Real world reflections on Gen AI hallucination and risk Legal IT Insider

The process for this verification should be part of a robust risk management process around the use of gen AI. In short, Generative Artificial Intelligence can look to the past to help banks make better financial decisions about the future and create synthetic data for robust analyses of risk exposure. Instead of relying on traditional credit score elements to determine creditworthiness, banks can have machine learning algorithms and AI to analyze vast amounts of data from multiple sources and create a more holistic financial picture of loan applicants.

Banks also need to evaluate their talent acquisition strategies regularly, to align with changing priorities. They should approach skill-based hiring, resource allocation, and upskilling programs comprehensively; many roles will need skills in AI, cloud engineering, data engineering, and other areas. Clear career development and advancement opportunities—and work that has meaning and value—matter a lot to the average tech practitioner. The Cannata Report is the leading source of news and analysis for office technology, business technology, and document imaging industry leaders. “Use large language models to help you understand value positioning or give you competitive analysis,” recommended Walton. He also suggests using AI as an assistant to help sales reps be more in front of customers, listening and attentive, and in the present moment.

In recent news, FinTech startup Stripe announced its integration with OpenAI’s latest GPT-4 AI model, highlighting the growing adoption of advanced AI technologies by financial institutions. This collaboration will enable Stripe to leverage GPT-4’s capabilities to improve various aspects of its services, including fraud detection, natural language processing, and customer support. The partnership exemplifies the transformative potential of generative AI in the banking sector, with numerous applications that can streamline processes, enhance security, and deliver personalized customer experiences. Furthermore, industry leaders are recognizing the value of generative AI in shaping the future of banking.

We can expect roles to absorb new responsibilities—such as software engineers using gen AI tools to take on testing activities—and for some roles to merge with others. Promising experiments that use gen AI to support coding tasks show impressive productivity improvements. Gen AI has improved product manager (PM) productivity by 40 percent, while halving the time it takes to document and code. At IBM Software, for example, developers using gen AI saw 30 to 40 percent jumps in productivity.2Shivani Shinde, “IBM Software sees 30-40% productivity gains among developers using GenAI,” Business Standard, July 9, 2024. Over several decades, banks have continually adapted the latest technology innovations to redefine how customers interact with them.

Processes such as funding, staffing, procurement, and risk management get rewired to facilitate speed, scale, and flexibility. Success in GenAI requires future-back planning to set the vision and a programmatic approach to use-case prioritization, risk management and governance. Banks will need to challenge their current understanding of AI primarily as a technology for back-office automation and cost reduction. Thinking through how GenAI can transform front-office functions and the overall business model is essential to maximizing technology’s return on investment.

The intelligent algorithms scan billions of transactions across millions of merchants, uncovering complex fraud patterns previously undetectable. Moreover, the tool goes beyond the basics, proactively identifying unusual activity, offering smart money moves, and even forecasting upcoming expenses. This customized, proactive approach empowers users to take control of their financial health, reduce stress, and confidently achieve their goals.

Ethical concerns include the potential for biased decision-making, transparency, and the impact on employment. Banks need to adopt responsible AI practices, such as auditing algorithms for fairness, providing explainability, and ensuring human oversight. Compliance with legal and data protection requirements is essential to maintain customer trust and avoid penalties. “It sure is a hell of a lot easier to just be first.” That’s one of many memorable lines from Margin Call, a 2011 movie about Wall Street. And it’s a good summary of wholesale banking’s stance on AI and its subset machine learning. Corporate and investment banks (CIB) first adopted AI and machine learning decades ago, well before other industries caught on.

In finance, any type of error can have a ripple effect, and can leave institutions open to new scrutiny from customers and regulators. It’s worth taking the extra time now to avoid a path that increases the likelihood of these negative outcomes. You can gen ai in banking also use gen AI solutions to help you create targeted marketing materials and track conversion and customer satisfaction rates. Like all businesses, banks need to invest in targeted marketing to stand out from the competition and gain new customers.

The Importance of AI in the Banking Industry

At one institution, a cutting-edge AI tool did not achieve its full potential with the sales force because executives couldn’t decide whether it was a “product” or a “capability” and, therefore, did not put their shoulders behind the rollout. Data quality—always important—becomes even more crucial in the Chat GPT context of gen AI. Again, the unstructured nature of much of the data and the size of the data sets add complexity to pinpointing quality issues. Leading banks are using a combination of human talent and automation, intervening at multiple points in the data life cycle to ensure quality of all data.

Reasons include the lack of a clear strategy for AI, an inflexible and investment-starved technology core, fragmented data assets, and outmoded operating models that hamper collaboration between business and technology teams. What is more, several trends in digital engagement have accelerated during the COVID-19 pandemic, and big-tech companies are looking to enter financial services as the next adjacency. To compete successfully and thrive, incumbent banks must become “AI-first” institutions, adopting AI technologies as the foundation for new value propositions and distinctive customer experiences. AI’s integration into banking represents a major shift from traditional methods to data-driven, automated processes.

Among the financial institutions we studied, four organizational archetypes have emerged, each with its own potential benefits and challenges (exhibit). While gen AI’s capabilities will eventually become more stable and proven, in the short term, companies will need to navigate a great deal of uncertainty. By zeroing in on skills and adapting their talent management approaches, and by being flexible https://chat.openai.com/ enough to learn and adjust, companies can turn their talent challenges into competitive advantages. To ensure that apprenticeship programs succeed, companies should create incentives by making apprenticing part of performance evaluations and provide sufficient time for people to participate. One audio company, in fact, has made apprenticeship an explicit part of its learning program.

Layer 1: Reimagining the customer engagement layer

Financial institutions must ensure that their AI systems are transparent, secure, and aligned with industry standards to maximize the benefits of this transformative technology. By analyzing customer data and then making personalized product recommendations. For example, it can recommend a credit card based on a customer’s spending habits, financial goals, and lifestyle. When powered with natural language processing (NLP), enterprise chatbots can provide human-like customer support 24/7. It can answer customer inquiries, provide updates on balances, initiate transfers, and update profile information. While some financial institutions are adopting generative AI tools at a breakneck pace (though mostly as pilot projects on a small scale), corporate implementation of Gen AI tools is still in its infancy.

These tools can help with code translation (for example, .NET to Java), and bug detection and repair. They can also improve legacy code, rewriting it to make it more readable and testable; they can also document the results. Exchanges and information providers, payments companies, and hedge funds regularly release code; in our experience, these heavy users could cut time to market in half for many code releases. 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.

In the US, the Commerce Department’s National Institute of Standards and Technology (NIST) established a Generative AI Public Working Group to provide guidance on applying the existing AI Risk Management Framework to address the risks of gen AI. Congress has also introduced various bills that address elements of the risks that gen AI might pose, but these are in relatively early stages. Similarly, Singapore has released its AI Verify framework, Brazil’s House and Senate have introduced AI bills, and Canada has introduced the AI and Data Act. In the United States, NIST has published an AI Risk Management Framework, and the National Security Commission on AI and National AI Advisory Council have issued reports. AI will be critical to our economic future, enabling current and future generations to live in a more prosperous, healthy, secure, and sustainable world.

The tool is designed to assist with writing, research, and ideation, boosting productivity and enhancing customer service. By keeping all information within the bank’s secure environment, OCBC ensures data privacy while empowering its workforce with advanced AI capabilities. With this support, consumers make informed decisions and choose the card that best suits their needs. Ultimately, AI-powered systems provide a convenient and efficient way for customers to find answers to all of their questions. The adoption of Generative AI in the banking industry is rapidly gaining momentum, with the potential to fundamentally reshape numerous operations. Let’s examine the top applications where this technology is making the most significant impact.

Overall, the switch from traditional AI to generative AI in banking shows a move toward more flexible and human-like AI systems that can understand and generate natural-language text while taking context into account. This is instrumental in creating the most valuable use cases in both customer service and back-office roles. In banking, this can mean using generative AI to streamline customer support, automate report generation, perform sentiment analysis of unstructured text data, and even generate personalized financial advice based on customer interactions and preferences. Generative AI-driven tools can also evaluate historical data, market trends and financial indicators in real time. This ability enables accurate risk assessments, aiding banks in making more informed decisions regarding loan applications, investments and other financial operations.

In a world ruled by algorithms, SEJ brings timely, relevant information for SEOs, marketers, and entrepreneurs to optimize and grow their businesses — and careers. Relatives and parents are sources of financial advice for 41% of Gen Zers, whereas 17% of them turn to friends for money advice. According to the survey from Insurify, here’s the breakdown of what sources Gen Z uses for financial advice. This video of the new series looks at the arrival of a new generation of AI-powered smartphones and computers. The advances they offer could power a surge in consumer demand and investment opportunities. While this is not the most widely recognized example of GenAI in banking, it goes to show the many Generative AI use cases in banking that have unintended, but impactful, consequences.

Ignoring challenges or underinvesting in any layer will ripple through all, resulting in a sub-optimal stack that is incapable of delivering enterprise goals. In 2014 he co-founded Procertas, a competency-based technology training program to improve lawyers’ use of Word, Excel, PDF and PowerPoint. Speaking to people who are using and testing Gen AI tools on a regular basis, it seems clear that one of the practical challenges for organisations is in getting users to understand how to use Gen AI tools and what their limitations are. Legal research was always going to be one of the most challenging nuts to crack, although that doesn’t take away from the fact that the progress being made in that area is significant. In a year of big advances for legal Gen AI tools, it is nonetheless clear that Stanford University’s controversial paper on hallucination continues to cast a long shadow over product updates and new releases. Hallucination – or, put very simply, making stuff up – is not new to us in this fast-moving post Gen AI world, but buyers and prospective buyers of new tools are in many cases struggling with how to put the risk in context.

By leveraging machine learning, natural language processing, and other AI technologies, banks can enhance operational efficiency, improve customer service, and manage risk more effectively. The transformative power of AI in banking is evident in its wide-ranging applications, from fraud detection to personalized financial advice. In the future, generative AI will play a pivotal role in shaping financial services by enabling predictive analytics for risk management, enhancing credit scoring systems, and offering customized financial advice. Furthermore, the integration of generative AI with existing banking systems will streamline operations, reduce costs, and improve decision-making processes.

You can foun additiona information about ai customer service and artificial intelligence and NLP. In the EU, there are enabling mechanisms to instruct regulatory agencies to issue regular reports identifying capacity gaps that make it difficult both for covered entities to comply with regulations and for regulators to conduct effective oversight. For the past few years, federal financial regulatory agencies around the world have been gathering insight on financial institutions’ use of AI and how they might update existing Model Risk Management (MRM) guidance for any type of AI. We shared our perspective on applying existing MRM guidance in a blog post earlier this year. Understanding the future role of gen AI within banking would be challenging enough if regulations were fairly clear, but there is still a great deal of uncertainty. As a result, those creating models and applications need to be mindful of changing rules and proposed regulations. If not developed and deployed responsibly, AI systems could amplify societal issues.

• Ignoring challenges or underinvesting in any layer will ripple through all, resulting in a sub-optimal stack that is incapable of delivering enterprise goals.
• EY refers to the global organization, and may refer to one or more, of the member firms of Ernst & Young Global Limited, each of which is a separate legal entity.
• 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.
• This archetype has more integration between the business units and the gen AI team, reducing friction and easing support for enterprise-wide use of the technology.
• Global, multi-disciplinary teams of professionals strive to deliver successful outcomes in the banking sector.

One year later, banking has moved from the question of whether the technology will change banking to where we should start and what the ultimate impact will be. 2 KPMG in the US, “The generative AI advantage in financial services” (August 2023). Financial services firms are performing better because of technology investments but now they need to fine-tune their digital transformation journeys. KPMG in the US

The generative AI advantage in financial services

(August 2023). 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. With bank technology leaders suggest they are inundated with requests from the business for genAI support.

Emerging applications of gen AI in risk and compliance

QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe. One example includes a life sciences company that is working to use an AI skills inferencing tool to create a comprehensive skills view of their digital talent. The tool scans vacancies, role descriptions, HR data about roles, LinkedIn profiles, and other internal platforms (for example, Jira, code repositories) to develop a view on what skills are needed for given roles. The relevant individual employee can then review and confirm whether they have those skills and proficiencies.

Additionally, take note of how forward-looking companies like Morgan Stanley are already putting artificial intelligence to work with their internal chatbots. With OpenAI’s GPT-4, Morgan Stanley’s chatbot now searches through its wealth management content. This simplifies the process of accessing crucial information, making it more practical for the company. Asset management was slower to embrace the transformational

power of technology.

The many banks that need to update their technology could take the opportunity to leapfrog current architectural constraints by adopting GenAI. However, for GenAI to be useful in the workplace, it needs to access the employee’s operational expertise and industry knowledge. 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. 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.

Convolutional natural network is a multilayered neural network with an architecture designed to extract increasingly complex features of the data at each layer to determine output; see “An executive’s guide to AI,” QuantumBlack, AI by McKinsey, 2020. But scaling gen AI will demand more than learning new terminology—management teams will need to decipher and consider the several potential pathways gen AI could create, and to adapt strategically and position themselves for optionality. At this very early stage of the gen AI journey, financial institutions that have centralized their operating models appear to be ahead. About 70 percent of banks and other institutions with highly centralized gen AI operating models have progressed to putting gen AI use cases into production,2Live use cases at minimal-viable-product stage or beyond. Compared with only about 30 percent of those with a fully decentralized approach.

This includes lower costs, personalized user experiences, and enhanced operational efficiency, to name a few. Given the nature of their business models, it is no wonder banks were early adopters of artificial intelligence. Over the years, AI in baking has undergone a dramatic transformation since machine learning and deep learning technologies (so-called traditional AI) were first introduced into the banking sector. With the release of Python for Data Analysis, or pandas, in the late 2000s, the use of machine learning in banking gained momentum. Banking and finance emerged as some of the most active users of this earlier AI, which paved the way for new developments in ML and related technologies. When it comes to technological innovations, the banking sector is always among the first to adopt and benefit from cutting-edge technology.

It ran a boot camp covering gen AI skills for about a dozen top-performing engineers who volunteered for the program. Each agreed to lead a three- to four-day boot camp for ten to 15 engineers, followed by two sessions per week for three months, in which anyone could ask questions and share their own learnings. Given the unproven and unpredictable nature of gen AI over the short term, new roles will be needed, such as one that focuses on AI safety and data responsibility and that also reviews and approves code. Other areas of significant scope that could require new roles may include LLM selection and management, gen AI agent training and management, third-party model liability, and LLM operations (LLMOps) capabilities to oversee model performance over time.

The answer to that question could be decisive for the future of many companies. Intel says the new processors will be rolling out with new models by the end of this month. The company claims the Lunar Lake series offers the fastest CPU, best built-in GPU and best AI performance to top it off. It even claims the battery life on the new Intel processors will be longer than what Qualcomm and AMD offer. Intel is gearing up for the long AI PC battle against Qualcomm and AMD with its new Lunar Lake or Core Ultra laptop processors.

How generative AI can speed financial institutions’ climate risk assessments

Centralized steering allows enterprises to focus resources on a handful of use cases, rapidly moving through initial experimentation to tackle the harder challenges of putting use cases into production and scaling them. Financial institutions using more dispersed approaches, on the other hand, struggle to move use cases past the pilot stage. We have found that across industries, a high degree of centralization works best for gen AI operating models. Without central oversight, pilot use cases can get stuck in silos and scaling becomes much more difficult. Looking at the financial-services industry specifically, we have observed that financial institutions using a centrally led gen AI operating model are reaping the biggest rewards.

Generative Artificial Intelligence can also educate on other financial tasks and literacy topics more generally by answering questions about credit scores and loan practices—all in a natural and human-like tone. Elevate the banking experience with generative AI assistants that enable frictionless self-service. For example, today, developers need to make a wide range of coding changes to meet Basel III international banking regulation requirements that include thousands of pages of documents.

• By continuously analyzing data patterns and trends, AI systems can identify potential risks and provide early warnings, allowing banks to take preventive measures and mitigate potential losses.
• But because gen AI moves quickly and there is little clarity about which skills will be needed, upskilling will need to be front and center.
• This ensures that gen AI–enabled capabilities evolve in a way that is aligned with human input.
• Karim Haji, Global Head of Financial Services, outlines why it’s such an exciting time for the financial services industry.
• To make this move, risk and compliance professionals can work with development team members to set the guardrails and create controls from the start.

These AI capabilities help banks optimize their financial strategies and protect themselves and their clients. Gen AI certainly has the potential to create significant value for banks and other financial institutions by improving their productivity. But scaling up is always hard, and it’s still unclear how effectively banks will bring gen AI solutions to market and persuade employees and customers to fully embrace them. Only by following a plan that engages all of the relevant hurdles, complications, and opportunities will banks tap the enormous promise of gen AI long into the future. Just as the smartphone catalyzed an entire ecosystem of businesses and business models, gen AI is making relevant the full range of advanced analytics capabilities and applications.

And to do that, you must always improve customer service and invest in creating a good customer experience. How a bank manages change can make or break a scale-up, particularly when it comes to ensuring adoption. The most well-thought-out application can stall if it isn’t carefully designed to encourage employees and customers to use it. Employees will not fully leverage a tool if they’re not comfortable with the technology and don’t understand its limitations. Similarly, transformative technology can create turf wars among even the best-intentioned executives.

In order to fully harness the potential of advanced AI models, traditional banks must collaborate with FinTech startups, which are often at the forefront of innovation. These partnerships can help banks accelerate their AI adoption, drive new product development, and enhance their service offerings. By continuously analyzing data patterns and trends, AI systems can identify potential risks and provide early warnings, allowing banks to take preventive measures and mitigate potential losses.

To offer investors and traders answers to bond-related questions, insights on real-time liquidity, and more. We’ve reached an inflection point where cloud-based AI engines are surpassing human capabilities in some specialized skills and, crucially, anyone with an internet connection can access these solutions. This era of generative AI for everyone will create new opportunities to drive innovation, optimization and reinvention. As financial fraud becomes increasingly sophisticated, banks need to invest in advanced technologies to stay one step ahead of the criminals. Generative AI offers unparalleled capabilities in detecting and preventing fraudulent activities. By analyzing large datasets and identifying patterns that may indicate fraud, AI-driven systems can quickly detect anomalies and alert banks to potential threats.