategies,” and “investment strategies,” and then summarizes each company’s outlook. Both data and LLM models can save banks and other financial services millions by enhancing automation, efficiency, accuracy, and more. Having been there for over a year, I’ve recently observed a significant increase in LLM use cases across all divisions for task automation and the construction of robust, secure AI systems. While 60% of surveyed BFM CEOs say their teams have the skills and knowledge to incorporate generative AI, more than half (53%) of respondents say they are already struggling to fill key technology roles. In addition, 50% of these CEOs said they are hiring for roles that did not even exist this time last year due to generative AI, showing the rapid shift occurring in the workforce. Business can either rely on off-the-shelf large language models or fine-tune LLMs for their use cases.

CFOs are not just concerned about financial capital, but human capital, intellectual capital, and social capital, he said. Digitalization aimed toward internal processes at a company and customers is requiring increased collaboration between departments and generative ai in finance enhanced key performance indicators (KPIs) to determine the most effective use of capital allocation. Sentiment analysis, an approach within NLP, categorizes texts, images, or videos according to their emotional tone as negative, positive, or neutral.

The hallucination risk, however, will likely remain a concern in the foreseeable future. However, it’s crucial to acknowledge hurdles such as security, reliability, safeguarding intellectual property, and understanding outcomes. Armed with appropriate strategies, generative AI can elevate your institution’s reputation for finance and AI. Successfully adopting generative AI requires a balanced approach that combines urgency and risk awareness.

The “opting out” choice for user data collection and use needs to be explicitly exercised. However, opting out seems to limit, although it’s unclear to what extent, GenAI responses and thus possibly diminishes the technology’s utility. The breadth and diversity of the data used by GenAI— which are at the core of its utility—make it exceedingly difficult at present to map GenAI’s output to the data, including in the extreme case of hallucination. Furthermore, GenAI’s architecture and decision-making process contribute greatly to the opaqueness of GenAI’s output process. GenAI algorithms runs on multiple neural network layers and uses numerous parameters to calculate the probabilities of each part of its answers.

A recent AI-generated report explains its decisions to favour certain companies and sectors while selling out of others. The tool, dubbed “Moneyball”, is meant to show portfolio managers “how they and the market have behaved in similar circumstances and helps them correct for bias and improve their process”, said Kristian West, head of investment platform for JPMorgan Asset Management. This publication contains general information only and Deloitte is not, by means of this publication, rendering accounting, business, financial, investment, legal, tax, or other professional advice or services. This publication is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect your business, you should consult a qualified professional adviser.

These properties are proving attractive to financial institutions, as they can customize their AI training to specific functions (for example, fraud detection), product development and delivery, and compliance reporting. In the context of financial services, GenAI hallucination is a significant risk on multiple levels. It undermines GenAI robustness and raises financial safety and consumer protection concerns.

An overreliance on gen AI and lack of understanding underlying analyses or data can also reduce the preparedness of finance teams to gut check “reasonable­ness” of outputs. It’s critical to bear in mind that gen AI is designed to enhance the productivity of people, not to replace them. GenAI could significantly expand the horizons for the use of synthetic data in the financial sector. GenAI is intrinsically geared toward generating new content and using more diverse sets of data sources, it can be used to code synthetic data–generator algorithms, and it better captures the complexity of real-world events.

Since September 2015, he had held the role of chief digital information officer, serving as customer zero for ServiceNow’s various products. But as Bedi began to spend increasing amounts of time with chief information officers and helping them work with AI, he took on the new role in May. Despite the developments, AI’s potential to drive long-term returns for asset managers has its sceptics.

You can foun additiona information about ai customer service and artificial intelligence and NLP. Conversational AI specifically focuses on simulating human-like conversations through AI-powered chatbots or virtual assistants, by using natural language processing (NLP), natural language understanding (NLU) and natural language generation (NLG). 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. GenAI is a specific subset of AI–machine learning (AI/ML) technologies, distinguished by their ability to create new content.

Just 21 percent of companies reporting AI adoption say they have established policies governing employees’ use of gen AI technologies. Part of the training for maintenance teams using a gen AI tool should be to help them understand the limitations of models and how best to get the right answers. That includes teaching workers strategies to get to the best answer as fast as possible by starting with broad questions then narrowing them down. This provides the model with more context, and it also helps remove any bias of the people who might think they know the answer already.

For example, Deutsche Bank is testing Google Cloud’s gen AI and LLMs at scale to provide new insights to financial analysts, driving operational efficiencies and execution velocity. There is an opportunity to significantly reduce the time it takes to perform banking operations and financial analysts’ https://chat.openai.com/ tasks, empowering employees by increasing their productivity. 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.

We’ve seen engineers build a basic chatbot in a week, but releasing a stable, accurate, and compliant version that scales can take four months. That’s why, our experience shows, the actual model costs may be less than 10 to 15 percent of the total costs of the solution. Some industrial companies, for example, have identified maintenance as a critical domain for their business. Reviewing maintenance reports and spending time with workers on the front lines can help determine where a gen AI copilot could make a big difference, such as in identifying issues with equipment failures quickly and early on. A gen AI copilot can also help identify root causes of truck breakdowns and recommend resolutions much more quickly than usual, as well as act as an ongoing source for best practices or standard operating procedures.

Banks with fewer AI experts on staff will need to enhance their capabilities through some mix of training and recruiting—not a small task. Our diverse, global teams bring deep industry and functional expertise and a range of perspectives that question the status quo and spark change. BCG delivers solutions through leading-edge management consulting, technology and design, and corporate and digital ventures. We work in a uniquely collaborative model across the firm and throughout all levels of the client organization, fueled by the goal of helping our clients thrive and enabling them to make the world a better place. Assess existing talent, identify skill gaps, provide training opportunities, and recruit individuals who are equipped to handle future use cases as they emerge.

Generative AI is well suited to transform these large repositories of written and spoken word into on-demand structured or semi-structured information that can power investment processes and retail investor interactions. In the financial services industry, leaders and developers are eager to understand generative AI’s potential and put it to work. Generative Al’s large language models applied to the financial realm marks a significant leap forward. 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.

Realizing this potential, however, is contingent on a commitment to responsible innovation and ensuring that the development and use of generative AI is supported by ethical considerations and safety and soundness. “Workforce needs are shifting rapidly in the financial services sector and CEOs must ensure that upskilling programs are prioritized as an important element of any financial institution’s enterprise strategy for scaling generative AI.” Schrage also discussed with Fortune editor-at-large Michal Lev-Ram how the meaning and measurement of capital allocation are being changed by digitalization across companies and industries.

Having model interfaces that look and feel the same as existing tools also helps users feel less pressured to learn something new each time a new application is introduced. QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges.

In a recent Harris Poll of workers, about half do not trust the technology.3 Finance leaders should consider change management carefully, leaning into the idea that generative AI can support our lives, transforming from an enabler of our work to a potential co-pilot. AI high performers are expected to conduct much higher levels of reskilling than other companies are. Respondents at these organizations are over three times more likely than others to say their organizations will reskill more than 30 percent of their workforces over the next three years as a result of AI adoption. Building a gen AI model is often relatively straightforward, but making it fully operational at scale is a different matter entirely.

JPMorgan also recently announced that it is developing a ChatGPT-like software service that helps selecting the right investment plans for the customers. Boston Consulting Group partners with leaders in business and society to tackle their most important challenges and capture their greatest opportunities. Today, we work closely with clients to embrace a transformational approach aimed at benefiting all stakeholders—empowering organizations to grow, build sustainable competitive advantage, and drive positive societal impact.

2023 was a game-changing year for business, with an explosion of interest in generative artificial intelligence. However, with gen AI, bad actors can perpetrate fraud at scale by targeting multiple victims at the same time using the same or fewer resources. In 2022 alone, the FBI counted 21,832 instances of business email fraud with losses of approximately US$2.7 billion. The Deloitte Center for Financial Services estimates that generative AI email fraud losses could total about US$11.5 billion by 2027 in an “aggressive” adoption scenario. At this stage, Apple can’t beat OpenAI at its own game, so it’s partnering instead.

Without LLMs, questions would typically have to be anticipated and a fixed set of answers would have to be created in advance by human authors. Whereas, with LLMs, answers can be generated on the fly and, as new information becomes available, it can be incorporated automatically into the answers provided. Across banking, capital markets, insurance, and payments, executives are eager to understand generative AI and applicable use cases, and developers want to experiment with generative AI tools that are easy to use, secure, and scalable. Below we explore four use case categories where generative AI can be applied in the financial services industry.

Cem’s hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks. 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. 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.