BBVA enhances productivity with strategic OpenAI partnership

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. The bank has published the research underpinning this model and made the underlying code available to other organizations, „so that every organization around the world can benefit, and tackle this insidious form of abuse,“ Jermyn said. „The team developed a novel combination of AI techniques to detect and reduce this behavior.“

AI might seem like a recent phenomenon, but its roots date to the mid-20th century. In fact, the term “artificial intelligence” was coined at Dartmouth College in New Hampshire in 1956. The late 20th and early 21st century saw advances in so-called narrow AI, which focuses on a specific subset of problems, versus the broader AI that has consumed the tech space in the past few years. We are going to provide our colleagues access to more detailed career management, give them the opportunity to perform multiple transactions, which they can access through Teams, as part of our interface, or directly.

For many banks, chatbots are now a core component of customer service because of their ability to provide real-time responses to customer inquiries 24/7. Bank of America’s Erica virtual assistant, for example, has surpassed two billion interactions and helped 42 million bank clients since its launch in June 2018. BBVA has commenced the deployment of 3,000 ChatGPT Enterprise licenses to its employees.

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2 KPMG in the US, “The generative AI advantage in financial services” (August 2023). The point is that — if banks were to focus purely on individual siloed use cases and cost outcomes — they would be missing the big opportunities that genAI can deliver. Those only come when you think holistically and focus on outcomes rather than costs. When ChatGPT launched in late November 2022, it took just five days to attract 1 million users. And by January it was estimated to have reached 100 million monthly active users.1 Bankers poured back into the office with dreams of massive productivity improvements and — perhaps — a bit more free time. With bank technology leaders suggest they are inundated with requests from the business for genAI support.

It’s like having a magnifying glass that can see patterns and links that might elude even the most experienced researchers. This ability to analyze vast amounts of data and identify patterns is paving the way for personalized healthcare. By tailoring therapies to an individual’s genetic makeup, we can provide more effective and targeted treatment options.

AI-powered supply chain management tools can track supplies as they make their way through the various links and partners in the supply chain. AI in supply chain management has the potential to improve demand forecasting, inventory evaluation, customer communication, operational performance and even sustainability. AI improves the capability of translation services, enabling automated, real-time translation in multiple languages. Translation requires a certain level of nuance, as translators need to be able interpret body language and emotions of the speaker or in the text they are translating.

These operating models give data scientists in areas like marketing and fraud a consistent way of thinking about problems, doing the work and running machine learning models. Bank technology executives honored on a list of artificial intelligence leaders released Wednesday see no letup in their companies of AI investment and effort, and are focused on practical use cases like fraud detection and personalization. Private-equity firms such as Blackstone are building teams to leverage AI’s cost-cutting and productivity-inducing benefits within the companies they own. The middle-market PE firm Thomas H. Lee, which launched a generative-AI coding tool for select portfolio companies, reported that its engineers were up to 30% more productive just four weeks after the rollout. Recommendations are then delivered in „an interactive, conversational format with lower incremental client servicing costs than human advisers.“ For example, Erste Bank in Austria launched Financial Health Prototype, a customer-facing tool that lets banking customers ask questions about their financial life, such as how can they manage financial debt or plan for a vacation.

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Despite the challenges of transparency, governance, and data privacy, the integration of AI offers substantial benefits in terms of operational efficiency and regulatory compliance. Financial institutions must continue to innovate and adapt to leverage the full potential of AI, ensuring that their compliance programs remain robust, transparent, and effective in addressing evolving regulatory requirements. The integration ChatGPT App of generative AI in AML and BSA programs presents significant opportunities for financial institutions. While challenges remain, particularly around transparency and regulatory compliance, the benefits of enhanced efficiency and improved compliance processes are substantial. AML and GFC initiatives are vital for detecting and preventing financial crimes such as money laundering, terrorist financing, and fraud.

IBM watsonx Assistant helps organizations provide better customer experiences with an AI chatbot that understands the language of the business, connects to existing customer care systems, and deploys anywhere with enterprise security and scalability. Watsonx Assistant automates repetitive tasks and uses machine learning to resolve customer support issues quickly and efficiently. Financial services organizations are embracing artificial intelligence (AI) for various reasons, such as risk management, customer experience and forecasting market trends. FinTech Magazine connects the leading FinTech, Finserv, and Banking executives of the world’s largest and fastest growing brands. Our platform serves as a digital hub for connecting industry leaders, covering a wide range of services including media and advertising, events, research reports, demand generation, information, and data services. With our comprehensive approach, we strive to provide timely and valuable insights into best practices, fostering innovation and collaboration within the FinTech community.

Deutsche Bank, for instance, recently unveiled its seven key use cases for GenAI at June’s Risk Live Europe conference, encompassing everything from digital assistants to automated document processing. These tools promise significant efficiency gains and cost reductions, as highlighted by the bank’s innovation head, Tim Mason. Mastercard’s insights come at a key time too, with banks expected to significantly ramp up their integration of Gen AI into back and front-end services over the next year. 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. AI can provide transparency into increasingly complex and expansive supply chains for manufacturers.

This modernization is essential for maintaining competitiveness and addressing the dynamic requirements of the financial industry. This documentation is essential for regulatory compliance, facilitating audits, and enabling continuous improvement of AI models. By regularly updating documentation and conducting benchmarking tests, financial institutions can ensure their AI systems remain effective, transparent, and compliant with evolving regulations. To address transparency, financial institutions must implement explainable AI techniques that provide insights into how AI models arrive at their decisions. This involves using interpretable models, documenting decision-making processes, and providing clear explanations to stakeholders.

Daniel Pinto, JPMC’s President and COO, recently estimated that gen AI use cases at the bank could deliver up to $2 billion in value. „If an employee can do something more interesting – help that consumer with a more challenging problem better and faster – job satisfaction is certainly going to go up,“ she said. „When you don’t have to do routine, dull parts of a job, people are going to be much more interested in their job and be able to add value. And so the customer in the end is a winner.“

The banking sector is adapting to a landscape sculpted by the six dominant trends of emerging technologies, ecosystem models, sustainability, digital assets, talent acquisition and regulatory adjustments. These forces are compelling the entire sector to evolve beyond traditional boundaries, affecting consumer banking but also reshaping investment, corporate banking and capital markets. In this dynamic environment, GenAI has emerged as a crucial enabler of innovation and transformation, empowering financial institutions to surpass today’s sophisticated client expectations of faster, more convenient and seamlessly integrated services. The recent agreement with OpenAI is a further example of BBVA’s ongoing commitment to generative AI as a key differentiating aspect in the value proposition it offers its customers. “New artificial intelligence tools are going to have a disruptive impact on society as a whole and on the financial industry in particular.

As the banking sector increasingly adopts AI to drive innovation and efficiency, the dual nature of AI’s impact on cybersecurity becomes a critical focal point. Insights from a recent Chief Risk Officer EY survey underscore the paradox of AI in cybersecurity, revealing it as both a potential vulnerability and a formidable tool for enhancing security measures. The insurance sector benefits from more efficient claims processing and risk assessments, as revealed during the EY collaboration with a Nordic insurance company to use AI in automating repetitive tasks in the claims process.

Predictive models play a crucial role in analyzing creditworthiness and determining default probabilities. You can foun additiona information about ai customer service and artificial intelligence and NLP. These models also use historical credit data, such as payment history, debt levels, income, employment history, and other relevant variables, to identify patterns and relationships that are indicative of credit risk. The guidelines come as regulators see increasing interest in GenAI from the banking sector, the HKMA said. In Hong Kong, 39 per cent of the authorised institutions the bank surveyed have already adopted or are planning to adopt GenAI. Community banks are at the edge of a technological revolution driven by increasingly sophisticated artificial intelligence.

The solution streamlined document processing, allowing agents to focus on more complex tasks and improving overall efficiency and customer satisfaction. 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.

• AI can support these groups with voice-activated banking as well as personalized financial advice.
• Across industries, staffing shortages force companies to “do more with less,” leveraging their limited resources for maximum efficiency.
• Although regulators try to keep pace with technological development by issuing nonbinding guidelines, the territory lacks GenAI rules and regulations.

Through GenAI code and tests generation, we expect significant time savings in software development. He told FA that the team is now in talks with a leading Chinese bank in terms of system applications, where “over 80%” of the conversations have been around building a resilient and secure platform. Operating profit before impairment charges for all licensed banks increased by 34.7% to HK$295 billion ($37.8 billion), compared to 2022. Since 2020, the model has blocked about 1 million transactions that contained abusive, threatening or offensive words in descriptions, he said.

Because it is such a nascent field, firms are still trying to figure out the most effective, efficient, and safest way to develop and scale the technology before unleashing it on the masses. „The hours aren’t going to change, because they’re a product of the culture more than actual workload,“ the former junior banker said. „People will always find things for you to do because the expectation is that you work 80 or 100 hours a week.“ Data privacy, security risks and transparency ranked high on the list of the AI issues that board members are digging into, according to a report from EY.

These advancements represent a new frontier where AI intersects with core financial operations, propelling the sector into an era of unprecedented innovation and efficiency. AI is reshaping the banking sector, enhancing efficiency and client engagement, and driving growth. The bank is already handing out licenses at its central services in Spain, and this process will continue in the Group’s other main countries.

That’s why it is critical to draw up a use-case roadmap, including the capabilities shared across use cases and the bottlenecks that might emerge during development and rollout. Some banks that began their generative AI journeys by integrating the technology into lower-risk initiatives that still involve humans are now becoming more ambitious, implementing solutions with greater scale (see Figure 1). For instance, Morgan Stanley conducted a pilot phase of its AI@Morgan Stanley Assistant and subsequently rolled out the offering to financial advisers and support staff. Generative AI is starting to transform the banking sector, changing the landscape for traditional operations and allowing banks to deliver services in innovative ways.

By embracing AI, financial institutions can improve their ability to meet regulatory demands, deliver superior customer experiences, and drive innovation in their operations. The evolution of generative AI turns data into an even more remarkable and valuable asset. Unstructured data, both inside and outside the bank, can now be incorporated to yield valuable insights about customer behavior if banks manage the data effectively.

Bringing customer data to life

Learning from initial quick wins will provide the momentum to move on to higher-value, higher-risk use cases when the organization is ready. It will also set the stage for using GenAI to transform and reinvent business models. Starting off small and driving quick wins will allow banks to assess their capabilities, recognize key challenges and considerations, and assess ChatGPT current and prospective partnerships or acquisitions to further scale. Identifying opportunities to modernize infrastructure, enhance data quality and improve data flows is the critical first step. Banks may need to enhance computing capabilities (e.g., server capacity, data storage and computational power) to deploy AI in bank’s existing tech and data environments.

In the past two years, BBVA added 7,187 professionals in the data and technology field to its workforce – a figure it plans to increase in 2024 with 2,700 new hires. Of this amount, 1,225 will take place in Spain for the bank’s headquarters in Madrid, Bilbao generative ai use cases in banking and Barcelona. 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.

While the efficiency of existing models is rising and the cost of deploying LLMs is dropping, the market continues to see newer, larger and more capable models being deployed. For now, most applications of generative AI and large language models (LLMs) that you may have seen in banks have been limited to lower-risk internal purposes. Train, validate, tune and deploy generative AI, foundation models and machine learning capabilities with IBM watsonx.ai, a next-generation enterprise studio for AI builders. Integrating data-driven AI systems increases the risk of data breaches, requiring continuous monitoring and updates to protect sensitive customer information. Furthermore, AI models rely on accurate and up-to-date data to produce reliable results.

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. 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. Existential risks posed by disrupters and new market forces demand that banks go beyond automation to reimagine banking business models,” says EY-Parthenon Financial Services Leader Aaron Byrne.

In an era where financial institutions are under increasing scrutiny to comply with Anti-Money Laundering (AML) and Bank Secrecy Act (BSA) regulations, leveraging advanced technologies like generative AI presents a significant opportunity. Large Language Models (LLMs) such as GPT-4 can enhance AML and BSA programs, driving compliance and efficiency in the financial sector, but there are risks involved with deploying gen AI solutions to production. Investment banking firms have long used natural language processing (NLP) to parse the vast amounts of data they have internally or that they pull from third-party sources. They use NLP to examine data sets to make more informed decisions around key investments and wealth management.

In manufacturing, AI has long played a critical role in automating repetitive, rote physical tasks. By using AI and robots to automate assembly line tasks such as product assembly, welding and packaging, manufacturers can benefit. Computer vision systems in manufacturing can identify flaws in the product using machine learning and sensor data. AI systems integrated with robots have the potential to increase precision, productivity and quality, reducing downtime on the assembly line and in manufacturing more broadly. Financial services firms are performing better because of technology investments but now they need to fine-tune their digital transformation journeys.

The bank has prioritized around 100 projects to be developed with various tools employing this new technology. Furthermore, BBVA has signed a strategic agreement with OpenAI, the creator of ChatGPT, to deploy this cutting-edge tool among its employees. After years at the forefront of artificial intelligence (AI)-based research and projects, BBVA has taken another significant step forward in the use of generative AI in its main markets. This helps lenders make informed decisions on whether to approve a credit application, set appropriate terms, and manage their overall credit risk effectively.

AI is already replacing jobs, responsible for nearly 4,000 cuts made in May 2023, according to data from Challenger, Gray & Christmas Inc. OpenAI — the company that created ChatGPT — estimated 80% of the U.S. workforce would have at least 10% of their jobs affected by large language models (LLMs). 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. KPMG Trusted AI, is our strategic approach and framework to designing, building, deploying and using AI solution in a responsible and ethical manner so we can accelerate value with confidence.

„Employees with AI skills will replace people without AI skills,“ Andrew Chin, the chief AI officer at the $759 billion money manager AllianceBernstein, told BI. But just as Excel didn’t replace accountants, tech leaders don’t see AI displacing humans. Since most roles in finance include a lot of collecting and processing data, there’s no question that generative AI is set to shake up jobs on Wall Street.

Natural language processing technologies are being used in banking to efficiently and accurately process and analyze large volumes of documents, Gupta said. Here are five areas where AI technologies are transforming financial operations and processes. The tech adoption strategy of most incumbents involves adding it on top of existing products or using the new technology to boost productivity. Startups meanwhile are using new technology to disrupt and unbundle what incumbents do.

“If you’re going to scale your capabilities across multiple lines of business, you’re going to need a research hub,” she said. Retrieval-Augmented Generation (RAG) techniques, which enhance LLMs by integrating external knowledge sources, add another layer of complexity. Effective governance frameworks must be established to manage these sophisticated AI systems.

This integration increases the complexity of AI systems, requiring robust governance frameworks to manage data quality, model performance, and compliance. Addressing the “black box” issue involves implementing explainable AI techniques that provide insights into model behavior and decision-making processes. Financial institutions must invest in research and development to enhance the interpretability of LLMs, ensuring that their decisions are transparent and accountable.