Hong Kong Bank Regulator Updates GenAI Guidelines Global Finance Magazine

The assessment allows the Accelerating Insights initiative to take a more role-based approach, with some roles receiving more technical training than others, according to Bangor’s Director of Strategic Initiatives, Sandra Klausmeyer. You can foun additiona information about ai customer service and artificial intelligence and NLP. With $7 billion in assets, Maine-based Bangor Savings Bank is already readying itself for the AI-fueled future by focusing on its employees. A multinational company adopted our AI contract review platform to streamline contract negotiations, allowing it to compare contract terms against the company’s predefined legal policies. This significantly sped up the review process and reduced the time to finalize agreements by 80%.

As such, leveraging AI to support cybersecurity is an area Red Hat works closely with its customers. “We’re starting to help them work with some of these newer AI-based tools,” notes Sasso. This includes AI-based creditworthiness assessments by banks, as well as pricing and risk assessments, meaning banks must comply with heightened requirements for such AI applications. “When it comes to Gen AI, there’s still constant innovation coming across,” Harmon says. “For banks today, it’s about understanding how and where they can best apply Gen AI while making sure they are collaborating with regulators to evolve the regulations in this space.

Risk Management And Compliance

Model benchmarking provides a standardized approach to evaluating AI performance, ensuring that models meet regulatory and operational standards. Documentation involves maintaining detailed records of model development, training, validation, and deployment processes. The summit promises to bring together banking leaders, fintech pioneers, and AI experts who have successfully implemented AI-driven solutions in areas like fraud detection and data enrichment. In the mid- and back-offices, the benefits include tackling some of the labour-intensive pain points that raise costs and tie-up time that could be more valuably used elsewhere. Properly deployed technology can reduce the overall cost of compliance by 30%-50%, for example, with specific benefits in areas ranging from workflows and reporting to data-driven decision-making. The paper suggests that financial institutions should implement specific controls for AI systems, including monitoring protocols and human oversight.

After the COVID-19 pandemic sent the adoption of virtual agent technology soaring, companies are now discovering how adding generative AI into the mix can pay dividends. Forward-thinking organizations can remove friction from customer self-service experiences across any device or channel, driving up employee productivity and enabling adoption gen ai in banking at scale. The banking industry is currently experiencing a lower adoption of Gen AI (87%) compared to other industries (97%) due to stricter control measures to reduce the risk of data leakage. According to a recent report released by Netskope Threat Labs, phishing is one of the most common cybersecurity threats in the banking industry.

Leveraging GenAI in banking

As generative AI is integrated into our everyday lives and workplaces, understanding its practical implications is crucial for banks, payments companies, and fintechs aiming to stay competitive and relevant. Companies like Hummingbird, Reality Defender, Ntropy, and SQream will showcase their AI solutions with real-world examples and practical applications. Chris’ comments are representative of a growing consensus that banks must navigate AI implementation carefully. The view is that AI must be regulated across the board, but especially in such a significant (and sometimes volatile) sector. If you’d like to know more about how GenAI could benefit your bank and how to realise the potential, please feel free to get in touch. As a result, you not only need to make sure the initial data sets and populations are right first time, but also keep prompting, checking and re-prompting the AI as part of a continuous cycle of input and output.

For instance, in financial services, they can generate detailed reports, summarize regulatory documents, and predict potential compliance issues based on historical data patterns. 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 ChatGPT 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). This data-centricity has been a reason why banks have been among the most prolific adopters of AI and other digital technologies.

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. The versatility of LLMs enables their application in diverse areas such as automated report generation, customer service chatbots, and compliance document analysis. Their ability to process natural language and generate contextually relevant outputs makes them ideal for successfully performing tasks that require subjectivity and producing human-like text.

By implementing mitigation strategies, financial organisations can balance leveraging the benefits of GenAI and maintaining robust cybersecurity measures. This approach will help safeguard customer data, maintain trust, and drive sustainable innovation in the digital banking landscape. 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.

In the past five years, we have scaled our AI capabilities to make it pervasive across all parts of the bank, delivering tangible outcomes of S$370m for DBS in 2023, more than double that of the previous year. We are confident of growing the economic impact of our AI initiatives in the coming years, affording us greater flexibility to navigate through business and economic cycles. For banks and lenders to overcome the current barriers and fully embrace AI, there needs to be a holistic strategy that can be incorporated on an organization-wide level. And while some banks and lenders have made these integrations to varying degrees of success, others are struggling to fully embrace this next technological chapter. She said she reminds those with whom she works to “lean on concepts and frameworks” that they’ve already built. The banks top a list of the largest banks in terms of AI talent, innovation and leadership.

• Generative Artificial Intelligence (GenAI) is transforming the banking sector, providing innovative solutions that optimise efficiency, enhance security, and increase customer satisfaction.
• In financial services, this adaptability allows LLMs to handle diverse tasks such as compliance monitoring, customer service, and risk assessment with minimal reconfiguration.
• Will it simply cut costs or enable entirely new business and operating models for banking?
• These include tokenization, virtual products and digital wallets, electronic transactions, straight-through transaction processing and product accounting, as well as sophisticated cloud-based risk and financial crime detection models.
• As certain costs have fallen, regulatory burdens have grown, and it has become more expensive to attract and retain customers.

Banks are no strangers to technological change and disruption, and they have a long tradition of investing heavily to keep pace with their peers and emergent fin-techs. While this has helped reduce some costs, banks have seen little benefit in their cost-to-income ratios. As certain costs have fallen, regulatory burdens have grown, and it has become more expensive to attract and retain customers. It’s also critical to adhere to a framework that establishes guard rails to govern how GenAI is used.

Concurrently, in Singapore, we worked with the Monetary Authority of Singapore as part of the MindForge consortium to develop a whitepaper that examines the risks and opportunities of GenAI for the financial sector. 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. 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.

• PwC’s AI Jobs Barometer has found that sectors with the most exposure to AI, which include FS, are seeing 4.8 times greater growth in labour productivity.
• But from the regulator’s perspective, this is a risk, as banks are now dependent upon external vendors.
• These lapses can not only cause severe reputational damage, but also lost opportunity costs.
• 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.
• By automating and accelerating the extraction of key information from customers’ loan application documents, loan officers are now able to make faster, more accurate and informed decisions in approving loans.
• With help from the IBM Partner Ecosystem, these institutions can effortlessly build assistants that wow customers while boosting the bottom line.

Enterprising fintech innovators are recognizing the potential for generative AI to create compelling new service offerings for their customers. One such case is Asteria, an IBM Business Partner based in Stockholm, Sweden. They teamed with IBM Client Engineering to build Asteria Smart Finance Advisor, a new virtual assistant based on IBM watsonx Assistant, IBM Watson® Discovery and IBM® watsonx.ai™ AI studio. Insurance can be complicated, and customers naturally want things to be as simple as possible when they interact with providers. Generali Poland, which offers comprehensive insurance services, recognized that its customer consultants were spending most of their time repeatedly fielding basic queries and managing straightforward claims and policy changes.

Gold mine of data

Ensuring compliance with diverse regulatory requirements is critical when deploying AI solutions that process sensitive financial data. Regulators require financial institutions to implement robust governance frameworks that ensure the ethical use of AI. This includes documenting decision-making processes, conducting regular audits, and maintaining transparency in AI-driven outcomes. Compliance with these regulations involves providing clear explanations of AI model decisions, ensuring data privacy, and implementing safeguards against biases and discriminatory practices.

Arthur Yuen, deputy CEO of Hong Kong Monetary Authority, says the territory’s central bank is preparing to open a regulatory sandbox focused on how financial institutions may use generative artificial intelligence. We’re starting to experiment with it to help customers complete service-related tasks, but it could also help them to manage their money, plan for the future and understand what NatWest can do to help them with those goals. For example, how can GenAI be used to help make the handover from Cora to a colleague as slick as possible?

Across industries, staffing shortages force companies to “do more with less,” leveraging their limited resources for maximum efficiency. Financial institutions are certainly not excluded from this struggle, and resource constraints may be even more pressing as some of the largest banks strive to process millions of transactions each day. GenAI’s power to process information and aid decision-making presents an immediate opportunity to automate many of the manual tasks comprising employee workloads. Whether it’s in building better internal processes or serving clients, banks and lenders must find the right way forward that serves their unique organizational needs in a truly diverse financial services landscape.

AML policies are designed to prevent criminals from disguising illegally obtained funds as legitimate income. Similarly, GFC encompasses a broad set of regulations aimed at ensuring financial ChatGPT App institutions operate within the legal standards set by regulatory bodies. Compliance with these regulations is crucial to avoid hefty fines and maintain the trust of stakeholders.