How banks can harness the power of GenAI Switzerland

That ability to be able to have a fairly substantial impact on what the experience will look like for the product set is a second big benefit for us. If we put the HR lens on it, that means that we have the opportunity to fundamentally change our colleagues’ experience of how they work with us. What that means at a macro level is how we work, where we work, and how the type of work we do is constantly evolving and changing. Generative AI can also contribute to increased efficiency and streamlined operations, while freeing up time of staff to focus on more enjoyable and value-adding tasks. This webinar will delve into the chilling realities of modern fraud, exploring how generative AI and trustworthy AI can both haunt and protect our financial systems. By leveraging EY.ai’s comprehensive platform, expertise and ongoing advancements, banks can embrace the transformative potential of AI in a secure and responsible manner.

As use cases are being built, tested, and scaled among workers, Business Insider talked to 35 people in various roles across the finance industry to hear how AI adoption is happening on the ground. Many of these people were granted anonymity to speak freely about their experiences. The banking industry is eager to adopt generative AI and safely capitalize on its potential benefits.

Other key use cases include process improvement, knowledge enhancement, and innovation. In the realm of technological breakthroughs, generative artificial intelligence (generative AI) is emerging as a defining paradigm shift. Tapas Mishra from Cognizant outline why this is the case and sheds light on the some of the most promising use cases in four sectors.

“Lower-skill workers benefit because AI assistance provides them with new solutions, whereas the best performers may see little benefit from being exposed to their own best practices.” “If you look at the way software developers spend their time, they typically only spend 30% to 40% of their time in writing the code, interacting with the program manager to figure out what to build,” Lhuer said. More than 60% of their time is spent in deploying the software and getting governance approvals and infrastructure provisions. Using AI in the back office to assist with solving problems of automation, such as assisting a bank’s IT staff with scripting automations for certain processes. They used narrow AI for things such as fraud detection, consumer loan decisioning and tailored chatbots many years before the release of ChatGPT. It is an exciting time, as we are now more empowered than ever to forecast, strategise and execute with confidence.

Real-world examples of financial institutions successfully utilizing AI for fraud detection

Sovereign funding enables these banks to focus on long-term investments and growth opportunities and many have invested heavily over the past five to seven years in upgrading their technology infrastructure. As a result, more banks in the region have adopted flexible, scalable cloud-native technologies and modular API-enabled product platforms, as well as platform-centric operating models. They do not have mission-critical systems with a large overhang of technology debt and key man risks from a dwindling pool of resources conversant in legacy programming languages such as Common Business Oriented Language (COBOL). Leveraging GenAI can enable banks to create personalised experiences for each customer while maintaining robust security systems. This tailored approach addresses logical hazards and minimises complications arising from traditional practices.

Many incumbents, weighed down by tech and culture debt, could lag in AI adoption, losing market share. “Most banks have incorporated all the data they legally can incorporate into an underwriting process,” he said. “So adding gen AI, which is going to give you random answers, is not going to add anything to it.” The use of generative AI seems to be helping employees be more productive, in banks and other workplaces. But so far, the results are far better for young people and novices than for skilled workers, who can even be negatively impacted by the technology. While the AI frontier is fraught with security and privacy challenges, banks and their customers have much to gain if they can implement AI responsibly.

One challenge is making sure that data, such as new product brochures and country or sector reports, moves quickly into a bank’s systems. Consequently, data architecture—the management of data from collection through transformation, distribution, and consumption—has risen to strategic prominence. More broadly, banks will need to invest in technology architecture to build common generative AI services. The architecture should allow a bank to monitor the cost and latency of the system, provide traceability for a query or conversation, moderate content, and help decide which foundation model to access for diverse use cases. The technology works best when orchestrated with other machine learning processes and systems, and it raises its own organizational, technological, regulatory, and ethical challenges.

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In this article, we will explore some of the most promising risk-reducing use cases for AI in financial institutions, and how they can help detect fraud, and maintain financial market stability. When selecting an AI service, banks should select proprietary or paid models that provide data privacy and security controls. Whether a generative AI service is free or paid can directly affect the privacy and security commitments of the vendor offering the service. What has changed — almost overnight — is the introduction of generative AI models into banking platforms. Whether through third-party models like ChatGPT or proprietary models, financial technology companies and banks are exploring myriad ways to leverage the exciting technology. For example, AI enables forecasts and scenarios to be constantly adapted based on compiled and processed data, and the quality of the forecasts improves over time.

A core job of internal compliance teams is to comb through myriad compliance regulations. AI can complement and speed up this work, using deep learning and NLP to review compliance requirements and improve decision-making. AI is more accurate than manual fraud detection methods or rules-based anti-fraud software, improving fraud detection processes, Sindhu said.

Risk Reducing AI Use Cases for Financial Institutions

But banks clearly understand the urgency; a huge majority are already dedicating resources to GenAI. Similarly, many banks have been pursuing industry verticalization and deposit retention strategies, as well as seeking new and diversified revenue streams. “We need to be able to understand the weights and understand the data that the model is being trained on,” he said.

Understanding how LLMs arrive at specific decisions can be difficult, complicating efforts to ensure transparency and accountability. 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. With the consumerization generative ai use cases in banking of AI, through the likes of ChatGPT, individuals have started experiencing AI at a personal level, which is reminiscent of the mobile phone revolution. Bulky landline phones were replaced by feature phones, and those gave way to smartphones. The technology now accounts for 31% of bank technology budgets as per the Infosys Bank Tech Index, up from 20% previously.

At one midsize Wall Street investment bank, a senior banker uses ChatGPT daily — a sign of how AI is seeping into the analog world of investment banking, where bankers’ interpersonal connections and flair in the boardroom have long reigned supreme. These are the puzzles that chief information officers, chief technology officers, and data leaders who oversee their firms’ AI strategies are expected to solve. But there’s no one-size-fits-all approach to tapping AI’s benefits, and finance doesn’t have the best reputation for integrating tech.

LLMs are being used across the financial services industry to improve operational efficiencies and enhance customer interactions. Applications range from automating routine tasks to providing advanced analytical insights. Data privacy laws vary significantly across jurisdictions, posing challenges for global financial institutions. Ensuring compliance with diverse regulatory requirements is critical when deploying AI solutions that process sensitive financial data. But given extensive industry regulations, banks and other financial services organizations need a comprehensive strategy for approaching AI.

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. Banks (for example, Morgan Stanley) use these AI tools to supercharge fintech such as customer-facing chatbots.

Executives should be looking for big impacts at an enterprise level rather than focusing on siloed use cases and productivity gains. Apply genAI across the process and you can start to run the various steps in parallel. And these kinds of applications could deliver productivity gains of, say, 75 percent. Some chatbots have been deployed to manage employee queries about product terms and conditions, for example, or to provide details on employee benefits programs. KPMG professionals have helped banks pilot genAI as information extractors to find anomalies within contracts or flag potentially fraudulent transactions. GenAI has also been used to quickly create bits of code that allow legacy systems to interact with new technologies.

“To do AI, you need to be a great thinker, but also a great doer and be able to bring people together.” One potential use case could be summarizing hundreds of pages of regulatory documents into a list of the relevant obligations, one Citi employee told BI. A human would still need to double-check and validate that the information was correct, “but that just saved you so much time that’s wasted just reading,” they said. A January report by the firm found that 93% of finance workers and employees are very keen to leverage generative AI, but Smith said their organizations’ rollout of AI tools and approach to upskilling are a source of frustration. Quantitative trading firms and hedge funds such as Two Sigma use powerful compute engines and AI chips to uncover new sources of investing alpha. Meanwhile, consumer banks like JPMorgan, with their sprawling technology footprints and troves of data about how consumers spend, save, and invest, have been focused on readying their data strategies to take full advantage of AI.

Meanwhile, Ally Financial stands out as a leader in real-world GenAI implementation. By embracing a collaborative approach with regulators and a flexible technology partner, the firm has successfully deployed large language models to summarise customer calls, paving the way for more than 400 additional applications. Although regulators try to keep pace with technological development by issuing nonbinding guidelines, the territory lacks GenAI rules and regulations.

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. Elevate the banking experience with generative AI assistants that enable frictionless self-service. For smaller and midsize organizations in earlier stages of GenAI adoption, a CoE will suffice as a first step and coordination point for knowledge. Further, a CoE will allow the organization to incrementally improve capabilities, spread best practices, foster knowledge sharing and promote early use cases.

Governance complexities with RAG implementations

While it is seemingly impossible to do business without running into a discussion on AI, separating what is hype from what is practical and useful can be difficult. And because AI development is rapidly and constantly changing, leaders have an even bigger challenge when using AI to get ahead. With the uses and power of generative AI expanding exponentially, banks will want to master the technology soon if they hope to thrive, not just survive. To do that, a bank will, of course, need a detailed, coherent strategy that incorporates scenario planning and a deep understanding of retail and commercial customers’ priorities. A bank must also execute well, notably by delivering use cases that can be easily scaled up and encourage test-and-learn adjustments. The components to support such a process include specific applications as well as a deeper bench of data analytics talent.

In addition to these teams, there are also analysts and data specialists working across all the business areas, giving a total of more than 5,500 employees, of whom 1,000 or so are data scientists. Recent industry reports highlight key priorities such as improving operational efficiency, enhancing customer experience, and bolstering risk management. AI, particularly generative models, offers solutions ChatGPT to these priorities by automating complex tasks, providing personalized customer interactions, and analyzing vast amounts of data to detect fraudulent activities. At EY organization, we have identified specific AI pathways banks and FinTechs that are taking initial steps and have captured significant lessons learned across the retail and wholesale banking, wealth management and insurance sectors.

Major cloud service providers are also integrating generative AI into their offerings, featuring integrated development environments and office productivity software. The guidelines arrive against a backdrop of HKMA noting growing interest in GenAI from the city’s banks. Locally, 39% of the authorized institutions the regulator surveyed are already using GenAI or are planning to use it. GenAI is revolutionising the banking industry by enhancing operational efficiency and customer satisfaction. As the market moves toward cashless banking, GenAI introduces a unique opportunity for banks to explore untapped possibilities and overcome existing limitations. All hype aside, genAI is creating fundamentally new approaches and models that can have a truly transformative impact on banks.

Artificial intelligence (AI) is an increasingly important technology for the banking sector. When used as a tool to power internal operations and customer-facing applications, it can help banks improve customer service, fraud detection and money and investment management. The paper takes a business-technical approach to highlight the deep potential of Gen AI  systems, the key quality determinants of such systems including its infrastructure to  contribute to the financial industry awareness and advancement. It highlights key  considerations for implementing Gen AI systems, which includes the need for high quality  data, fit-for-purpose technology and the ability to distinguish between Gen AI models. The  paper also provides an overview of how to construct Gen AI use case portfolios and  identify optimal use cases based on requirements. It also discusses key risks and  mitigants related to data, systems, cyber security, dependency and sustainability, which  are familiar to the industry.

While the jury is still out as to who will win between skeptics and futurists, we believe that in five years, AI will solve complex business problems – and do it at scale.The tech still has a long way to go as it is faced with many challenges. AI isn’t yet providing early warning signals in risk, while fraud and cyber threats continue to rise.It also needs to improve decision-making for portfolio analysis and for making credit decisions. While the tech will become mainstream across financial institutions, now is the time to put together an integrated AI-first led strategy and experiment and evolve to build competitive differentiation and realize the bank’s business objectives. The cloud offers a dynamic and efficient foundation by enabling access to computing resources, storage, and innovative services. Yet institutions face hurdles in the form of data quality, accessibility, and governance.

It proposes key recommendations for industry  adoption and integration, including the need for industry-level accepted test data sets, air  usage policies, AI sandboxes and streamlined regulations. It also offers a view of a  strategic “composable Gen AI” roadmap for companies beginning their AI journey. In some cases, Gen AI technology is useful in identifying the most relevant use cases to pursue.

“We want very deliberately to do a human-in-the-loop kind of approach to start with, so that you’re never actually quite exposing things outside,” says Natarajan. “We really work to make sure we have the appropriate guardrails and security in place for our general use cases,” says Michael Ruttledge, chief information officer at Citizens Financial. Deutsche Bank has collaborated with Kodex AI, a Berlin based start-up company that  focuses on developing Artificial Intelligence (AI) solutions, to launch the whitepaper  ‘Adopting Generative AI in Banking’. Consumers give BNPL high marks, relative to other forms of payment, in our new survey. Once the roadmap has been drawn up, the ability to deliver use cases through a streamlined and scalable approach typically relies on five steps, from the initial design to continuous monitoring and improvement (see Figure 2).

Additionally, AI plays a crucial role in modernizing legacy systems, enabling them to support advanced applications and meet evolving business needs. Predictability requires rigorous testing and validation of AI models to ensure consistent and reliable outputs. By maintaining transparency and predictability, financial institutions can build trust with regulators, customers, and other stakeholders, demonstrating their commitment to ethical AI practices.

“These models can do this type of categorization really well, because you are simply asking it to understand the English language and say, did it fit into this category or does it fit into this other category?” Swamy said. Generative AI might also be used to categorize documents submitted in a loan application process, like documents verifying income, identity or residence. Commonwealth has also used AI to provide immediate support to customers ChatGPT App in natural disaster zones, and helped customers obtain more than $1.2 billion of government benefits and rebates to which they were entitled, through its benefits-finder tool. Tyrie was honored for his team’s work in scaling up Bank of America’s chatbot, Erica. According to the bank, Erica has responded to 800 million inquiries from more than 42 million clients and provided personalized insights and guidance more than 1.2 billion times.

• The European Union has the AI Act, which establishes a common regulatory and legal framework for AI in the EU.
• Multinational banks now find little room for regulatory arbitrage due to an equivalent increase in scrutiny from other regulatory bodies and intense international surveillance around AI usage.
• Because regulation is catching up, firms will need to think about how they build and enable systems that anticipate developments in regulation, rather than building processes that might be overtaken by restrictions.

When further help is required, Erica can smoothly transition the client to additional support. The use of AI isn’t new to Lloyds Banking Group, with Martin explaining that it has been adopted across multiple systems for quite a bit of time. However, often this has happened in the background, for instance by using data to help Lloyds become a skills-based organization. The difference now is that the bank is putting AI – particularly generative AI – in front of employees to help them query and carry out HR transactions themselves. Lloyds Banking Group is working with ServiceNow, Workday and Microsoft in its HR function to help enable this transformation. Martin said that by focusing on the larger technology players, this means that more often than not they come with a similar set of values and with guardrails specific to AI and generative AI built into their platforms.

The substantial investments by leading banks, together with the strategic deployment of platforms such as EY.ai, highlight the banking sector’s commitment to harnessing AI’s potential. These efforts are not just about adapting to advancements but driving them forward, ensuring that the future of banking is more innovative, efficient and customer-centric than ever before. You can foun additiona information about ai customer service and artificial intelligence and NLP. The rise of GenAI also brings forth challenges such as cultural resistance within organizations, strategic misalignment and the need to balance the costs of innovation against returns on investment.

Though some say OpenAI’s debut of ChatGPT represented a step change in AI and machine-learning capabilities, these generative AI models can still spew out misinformation, which means humans need to check the work done by AI. Not everyone in finance is convinced that generative AI will bring radical changes. Some employees at firms that have long used models powered by AI to seek an edge, such as quantitative hedge funds or trading firms, say a lot of the benefits are overhyped. The banker told BI he’s “retraining” himself to use tools like ChatGPT and Copilot more frequently. It’s transformed how he researches “everything,” from data about industry sectors to ideating new proposals to bring to clients. “Rather than just brainstorming with colleagues, you might brainstorm with a robot now,” he said, “which I’ve actually found to be pretty helpful because it spurs some new thoughts.”

Existing financial institutions are exploring AI use cases in areas like customer service and risk compliance, demonstrating operational cost reductions. However, issues including data quality and fragmented legacy systems pose challenges. MENA banks are uniquely positioned to leverage AI, thanks to substantial technology investments and robust regulatory frameworks. Yet, they too face data quality and accessibility challenges, which must be addressed to fully harness AI’s potential. Generative AI (GenAI) opens the way for innovation and operational efficiency in the financial services sector.

When applied to new credit applications, the models assess the applicant’s information, generate a creditworthiness score, and estimate the probability of default. When it comes to phasing in generative AI, banks should seek low-risk use cases that avoid areas of heavy regulatory concern. That will allow banks to learn as they go and introduce generative AI into more complex use cases methodically. Banks should also ensure that a bank employee is assigned to continuously test and monitor the AI’s behavior for quality assurance purposes. For example, while Gemini is free, it is subject to Google Workspace enterprise data privacy and security controls if purchased as part of a Google Workspace enterprise license. That means that data fed to the model is not aggregated for other purposes (such as model training) and is kept proprietary to the organization that licensed it.