Unlocking new opportunities with generative AI in financial services

As banks monitor initial use cases and partnerships, they should continually evaluate use cases for scaling up or winding down, as well as assessing which partnerships to consolidate. Banks will also need to decide how the control tower will interact with the different lines of business, and how ownership of use cases, budget, success and governance should be spread or centralized. Economic realities are limiting banks’ investments in all technologies and GenAI is no exception. More than half of survey respondents cited implementation costs as a challenge when exploring GenAI initiatives.

In a dynamic banking environment, banks are seeking to differentiate themselves and gain a competitive advantage. Generative Artificial Intelligence (GenAI) is transforming the banking sector, providing innovative solutions that optimise efficiency, enhance security, and increase customer satisfaction. In the past, financial crime prevention technology often came with high, recurring expenses, including licenses and project updates. The software-as-a-service (SaaS) model, however, offers a “consume what you need” approach, allowing companies to avoid periodic costly updates.

Before the new AI app was launched, some financial advisors would take an hour after a call to clean up notes. That Morgan Stanley source was hesitant when asked about the global analytics goal. “That’s not the goal, at least not today,” the source said, adding that initial efforts will not necessarily be reviewed by ChatGPT App corporate. If they want to send it to their branch manager they can, but we are not reviewing the tech output,” the source said. The biggest questions around data protection or data leakage are around how Morgan Stanley is hosting the OpenAI code and whether Morgan Stanley is interacting with APIs on OpenAI servers.

This feature improves operational efficiency and reduces manual workloads, allowing teams to focus on more strategic activities. The question now is what will financial services do next and how soon will they apply AI across the entirety of their organizations and more broadly with customers. Latest market insights and forward-looking perspectives for financial services leaders and professionals. All hype aside, genAI is creating fundamentally new approaches and models that can have a truly transformative impact on banks. Executives should be looking for big impacts at an enterprise level rather than focusing on siloed use cases and productivity gains. Around the world, KPMG banking and technology professionals have been hard at work helping clients think through the opportunities, risks and implications of genAI.

Explore the future of AI content and the critical role of digital watermarking in protecting creators’ rights and ensuring content authenticity. With experience in both the institutional and the startup side, Kundu brings his knowledge of data, AI, and how organizations work to discuss how genAI is impacting finance. Shameek Kundu discusses the implications of these changes with DigFin‘s Jame DiBiasio. Kundu served as Standard Chartered Bank’s group chief data officer before jumping into the world of AI startups, where he helped promote tools to assist in FIs’ understanding of machine learning. Breaking it down further, Rawlings notes that a data intelligence platform is trained on an enterprise’s own data and concepts, so it’s tailored to an organisation’s exact needs. According to Russ, data intelligence looks like all employees – including non-technical individuals – having the skills, knowledge, and understanding to confidently use data.

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Identify and address any potential shortcomings or discrepancies to ensure model robustness before deployment. Flow-based models are generative models that transform a simple probability distribution into a more complex one through a series of invertible transformations. These models are used for image generation, density estimation, and data compression tasks.

For more insights, we invite you to download the full IBV CFO Study and listen to this on-demand webinar to learn more about the evolving role of CFOs in the age of AI. More broadly, gen AI could transform compliance and security measures, enabling firms to meet regulatory requirements more efficiently while reducing the cost and effort involved in combating financial fraud and managing risk. Member firms of the KPMG network of independent firms are affiliated with KPMG International. No member firm has any authority to obligate or bind KPMG International or any other member firm vis-à-vis third parties, nor does KPMG International have any such authority to obligate or bind any member firm. 2 KPMG in the US, “The generative AI advantage in financial services” (August 2023). Banks seeking to use GenAI in their products should follow a range of principles—including ensuring that clients can opt out of using the technology and that AI models do not disadvantage or lead to an unfair bias toward certain client groups.

They should also foster a culture of transparency and accountability within their organizations, encouraging open discussion about the ethical implications of AI and empowering employees to raise concerns or suggest improvements. It is possible today to integrate AI into existing finance technology stacks (e.g. ERP, CRM, AP/AR systems), which is already starting to revolutionize the way we work in finance and accounting. For a deeper exploration of these valuable insights, we invite you to join the two public stage sessions hosted by NTT DATA at Sibos. These sessions will provide a comprehensive overview of our findings from the survey and offer insights from NTT DATA’s experts.

Human analysts often rely on intuition, experience, and an understanding of subtle market dynamics that may not be easily captured in structured data sets. This tacit knowledge is challenging to transfer to AI systems, regardless of the amount of historical data available. Generative AI can handle vast amounts of financial data but must be used cautiously to ensure compliance with regulations such as GDPR and CCPA. A centralized operating model is often used for generative AI in banking due to its strategic advantages. Centralization allows financial institutions to allocate scarce top-tier AI talent effectively, creating a cohesive AI team that stays current with AI technology advancements.

Additionally, finance professionals must navigate ethical and compliance issues related to AI, such as algorithmic bias and the role of human oversight. Compliance with industry standards like SOC (System and Organization Controls) is essential to maintain trust and transparency in AI-driven financial processes. In this environment, AI is democratizing financial data, making it more accessible and understandable for all levels of an organization’s management. gen ai in finance 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. DTTL (also referred to as “Deloitte Global”) does not provide services to clients. In the United States, Deloitte refers to one or more of the US member firms of DTTL, their related entities that operate using the “Deloitte” name in the United States and their respective affiliates.

On the other hand, there is a growing awareness among customers and an increased demand for flexible payment and financing solutions. “I think that the future really is getting much faster, better accurate insights out of all of that data,” he said. Maufe noted that data has ended up in silos for various reasons including technology constraints and organizational preferences. He also said that the financial ecosystem contains a large amount of both structured and unstructured data.

Editors would then need to write additional content to flesh out the articles, pushing the search for unique sources of information lower on their list of priorities. In past automation-fueled labor fears, machines would automate tedious, repetitive work. GenAI is different in that it automates creative tasks such as writing, coding and even music making. For example, musician Paul McCartney used AI to partially generate his late bandmate John Lennon’s voice to create a posthumous Beatles song. In this case, mimicking a voice worked to the musician’s benefit, but that might not always be the case. But more importantly, involving them, particularly agents and claim adjusters, allows the company to find the right genAI solutions and pivot when needed.

Importance of model benchmarking and documentation

Finance must also address data governance and be involved in ensuring data accuracy, which is crucial to training the LLMs correctly and ensuring accurate outputs. It’s also important that finance understands generative AI’s ethical implications and data privacy compliance requirements. AI can provide transparency into increasingly complex and expansive supply chains for manufacturers.

As AI continues to advance, we can expect to see even more transformative changes in finance and across all sectors. AI has the potential to revolutionize strategic financial decisions through advanced predictive capabilities, such as scenario planning and risk assessment. The convergence of AI with other technologies like blockchain and the Internet of Things (IoT) could also open up new possibilities for financial management and reporting. The survey gave us profound insights into prevailing market trends, and our experts will be on hand to present an in-depth exploration of the data and results at Sibos 2024, the premier annual event for the financial services industry. Financial services is one of the many industries where generative AI technology can significantly transform operations. For banks, there’s the potential to tackle challenges such as regulatory hurdles, data governance and rising customer expectations – among others.

“The banks that are the most successful at core modernization have a north star” feat. Valley Bank and Galileo

This event brings together some of the most innovative minds in fintech and traditional finance, providing attendees with firsthand insights into the cutting edge of AI implementation. This model ensures critical decisions on funding, new technology, cloud providers and partnerships are made efficiently. It also simplifies risk management and regulatory compliance, providing a unified strategy for legal and security challenges. Imagine a world where your AI assistant generates complex financial reports in minutes, predicts market trends with high accuracy or even suggests cost-cutting strategies based on real-time data.

Compliance with these regulations involves providing clear explanations of AI model decisions, ensuring data privacy, and implementing safeguards against biases and discriminatory practices. Financial institutions must stay informed about evolving regulatory requirements and adapt their AI strategies accordingly. Existing AI regulations in financial services are primarily focused on ensuring transparency, accountability, and data privacy. Regulatory bodies emphasize the need for financial institutions to demonstrate how AI models make decisions, particularly in high-stakes areas like AML and BSA compliance. Our team of thought leaders combines exceptional service with expertise in the field, providing a tailored experience for both veteran and new clients. Let’s delve into grasping the holistic and strategic approach required for integrating Generative AI in financial services.

In addition, the enterprise should emphasise the development of soft skills such as critical thinking, creativity, and emotional intelligence. Generative AI has the potential to transform core accounting and finance operations. Applying generative AI to processes such as the reviews of the general ledger and outstanding reconciliation items means reviews become more rigorous and effective. Generative AI deployment is not about automating for efficiency but rather about new possibilities for how customers are served and the products they are offered.

The adoption of LLMs in financial services is driven by their ability to process and generate human-like text, enhancing operational efficiency and customer experience. Use cases include automating regulatory reporting, analyzing transaction data for fraud detection, generating personalized customer communications, and providing real-time financial advice. LLMs enable financial institutions to streamline processes, reduce operational costs, and deliver enhanced value to customers through advanced analytical capabilities.

Generative AI for Finance

One of the primary challenges of using generative AI in AML/GFC is the “black box” nature of these models. Understanding how LLMs arrive at specific decisions can be difficult, complicating efforts to ensure transparency and accountability. Financial institutions must document and justify AI-driven decisions to regulators, ensuring that the processes are understandable and auditable. Predictability in AI outputs is equally important to maintain trust and reliability in AI systems. Unlike traditional machine learning models, which often require extensive feature engineering and domain-specific adjustments, LLMs can generalize from vast datasets without the need for such tailored configurations. Anti-Money Laundering (AML) and Global Financial Compliance (GFC) frameworks are foundational to maintaining the integrity of the financial system.

The complexity of LLMs makes it challenging to interpret their decision-making processes. This lack of transparency can hinder efforts to justify AI-driven decisions to regulators and stakeholders. This can lead to unfair outcomes in areas like loan approvals, credit scoring, or algorithmic trading. Biased data can perpetuate historical inequalities and lead to discriminatory practices.

As economic volatility continues to rise, CFOs face increasing pressure to ensure operational efficiency while also spearheading digital transformation. The challenge lies in adopting new technologies to stay ahead of the competition, while managing the complexities of today’s financial landscape. The answer to this challenge might lie in harnessing the power of artificial intelligence (AI). Chief financial officers (CFOs) are no longer just number crunchers; they are strategic leaders responsible for driving innovation and growth.

For example, the application of GenAI to lending decisions could lead to biased outcomes based on protected characteristics (e.g., gender or race). The burden of proof rests with banks, meaning they will need to collect evidence to show regulators why applications are denied and that applicants are considered fairly. Even where there are no legal or regulatory boundaries at present, governance models must be designed to promote responsible and ethical use of GenAI.

David Parker is Accenture’s global financial services industry practices chair who covers the impact of technology and fintech on the banking, capital markets and insurance industries. He’s written about how financial services firms can unlock the full value of generative AI, why the FS adoption of cloud computing has been slower than envisioned and lucrative niches for fintechs moving forward. In addition to his global role, David is the co-organizer of Accenture’s FinTech Innovation Lab, a mentorship program bringing together fintech start-ups and leading financial institutions, with labs in the U.K., U.S., and Asia-Pacific. Follow him for continued coverage around how financial services firms and fintechs are embracing technology, AI and data to reinvent their operations and deliver a more personalized customer experience.

Recent research from EY-Parthenon reveals how decision-makers at retail and commercial banks around the world view the opportunities and challenges of GenAI, as well as highlighting initial priorities. In the beginning, it is likely that Morgan Stanley people will be very meticulous in verifying what the app delivers, particularly making sure that nothing important was missed. Over time, though, Cirksena said, people may start to trust the app too much and pull back on time-consuming verification efforts. Even if it works precisely as planned, some question whether this analysis could have a downside for Morgan Stanley.

• Addressing issues such as algorithmic bias, data privacy, and the appropriate level of human oversight is crucial to maintaining trust and transparency.
• Making these advanced capabilities a reality requires a clear vision, the ability to execute change, new technology capabilities and new skills and talent.
• The convergence of AI with other technologies like blockchain and the Internet of Things (IoT) could also open up new possibilities for financial management and reporting.
• The result has been reduced customer wait times, and less need for human intervention as digital agents learn how to answer more, and more complex, questions.

Today, we’re joined by Bill Borden, Corporate Vice President, Worldwide Financial Services, at Microsoft, and Suzanne Dann, CEO for the Americas at Wipro. Together, they discuss their collaboration on leveraging Azure OpenAI to enhance generative AI in finance. This partnership focuses on improving customer experiences, streamlining processes, and ensuring responsible AI practices in the financial industry.

The integration of generative AI in AML and BSA programs presents significant opportunities for financial institutions. While challenges remain, particularly around transparency and regulatory compliance, the benefits of enhanced efficiency and improved compliance processes are substantial. LLMs are being used across the financial services industry to improve operational efficiencies and ChatGPT enhance customer interactions. You can foun additiona information about ai customer service and artificial intelligence and NLP. Applications range from automating routine tasks to providing advanced analytical insights. AML and GFC initiatives are vital for detecting and preventing financial crimes such as money laundering, terrorist financing, and fraud. These frameworks require continuous monitoring, reporting, and updating to address evolving threats and regulatory changes.

Bud uses advanced technologies like DataStax Astra DB to manage and scale their data operations seamlessly, ensuring high performance and reliability. Astra DB’s scalability and performance enable Bud to process hundreds of thousands of transactions per second, delivering real-time insights and services. These industry leaders will share insights on how they’re leveraging generative AI to drive innovation and efficiency in their operations, as well as discuss the challenges and opportunities they’ve encountered in implementing these technologies. Their firsthand experiences and perspectives will provide valuable context for understanding the current state and future potential of AI in finance. We assessed the current AI impact on each job role as high, medium or low, based on the current capabilities of generative AI and its implementation in these areas.

In recent months, leaders in the AI industry have been actively seeking legislation, but there is no comprehensive federal approach to AI in the United States. Several states — including California, Illinois, Texas and Colorado — have introduced or passed laws focused on protecting consumers from harms caused by AI. AI chatbots could also be used internally to help employees access their benefits and perform other self-service tasks. The prevalence of AI in vehicles has the potential to affect car and truck driving jobs. Rideshare companies are partnering with self-driving car providers to minimize the need for human drivers and give riders the option to ride in an autonomous vehicle. Generative AI tools such as ChatGPT and Gemini can generate text that aims to convince readers that a human wrote it.

In a competitive landscape, banks are constantly seeking to reduce costs, pioneer new products and services that gain customer support, and advance their market share. The potential of AI in financial crime prevention lies in understanding not only the processes but also the regulatory obligations of the field. AI solutions need to complement human-driven decision-making while focusing on outcomes in managing risk. Traditionally, financial services, gaming, insurance, and payments organisations regulated by anti-money laundering (AML) laws relied on third-party technology providers solely for boosting efficiency around transaction monitoring and screening.

Poor or incomplete datasets can lead to incorrect outputs, negatively impacting financial decision-making and customer trust. Karim Haji, Global Head of Financial Services, outlines why it’s such an exciting time for the financial services industry. Market insights and forward-looking perspectives for financial services leaders and professionals. Global, multi-disciplinary teams of professionals strive to deliver successful outcomes in the banking sector. KPMG professionals use close connections and their understanding of key issues, with deep industry knowledge to help drive successful and sustainable technology and business transformations. Some chatbots have been deployed to manage employee queries about product terms and conditions, for example, or to provide details on employee benefits programs.

In the survey, over 75% of CEOs emphasised the importance of ecosystems, partnerships, and collaboration in achieving successful outcomes with generative AI. On June 21, Senate Majority Leader Chuck Schumer formally unveiled an open-ended plan for AI regulation, explaining that it could take months to reach a consensus on a comprehensive proposal. Schumer emphasized that the regulations should focus on protecting workers, national security, copyright issues and protection from doomsday scenarios. In May 2024, Schumer and several other senators released a document to guide congressional committees’ approaches to future AI bills. Despite, generative AI’s positive effect in this field, it also comes with risk in the form AI hallucinations, which can potentially introduce inaccurate or useless information. Some people draw an analogy between ChatGPT and when students weren’t allowed to use calculators in the classroom.

AI solutions simulate natural language by using natural language processing (NLP). Banks (for example, Morgan Stanley) use these AI tools to supercharge fintech such as customer-facing chatbots. These programs now handle an array of customer service interactions regarding topics from account information to personalized financial advice, acting as virtual financial advisors. The AI-driven models can analyze a vast array of information sources — from financial news outlets and social media feeds to corporate announcements and economic reports.

On the one hand, most seem to believe that the technology could dramatically increase their ability to detect and predict attacks. But, at the same time, they worry that the enterprise adoption of a new technology might create new attack vectors. This is particularly valuable for financial service organizations, which are not only information intensive, but often have data stored in multiple locations, in the cloud and within local legacy systems. For example, Stanford Digital Economy Lab scholars recently studied3 the impact of a GenAI tool that was deployed at a busy call center.

Evidently, a smaller, customised LLM can be invaluable for financial institutions. But it’s not just about generative AI; Rawlings notes that organisations can’t neglect the importance of data. On a broader level, Russ adds, this can facilitate in-house training of custom models across the industry, empowering every organisation to extract the most value out of a model that is powered by its own private data. Russ Rawlings, RVP, Enterprise, UK&I at Databricks, says the first step to leveraging generative AI tools, like customised large language models (LLMs), is entering with an optimistic outlook.