The gen AI skills revolution: Rethinking your talent strategy
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
