GenAI shakes FIs & startups Shameek Kundu VOX 84 Digital Finance

At the same time, they’re balancing potential use cases with the costs and inherent risks of using AI tools, she noted. It offers a suite of capabilities to innovate the entire life cycle of contracts within banking and enterprises while dealing with their clients. A November 2023 Gartner Inc. survey of 101 procurement leaders found that GenAI was expected to have the most impact on sourcing and contract life cycle management over the next 12 months. In addition, it has looked at stress testing and scenario analysis, where the technology helps simulate stress scenarios and assess their impact on a bank’s balance sheet. 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.

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. As a first step, banks should establish guidelines and controls around employee usage of existing, publicly available GenAI tools and models. Those guidelines can be designed to monitor and prevent employees from loading proprietary company information into these models. Additionally, top-of-the-house governance and control frameworks must be established for GenAI development, usage, monitoring and risk management agnostic of individual use cases.

In investment banking, generative AI can compile and analyze financial data to create detailed pitchbooks in a fraction of the time it would take a human, thus accelerating deal-making and providing a competitive edge. 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.

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. Another significant challenge is the integration of AI technologies within existing banking systems. Many banks operate with legacy systems that might not be compatible with new AI frameworks, which can create costly and time-consuming issues. Ultimately, the goal is to harness the power of GenAI responsibly, ensuring that innovation does not come at the cost of security and customer trust.

While firms are eager to capitalize on their new technology, how they do so is going to dictate the degree of success they will have. Whether their goal is to increase revenue, improve customer service experiences, or bolster organizational decision-making, the opportunity is there. Those who work with the right partner who understands their specific needs and helps them build a comprehensive AI strategy will be the ones who find the most success. When it comes to where banks and lenders are using GenAI sparingly, the results are surprising. Just 29% are using GenAI to identify and track financial crime, while only 28% are using it for fraud detection, and 26% for credit/mortgage lending.

HKMA planning sandbox for generative AI in banking

While GenAI offers several advantages for the banking and FinTech market, it also introduces risks that need to be effectively mitigated, which may have important implications for financial institutions. 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. Identifying opportunities to modernize infrastructure, enhance data quality and improve data flows is the critical first step.

But gen AI could change all that by helping banks enhance the customer experience and drive deeper engagement. Latest market insights and forward-looking perspectives for financial services leaders and professionals. But it must be done within the framework of smart governance and risk management. GenAI can help unlock massive benefits, but only when it is applied smartly, responsibly, and holistically. 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.

There are several actions that could trigger this block including submitting a certain word or phrase, a SQL command or malformed data. When it comes to malicious content delivery, Netskope’s report found that Russian criminal groups are the most likely to target the banking industry – particularly the TA577 and Indrik Spider groups. 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. KPMG firms are excited about AI’s opportunities and equally committed to deploying the technology in a way that is responsible, trustworthy, safe and free from bias.

• AI, particularly generative models, offers solutions to these priorities by automating complex tasks, providing personalized customer interactions, and analyzing vast amounts of data to detect fraudulent activities.
• A key part of this shift in mindset is a readiness to experiment even if this can end up in failure.
• Bahrain and Dubai are positioning themselves as Islamic finance hubs, and applying generative AI would seem a natural progression that could have global implications for the two tech-centered economies.
• This lack of transparency can hinder efforts to justify AI-driven decisions to regulators and stakeholders.

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. Financial institutions must implement robust systems to identify suspicious activities, conduct thorough customer due diligence, and maintain detailed records. The integration of generative AI into these systems can enhance their effectiveness by providing real-time analysis, improving detection capabilities, and streamlining compliance workflows. Generative artificial intelligence (GenAI) opens up game-changing opportunities for banks, from more compelling and personalised customer service to improved operational efficiency and data-driven decision-making.

Hong Kong banks explore GenAI benefits as profits rise

Falcon 2’s array of applications, and its developer’s claim that it is the only AI model with vision-to-language capabilities, makes it probable GCC banks will want to evaluate a homegrown variant. But with GenAI chatbots now available based on OpenAI’s ChatGPT and Alphabet’s Bard, workers can engage and use the latest AI iterations as digital assistants, transforming the way in which banks do business. New opportunities to drive customer engagement, such as gamification, also promise to increase customer retention. Although regulators try to keep pace with technological development by issuing nonbinding guidelines, the territory lacks GenAI rules and regulations.

The material published on this page is for information purposes only and should not be regarded as providing any specific advice, or used by consumers to make financial decision. The third generation of Cora involved reusing those same digital journeys from online and mobile banking in different channels like telephony. This meant customers could contact us via their channel of choice – and instead of queuing to speak to a colleague, they could chat with Cora for help instead. Cora is freeing up time for colleagues to have quality conversations with customers in the moments when they really need that care, empathy and consideration.

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Banks should act and adopt new forms of AI like Gen AI, but it shouldn’t come at the cost of the livelihoods of millions of people or at the risk of building prejudiced systems. The industry in general is still cautious around scaling up GenAI functions in core products, before conducting rigorous security checks and launch of designated modules, he added. “A lot of the banks we talked to are not ready for scalable adoption of GenAI yet, with a lack of adequate data or infrastructure,” he said.

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. The future of financial services lies in the effective integration of AI, and institutions must act now to harness its benefits and stay competitive in a rapidly evolving regulatory landscape. Financial institutions must stay informed about changes in data privacy regulations and adapt their AI strategies accordingly to ensure compliance. By prioritizing data privacy, financial institutions can build trust with customers and regulators, demonstrating their commitment to ethical data practices.

Finance in the experience age heralds a new era for customers and banks alike, with embedded finance the key to success. AI contributes to IT development by assisting in software development processes, from coding to quality assurance. It also aids in modernizing legacy systems, ensuring they remain robust and capable of supporting advanced AI applications. Financial institutions must develop strategies to manage input sensitivity, ensuring that LLMs produce reliable and consistent outputs in compliance scenarios. By enhancing the robustness and reliability of LLMs, financial institutions can mitigate risks and ensure the effectiveness of their compliance programs.

People & Culture

Leveraging gen AI to reinvent talent and ways of working, the top banking technology trends for the year ahead and the mobile payments blind spot that could cost banks billions. Follow him for continued coverage around banks’ tech transformation efforts. Several forces are helping to bring gen AI costs down and this could open the door for banks to … With its ability to process unstructured data, Gen AI solutions could find and put in front of HR managers candidates who may lack traditional banking employment backgrounds — but have much to offer. Notably, these projects are draining time and budgets, with banks spending upwards of $100 million on multi-year payments modernization projects and hiring teams of up to 50+ business analysts to deliver them.

Much has been written about whether generative AI will conform with the familiar technology hype cycle, and if so, whether the Trough of Disillusionment awaits. Fueling much of this debate is the current high cost of deploying, training and using the technology. This group, drawn from various departments within CaixaBank and its technology subsidiary CaixaBank Tech, will spearhead the bank’s efforts to leverage generative AI. This project aims to scale up and implement AI use cases across the entire banking group, building upon the success of its predecessor, GenIAl. 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.

This capability has proved to be a game changer for meeting the challenges today’s banks and capital markets are facing. “The lack of a cohesive and widely accepted cross-border Islamic finance framework leads to complexity and inefficiencies that make multinational financial institutions’ compliance [obligations] especially difficult,” he says. The UAE, which has its own AI university, has taken another technological leap, launching its own open-source, open-access large language model. The latest version, Falcon 2, offers itself as the Gulf’s answer to Google’s and Meta’s GenAI innovations.

KPMG in the US helped one of the world’s largest financial institutions reduce its loan processing time from days to hours.

In the competitive home improvement industry, the biggest players are using AI to get ahead. We’ve uncovered how the top retailers are using AI-powered apps, digital twins and even robots to improve customer experience and drive growth. We strive to uphold the highest ethical standards in all of our reporting and coverage.

We’re seeing how GenAI’s ability to analyse vast datasets and spot anomalies and unusual patterns in real-time can not only boost detection, but also filter out false positives, so bank professionals can concentrate on genuine threats. 3 min read – Solutions must offer insights that enable businesses to anticipate market shifts, mitigate risks and drive growth. Generative AI assistants are an ideal entry point for organizations in the financial and banking sectors looking to gain a foothold in this exciting new world. With help from the IBM Partner Ecosystem, these institutions can effortlessly build assistants that wow customers while boosting the bottom line.

Financial advisors and their clients could use AI-powered simulations to deepen their grasp of complex investment strategies. Gen AI could promise bots capable of responding to customer inquiries in contextually appropriate ways. The image of the bank client trying to bypass a chat system to reach a human operator could become obsolete.

• Identifying opportunities to modernize infrastructure, enhance data quality and improve data flows is the critical first step.
• But the premise that they are displacing traditional banks in the US and Europe is unproven.
• The US Fed’s decision to keep interest rates higher for longer continued to benefit Hong Kong banks’ performance in 2023, with notable increases in net interest margins (NIM) and operating profit.
• With unique insight into a bank’s most resource-heavy functions, risk and compliance professionals have a valuable role in identifying the best areas for GenAI automation.

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, ChatGPT the ability to execute change, new technology capabilities and new skills and talent. Kris Stewart, JD, CRCM, is a senior director in the compliance product management team at Wolters Kluwer.

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AI to improve collaboration with consultancies—Banks commonly work with partners on payment projects. The top five areas AI can improve when outsourcing payments projects are quality of work (46%), payment expertise (44%), long-term vision (40%), speed (38%) and cost (36%). This urgency is reflected in the research, which reveals a substantial 9 in 10 (91%) banks rank payment modernization as either essential or very important.

You can foun additiona information about ai customer service and artificial intelligence and NLP. Another 30% pointed to lack of transparency and accountability, a number that’s slightly higher than other industries. Over half (54%) said that using public and proprietary data gen ai in banking sets has been, or likely will be, an obstacle to implementing GenAI. And nearly as many (49%) said they are experiencing challenges moving GenAI from conceptual to practical.

Bank systems are getting more difficult to manage as banks try new technologies. It means that commercial banks must sharpen their pencils when it comes to liquidity, operational resilience, and understanding how such failures impact their customers – who can now shift their funds with just a few clicks on their mobile phones. Yuen pointed to the March, 2023 collapse of three banks in the US (Silicon Valley Bank, Signature Bank and Silvergate Bank), as well as Switzerland’s shutting down Credit Suisse, as harbingers of new risks to financial stability. Speaking at a conference organized by The Asian Banker, Yuen expressed alarm at the bank failures of March 2023, which demonstrated new risks to financial stability arising from digital innovation.

This, in turn, requires explainability, or in other words, the ability to understand how GenAI arrived at its recommendations, and what inputs and data the technology drew on to do so. The good news is, most financial service organizations already have well-established governance capabilities in place. This provides control over data quality, supports traceability, and can serve to reduce unforeseen bias. Additionally, human staff should oversee AI processes and take action where necessary to address unwanted behaviours or outcomes. Financial institutions are encouraged to embrace AI technologies to stay ahead of regulatory demands and enhance their operational capabilities. By integrating advanced AI solutions like LLMs, banks can ensure robust compliance, improve customer satisfaction, and drive operational efficiencies.

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. We have built these insights in EY.ai platform that combines our vast experience in strategy, transactions, transformation, risk, assurance and tax, with EY technology platforms, ecosystems and leading-edge capabilities. Finally, the EY global alliances network provides MENA financial institutions and FinTech institutions alike with access to proven Gen AI solutions from both large global technology partners, as well as innovative new startups.

These AI systems can handle a wide array of queries, from account information to complex financial advice. Benchmarking AI models involves rigorous testing against standard datasets to evaluate their performance. Continuous documentation and updating of AI models ensure they remain compliant with regulatory standards and perform consistently over time. LLMs like Granite from IBM, GPT-4 from OpenAI, are designed to intake and generate human-like text based on large datasets. They are employed in various applications, from generating content to making informed decisions, thanks to their ability to detect context and produce coherent responses. The summit will feature discussions on building and scaling an AI factory, as well as key use cases like fraud prevention and customer service.

The business case for such deals should be based on a careful assessment of capabilities and with results from initial use cases. Compared with cross-industry averages, banks use GenAI at a higher rate in marketing (47%), IT (39%), sales (36%), finance (35%) and customer service (24%). Beyond the 17% of banking leaders who reported fully implementing GenAI into their business processes, another ChatGPT App 43% indicated they are experimenting with the technology at the enterprise level. Six in 10 said they have deployed at least one GenAI use case to date – the highest of any industry. While the human brain is excellent at reacting to immediate information and making decisions, GenAI can take a bird’s-eye view of an entire information landscape to surface insights hidden to the naked eye.

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The goal is to showcase how leading institutions are using such frameworks while keeping governance and data quality front and centre. The emergence of GenAI will raise the bar for upskilling as so much of the potential centres on increasing workforce capability and productivity. But it could also create incentives by allowing people to reimagine their roles and what they can accomplish within them. Cut through the noise to determine how your particular business can benefit and how this would support your overall strategy – tackling specific paint points or enhancing customer experience, for example. 3 min read – With gen AI, finance leaders can automate repetitive tasks, improve decision-making and drive efficiencies that were previously unimaginable.

In fact, outside the technology and communications sectors, financial services companies (of which banking is a key industry) have sought to hire more AI-related professionals than any other sector (see chart). Organizations must consider when and how employees can leverage GenAI and evaluate the distinct risks of internal and external use cases. 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. Today, banks of all sizes have access to a considerable amount of customer data that’s processed and stored on a daily basis, from credit history to buying activity.

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. In shaping their GenAI strategies and plans, banking leaders must recognize GenAI’s position alongside Web3, blockchain, quantum computing and other disruptive technologies. Long-term roadmaps must reflect how these technologies, when deployed in the right combinations, can transform core business functions (e.g., operations, finance, risk management, product development and sales).

For example, AI can quickly process and summarize large volumes of financial data, generating draft reports and credit memos that would traditionally require significant manual effort. The efficiency of generative AI in summarizing regulatory reports, preparing drafts of pitch books and software development significantly speeds up traditionally time-consuming tasks. This feature improves operational efficiency and reduces manual workloads, allowing teams to focus on more strategic activities.

Moving a cloud platform can provide a fast, accessible and scalable plug-and-play entry point for a range of digital technologies including GenAI. “Regulators could anchor to industry best practices and standards that they consider strong – perhaps presumptive – evidence that the requirements of model risk management frameworks have been met,” Behnaz and Jo Ann say. Both writers emphasise that the technology sector and financial institutions must work together to ensure responsible implementation of these systems.

Which shifts the responsibility from the employer to the employee, the very party most at risk. Operating profit before impairment charges for all licensed banks increased by 34.7% to HK$295 billion ($37.8 billion), compared to 2022. Of course, when outsourcing to third parties, compatibility is needed between third-party software and the often legacy infrastructure employed by banks. With Red Hat solutions like Ansible Lightspeed talking with Advanced Cluster Security or Advanced Cluster Management, threats can be automatically averted to keep systems up and running,” Sasso adds. Yuen said more details about the genAI sandbox will be revealed later this year, noting that it should be ready to admit banks before the end of 2024.