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To choose the operating model that works best, financial institutions need to address some important points, such as setting expectations for the gen AI team\u2019s role and embedding flexibility into the model so it can adapt over time. That flexibility pertains to not only high-level organizational aspects of the operating model but also specific components such as what is the completed contract method<\/a> funding. We recently conducted a review of gen AI use by 16 of the largest financial institutions across Europe and the United States, collectively representing nearly $26 trillion in assets. Our review showed that more than 50 percent of the businesses studied have adopted a more centrally led organization for gen AI, even in cases where their usual setup for data and analytics is relatively decentralized. This centralization is likely to be temporary, with the structure becoming more decentralized as use of the new technology matures. Eventually, businesses might find it beneficial to let individual functions prioritize gen AI activities according to their needs.<\/p>\n<\/p>\n As shown in Table 4, 73 (out of 110) papers explicitly refer to some theoretical framework. Specific theories concerning types of neural networks and learning methods are used too, such as the fuzzy set theory, which is mentioned in 8% of the sample, and to a lesser extent, the Naive Bayes theorem, the theory of neural networks, the theory of genetic programming and the TOPSIS analytical framework. Finance theories (e.g. Arbitrage Pricing Theory; Black and Scholes 1973) are jointly employed with portfolio management theories (e.g. modern portfolio theory), and the two of them account together for 21% (15) of the total number of papers. Finally, bankruptcy theories support business failure forecasts, whilst other theoretical underpinnings concern mathematical and probability concepts. The nascent nature of gen AI has led financial-services companies to rethink their operating models to address the technology\u2019s rapidly evolving capabilities, uncharted risks, and far-reaching organizational implications.<\/p>\n<\/p>\n For many banks, ensuring adoption of AI technologies across the enterprise is no longer a choice, but a strategic imperative. Envisioning and building the bank\u2019s capabilities holistically across the four layers will be critical to success. Over several decades, banks have continually adapted the latest technology innovations to redefine how customers interact with them.<\/p>\n<\/p>\nA detailed account of the literature on AI in Finance<\/h2>\n<\/p>\n
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