Showcasing our latest research on GenAI and banking at Sibos 2024

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. Predictability requires rigorous testing and validation of AI models to ensure consistent and reliable outputs. By maintaining transparency and predictability, financial institutions can build trust with regulators, customers, and other stakeholders, demonstrating their commitment to ethical AI practices.

A shift to a bot-powered world also raises questions around data security, regulation, compliance, ethics and competition. Since AI models are known to hallucinate and create information that does not exist, organizations run the risk of AI chatbots going fully autonomous and negatively affecting the business financially or its reputation. You can foun additiona information about ai customer service and artificial intelligence and NLP. AI could drive productivity gains for banks by automating routine tasks, streamlining operations, and freeing up employees to focus on higher value activities. Cognitive assistants can transform how banks and financial institutions interact with their clients. These AI tools offer personalized advice by understanding a customer’s history and preferences. Identifying a use case necessitates substantial effort in prioritization, cost-benefit analysis, and strategic considerations regarding technology and data architecture.

The human element in an AI-driven future

As artificial intelligence (AI) continues to advance, its role in financial crime prevention is growing, with organisations now considering AI as a foundational element in their risk management strategies. When it comes to GenAI specifically, banks should not limit their vision to automation, process improvement and cost control, though these make sense as priorities for initial deployments. GenAI can impact customer-facing and revenue operations in ways current AI implementations gen ai in finance often do not. For example, GenAI has the potential to support the hyper-personalization of offerings, which helps drive customer satisfaction and retention, and higher levels of confidence. Given the newness of GenAI and the limited tech capabilities of many banks, acquisitions or partnerships may be necessary to access the necessary skills and resources. GenAI’s ability to work with unstructured data makes it easier to connect and share data with third parties via ecosystems.

With $7 billion in assets, Maine-based Bangor Savings Bank is already readying itself for the AI-fueled future by focusing on its employees. 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 tech adoption strategy of most incumbents involves adding it on top of existing products or using the new technology to boost productivity.

Key facts

As companies use genAI, the governance framework, which involves human oversight, is crucial in mitigating risk, Ricard said. Today, fintech providers have little choice but to leverage the latest technology at risk of being left behind. Efficiency improvements come from AI’s ability to handle tasks like call transcription, initial data gathering, and summarising claim files, allowing claims handlers to focus on complex cases.

The impact of generative AI on the finance industry is a topic of intense debate among experts. Major financial institutions are rapidly integrating generative AI into their operations. Goldman Sachs has deployed its first generative AI tool across the firm, focusing on market analysis and creating a copilot assistant for investment bankers. JP Morgan has implemented AI in its fraud detection systems, while Bank of America and Capital One are using AI-powered chatbots to revolutionize customer service. Ally Financial has identified more than 450 use cases for generative AI, with applications ranging from transcribing and summarizing contact center calls to recapping earnings reports and conference call transcripts.

Implementing robust data encryption techniques for enhanced privacy, developing explainable AI models for better interpretability, and offering comprehensive training programs to bridge talent gaps are potential solutions to these challenges. Additionally, Generative AI assists in generating synthetic financial data for training predictive models, optimizing portfolio management, and streamlining financial document processing. Collaborate closely with software engineers to seamlessly integrate models into existing software workflows, ensuring UI/UX interaction and enhanced operational efficiency in the finance domain. Autoregressive models, such as autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA), predict future values in a time series based on past observations. Generative AI algorithms develop and implement algorithmic trading strategies by analyzing market data and identifying profitable trading opportunities. This enhances trading efficiency and enables traders to capitalize on market fluctuations in real-time.

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. The aged, heavily-customized technology architectures in place at many banks today, with all their workarounds and poor data flows, are a barrier to AI implementation. 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. Aaron Cirksena, founder and CEO of MDRN Capital, said the real value for Morgan Stanley in this move is getting into deep global analytics insights, building on the hoped-for data consistency through one centralized offering.

The insights and services we provide help to create long-term value for clients, people and society, and to build trust in the capital markets. Neil Jensen of Workday and Jannine Zucker of Deloitte talk on the revolutionary impact of generative artificial intelligence on enterprise operations, digging deep into the insights and best practices for leveraging AI in finance and beyond. DeepBrain AI, a pioneering generative AI company, announced the launch of the latest iteration of its AI Bank Teller complete with next-generation machine learning. Developed in collaboration with Shinhan Bank, this deep learning technology aims to revolutionize banking by creating interactive AI versions of employees to service customers.

• At VentureBeat Transform 2024, attendees will have the opportunity to dive deep into these issues with executives from major financial institutions and tech companies.
• Generative artificial intelligence in finance can analyze vast amounts of regulatory data and provide insights to organizations on how to adapt to regulatory code changes efficiently.
• AML and GFC initiatives are vital for detecting and preventing financial crimes such as money laundering, terrorist financing, and fraud.

Ground rules, in fact, are particularly essential because, as with other emerging technologies, there are risks with GenAI. “Applying GenAI to market analysis can reliably ChatGPT App support and supplement human analysts,” says Lees. “This accelerates their work while detecting trends, and delivering, potentially, more accurate market predictions.”

Opportunity knocks: Unlocking value through financial services transformation

“We need to be able to understand the weights and understand the data that the model is being trained on,” he said. We can help you strategically plan to close GenAI gaps, develop an efficient roadmap, and responsibly harness its capabilities. Sidebar is a member-exclusive section where we discuss stories tangential to the main story above. This week’s sidebar is about the recent applications we have seen of Gen AI from the biggest players in the industry. Banks’ strategies should facilitate their employees’ adaptation to a Gen AI future. For some hardcore proponents of AI, the only way to avoid job loss is to upskill, and people ought to have started yesterday.

We are a talent- and innovation-led company with approximately 750,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. We are uniquely able to deliver ChatGPT tangible outcomes because of our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song. These capabilities, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities.

But banks clearly understand the urgency; a huge majority are already dedicating resources to GenAI.

Which shifts the responsibility from the employer to the employee, the very party most at risk. “It’s almost like you’re talking to a human who has all the knowledge,” Suzanne notes. It has all the intelligence built in, and that’s the real differentiator,” explains Suzanne. “For example, it could hallucinate or produce inaccuracies, and in an industry as heavily regulated as finance, this would spell disaster.” The finance function needs to enable the organisation to become more agile and improve its ability to adapt and transform as generative AI is deployed. “The EU has moved swiftly,” says John Duigenan, “And it’s important that they did because the need for transparent, trusted AI used by enterprises is vast.”

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. Generative AI can also automate time-consuming tasks such as regulatory reporting, credit approval and loan underwriting. 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.