Hong Kong issues generative AI guidelines to banks to avoid bias against consumers South China Morning Post

While GenAI has tremendous potential, there are emergent risks, especially in areas such as data confidentiality, GenAI hallucination, bias, toxicity and cyber security. We will continue to enhance our existing AI and machine learning risk framework to cater for such risks. “The challenges are deep domain knowledge of treasury management and digital skills gap, complexity of implementation, high cost of digital transformation, increased dependency on legacy technology and organisational siloes,” Hovhannessian concluded. From the team’s point of view, the technology has so far been helpful during the process of product design for programmers, those inhouse with banks or at third-party fintechs. While at the same time, it’s moving slowly towards an integration into products themselves, with pilot projects being tested out, he shared.

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 generative ai use cases in banking successful and sustainable technology and business transformations. Around the world, KPMG banking and technology professionals have been hard at work helping clients think through the opportunities, risks and implications of genAI.

GenAI can help unlock massive benefits, but only when it is applied smartly, responsibly, and holistically. However, it is worth taking a step back from the hype to really understand what genAI is, what it can do, and the risks and opportunities involved. EY refers to the global organization, and may refer to one or more, of the member firms of Ernst & Young Limited, each of which is a separate legal entity. Ernst & Young Limited is a Swiss company with registered seats in Switzerland providing services to clients in Switzerland. Risks related to data privacy, security, accuracy and reliability are banks’ top concerns for GenAI implementations. That’s understandable given that large language models (LLMs) can be subject to hallucination and bias.

However, for GenAI to be useful in the workplace, it needs to access the employee’s operational expertise and industry knowledge. 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. “I think that the future really is getting much faster, better accurate insights out of all of that data,” he said.

A control tower approach both provides GenAI leadership and coordinates ongoing execution and deployments. It’s critical that the right controls and metrics be put in place, with adjustments being made over time as business outcomes are tracked and needs change. All sizes of financial institutions can benefit by standing up a GenAI center of excellence (CoE) to implement early use cases, share knowledge and best practices and develop skills. Strong use cases will include “high-touch” activities historically owned by people, which leverage large datasets or require a generative response logic.

Leveraging GenAI in banking: Opportunities, risks, and security measures

The Artefact solution uses the target persona to model a virtual “cluster” of customers, with each cluster representing 2 to 3 million real customers. The employee can then interact conversationally with customer avatars generated by IBM watsonx.ai AI studio, querying them about their personal preferences and consumption habits. Asteria Smart Finance Advisor gives Asteria’s small and medium enterprise (SME) clients immediate insight into the financial health of their businesses.

Company executives have emphasized taking a pragmatic and disciplined approach as the plan has moved forward in concert with the bank’s ongoing cloud migration. For example, U.S.-based Bankwell Bank has deployed Cascading AI’s Casca conversational AI assistant loan origination system for small business owners. In this report, we discuss what use cases are likely in the next couple of years, and we gaze further ahead too, calling on insights ChatGPT from those at the sharp end of progress. Long-established jobs have been eliminated in past periods of technological transformation, to be replaced by new ones. The industry’s AI spend is projected to rise from $35 billion in 2023 to $97 billion by 2027, which represents a compound annual growth rate of 29%. The largest players are aggressively investing in developing their AI infrastructure and scaling use cases to capture more value.

Everest Reinsurance strengthens leadership team with Emily Davis as head of global specialties

These programs now handle an array of customer service interactions regarding topics from account information to personalized financial advice, acting as virtual financial advisors. While AI governance processes and controls are somewhat similar to those for legacy technologies, new risks require new models and frameworks, both for internal use cases and use of third-party tools. Larger banks further along in their AI experimentation should establish a control tower function to not only provide direction and vision, but also document a high-level roadmap to achieving the firm’s GenAI goals. Such a roadmap requires a rethink of the value chain and business model, a full assessment of technology architectures and data sets and evaluation of innovation investments.

Issues about data privacy also come into play when the question of publicly available systems respect user input data privacy, and whether there is a risk of data leakage, noted the European Central Bank. The bank generates ROI by acquiring new customers and improving sales leads, she said. The latest EY report finds that CEOs recognize the potential of AI but are encountering significant challenges in developing AI strategies. Join us at the EY GCC GenAI Conclave 2024 to hear from industry experts on flagship event for GCC leaders of leading organizations across India, focussed on trends and topics concerning today’s GCCs. Explore the future of AI content and the critical role of digital watermarking in protecting creators’ rights and ensuring content authenticity.

Build confidence, drive value and deliver positive human impact with EY.ai – a unifying platform for AI-enabled business transformation. Making these advanced capabilities a reality requires a clear vision, the ability to execute change, new technology capabilities and new skills and talent. In our corporate call centre, we are using GenAI for call transcription, summarisation, service request generation and knowledge base lookup, reducing the amount of time needed to handle customer requests while improving our response quality. Artificial intelligence (AI) and generative AI (GenAI) are game changers, and we will see significant developments in the next five years that will fundamentally shape the way we work. The use of AI is not new to DBS, and we have been working with AI for over a decade now.

Integration with compatible up-and-coming technologies such as blockchain and Internet of Things (IoT) offers the potential to further expand the capabilities and benefits of GenAI. The banks that adopt these innovations will be best poised to take the lead in digital transformation and establish new benchmarks in efficiency, security, and customer experience for the industry. One European neobank, bunq, is already using generative AI to help improve the training speed of its automated transaction monitoring system that detects fraud and money laundering.

While AI is powerful on its own, combining it with automation unlocks even more potential. AI-powered automation takes the intelligence of AI with the repeatability of automation. For example, AI can enhance robotic process automation (RPA) to better parse data analytics and take actions based on what the AI decides is best.

Artificial Intelligence (AI) will profoundly change the future of finance and money. And according to a new Citi GPS report, it could potentially drive global banking industry profits to $2 trillion by 2028, a 9% increase over the next five years. 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.

Two other fundamental analysts BI spoke with agreed there was no explicit use of AI in their processes. You can foun additiona information about ai customer service and artificial intelligence and NLP. He uses the extra time to write notes for his portfolio manager or craft questions for the management teams he covers. Jobs across the industry vary, obviously, but if you’re in the business of giving advice and influencing outcomes, it’s time for those hard-for-a-machine-to-replicate skills to shine. The banker primarily uses AI to compress dense troves of information — whether from research notes or dozens of meetings — into more digestible takeaways. “I can query those reports through AI and get a pretty snappy summary of what they are, and then I can take that and use my own brain at that point to put it all together.” From C-suiters to junior staff, most said they welcome generative AI’s potential to boost efficiencies and cut grunt work.

How gen AI is ‘raising the floor’ for explainability and access in financial services

CFOs at financial institutions also worry about the nontrivial costs of resources required to operate the better-known generalized LLM platforms. Banks are increasingly turning to smaller, more specialized domain models that can be finely tuned on proprietary data, creating a competitive edge while also being more cost-effective. These domain-specific models require fewer tokens to perform tasks, thus reducing operational costs. Additionally, most established financial institutions as well as FinTech institutions rapidly progress from initial exploration with a single LLM to a portfolio of domain-specific models tuned for specific use case categories. These categories would be based on a common substrate that both protects sensitive data and allows results to be compared on a like-for-like basis across multiple LLMs.

• We develop outstanding leaders who team to deliver on our promises to all of our stakeholders.
• The right talent is the bedrock for building resilient, compliant, and secure AI systems.
• 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.
• Taking advantage of the transformational power of GenAI requires a combination of new thinking about a longstanding challenge for banks — how to innovate while keeping the lights on.

The financial services company brought Sri Shivananda on board as the firmwide technology chief in June, reporting to CIO Lori Beer. Teresa Heitsenrether was also appointed as chief data and analytics officer in June 2023, tasked with helming AI adoption efforts at the bank. GenAI is also expected to have a significant impact on productivity across financial services. Deloitte predicts that the top 14 global investment banks can boost their front-office productivity by as much as 27% to 35% with GenAI.

The Last Word: AI in Community Banking — Finding the Right Fit

These use cases would be optimized for ROI, ensure integration feasibility, reduce compliance risks or cater to some other set of prioritization criteria. EY analysis suggests that rethinking the traditional financial institution with Gen AI at its core has the potential to create US$200b to US$400b in value by 2030. Additionally, productivity gains could reach up to 30% by 2028, supplementing new revenue opportunities. Many of the enabling technologies required for adaptive AI powered banking already exist.

They are beginning to see early gains in operational cost reductions, significantly improved client onboarding and servicing journeys, as well as dynamic financial crime controls. GenAI  offers tremendous potential for enhancing efficiency, personalisation, and customer engagement in the banking sector. However, it also introduces new cybersecurity risks that must be carefully managed.

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. There might also be a time when it becomes accepted for students to use ChatGPT to aid with schoolwork. Teacher sentiments range from being worried about the technology replacing them to insisting that the in-person classroom connection is essential to education. Currently, ChatGPT can’t automate an entire contact center, but there are many ways it could lighten the workload, such as translating or summarizing customer inquiries. A June 2023 McKinsey report stated that generative AI (GenAI) would automate 60% to 70% of employee workloads.

Join us as we uncover the dark arts of cybercriminals and arm your organization with the knowledge to ward off these digital phantoms. But erecting consistent guardrails and industry standards around model behavior remains paramount. As banks raced to develop generative AI use cases, JPMorgan balanced speed with prudence.

By embracing an integrated approach that emphasizes security by design, ethical development practices and collaborative innovation, banks can harness AI’s full potential to fortify their cybersecurity defenses. This balanced strategy ensures that the sector can navigate the complexities of AI integration, leveraging its capabilities to create a more secure and resilient financial ecosystem. BBVA has taken a firm step toward the future by ChatGPT App expanding its Data University program, which now features new courses on generative artificial intelligence (generative AI). BBVA’s Data University has already impacted more than 50,000 employees in 6 years, of whom nearly 5,000 have completed specialized courses. BBVA is also looking to strengthen its educational ecosystem through partnerships with prestigious institutions such as the University of Navarra, Telefónica and Coursera.

Recognizing these constraints, a significant proportion of survey respondents said they did not believe their institution had the correct technological infrastructure and capabilities to implement GenAI. Discover how EY insights and services are helping to reframe the future of your industry. We are at a historic cusp in time, and we will all need to navigate how GenAI figures in the way we live and work. On DBS’s end, we have in place a governance structure that will help us balance reaping the benefits of Gen AI while managing the risks of a still-emerging field. What’s different with the emergence of GenAI is that we now have the ability to process vast amounts of unstructured data. Coupled with our existing capabilities around structured data, we are well placed to sharpen the outcomes of our current AI use cases while enabling a new class of data-driven use cases.

The aim is for all areas and departments to have access to ChatGPT, so that licensed employees can collaborate with their colleagues in undertaking various projects. In tandem, BBVA will be collecting feedback and suggestions from these users through a multi-country community, with the aim of flagging the most outstanding use cases and sharing best practices. 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. One of the primary challenges of using generative AI in AML/GFC is the “black box” nature of these models.

LLMs in comparison with traditional ML models

Taking treasury functions as example, Hovhannessian pointed out that Apac banks are increasingly asking for quick deployment, open platform, scalability and resilience from their external fintech partners. Quicker adoption, the ability to add in new functions and products at relative ease, as well as high availability infrastructure are what the banks need the most. Hovhannessian said that the adoption of GenAI will, ultimately, lead to cost reduction generated from better-informed decision-making processes and higher efficiency compared to purely manual labour. According to Hovhannessian, a wider adoption of GenAI across banking institutions would not happen before it is integrated into a bank’s core system, a process that requires sophisticated coding, cautious scrutiny and absolute security.

“Strengthening regulations and security for AI will boost trust and investment, integrating AI across functions like customer service, risk management and fraud detection [as well as] redefining the industry’s operations and competition.” GenAI is also enabling banks and financial institutions to automate internal processes as much as possible. This includes areas such as data extraction, incident resolution, or the generation of quick documents and summaries to understand internal policies and procedures — “anything and everything that allows a bank to function day to day,” Sindhu said. This will lead to productivity gains by freeing up staff to do more strategic work.Right now, banks and financial institutions remain more focused on prioritizing internal use cases over customer-facing use cases, she added. They are trying to determine how they can manage risk and the cost-effectiveness of AI systems, how they can demonstrate ROI, and whether these investments are successful, Sindhu said. “These are the three top questions leaders are trying to work around as they scale their GenAI efforts.”

The researchers studied three million conversations between customers and 5,179 customer support agents at a large software company. Some of the agents used an AI chatbot based on OpenAI’s GPT-3 that generated suggestions for how agents should respond to customers as well as links to the company’s internal documentation for technical issues. To get the full value of giving software developers generative AI, banks have to build a “virtuous cycle” around it, said Xavier Lhuer, McKinsey partner, in an interview. At Netguru we specialize in designing, building, shipping and scaling beautiful, usable products with blazing-fast efficiency. AI-powered algorithms have the ability to analyze large volumes of data to detect fraudulent activities by leveraging advanced data processing techniques.

In financial services, LLMs can analyze regulatory documents, generate compliance reports, and provide real-time responses to customer inquiries, enhancing efficiency and accuracy. Advanced AI systems such as large language models (LLMs) and machine learning (ML) algorithms are creating new content, insights and solutions tailored for the financial sector. These AI systems can automatically generate financial reports and analyze vast amounts of data to detect fraud.

The prevalence of sensitive and confidential data in banking raises concerns about accidental data breaches and erroneous transactions. The competing options for deploying AI challenge banks to identify the most impactful initial use cases. Many banks are prioritizing legacy automation capabilities (e.g., robotic process automation) in back-office functions. A clear majority of respondents say their banks are waiting for further development and testing before prioritizing front-office use cases. Horn noted that finance can be a very complex topic, that can be particularly difficult for smaller businesses that might not have the resources to have a dedicated Chief Financial Officer (CFO). With gen AI, he said that complex topics can be translated into an easier-to-understand natural language that can potentially enable a digital CFO capability for an organization.

GenAI’s power to process information and aid decision-making presents an immediate opportunity to automate many of the manual tasks comprising employee workloads. As the banking sector embraces the transformative potential of AI, including the innovative development of GenAI, it is encountering a complex landscape of challenges and opportunities. Tempering the promise of AI to revolutionize banking through growth and innovation is the need to address inherent risks scrupulously. These encompass ensuring data privacy and security, navigating an evolving regulatory landscape, and the meticulous work required to mitigate potential biases and inaccuracies inherent in AI predictions. As the banking sector embraces the transformative potential of AI, acknowledging its inherent limitations becomes crucial.

For Zac Maufe, global head of regulated industries at Google Cloud, gen AI is the catalyst that can potentially help financial services organizations to unlock insights from data better. If AI tools can eliminate much of Wall Street’s entry-level work, it could shake up typical career paths. But only time will tell how this will change Wall Street’s work-till-you-drop culture. In a survey of 780 banking and capital-markets employees by Accenture Research, 62% of respondents expect generative AI to increase people’s stress and burnout. “Algorithmic bias is a major concern as AI systems can perpetuate existing biases from training data. This can lead to unfair treatment in loan approvals, credit scoring or fraud detection,” Sindhu said. “Similarly, lack of transparency and explainability in many AI models complicates regulatory compliance and may erode customer trust.”

Maufe said that many gen AI deployments in financial services are for internal use cases where organizations are using a human in the loop as a control point. He does however see a near-term future where gen AI is even more widespread and prominent in financial services. Along the macro trends, banks have turned to generative artificial intelligence (GenAI), with ambitions of improving customer experience, increasing efficiency and optimising cost and returns ultimately. But for now, much is considered hype and will require more time for concrete developments.

The disruptive power of GenAI extends beyond banking to wealth management, insurance and payments, transforming customer engagement, transaction processing and fraud detection. Strategic advisor mainly within the financial services industry, focused on AI and digital innovation. OpenAI has also agreed to deliver training and provide the latest updates for its large language models (LLMs), the technology on which ChatGPT is built. By working in close partnership with OpenAI, BBVA will drive forward the most successful use cases for the bank’s business and processes. The data-rich financial sector is uniquely positioned to lead the way in responsible adoption.

Additionally, cost optimisation was underlined as one of the key themes for banks globally, with some of them aiming for 10% cost efficiencies over the next 12 months and up to 20% to 30% over the next three years. This is in part due to anticipated rates reductions by the Fed, but also tied to a relatively weak investment banking segment in markets such as Hong Kong, and a greater need to manage credit risks in their loan profiles. “My sense is that there is still an ongoing, sharp increase in AI investment in the broader industry, which I would expect to continue for some time,” said Dan Jermyn, chief AI officer at Commonwealth Bank of Australia. Traditional machine learning (ML) techniques are widely utilized in areas such as fraud detection, loan and credit approval processes, and personalized marketing strategies, Gupta said. AI plays a crucial role in risk management by providing timely alerts, improving risk assessment accuracy, and automating manual tasks.

Banks can use the data to simulate how customers might respond to these new products or to other scenarios, like a financial recession. Some FS firms are already trialing tools in this space, but it may take some time before they are truly enterprise ready. In this crucial area for banks, machine learning algorithms can swiftly analyze patterns in transactions, flagging suspicious activities in real-time. This significantly strengthens security measures and minimizes potential risk for both customers and the bank.

As we are living in volatile times, adaptability is also key for corporate treasurers. Over the past few years, we have seen successful examples of AI usage in finance, e.g. around predictive cash flow forecasting, payment fraud detection through abnormal patterns, and data extraction from trade finance documents. At the same time, many banks and IT vendors are wondering what they can develop in terms of AI.