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      "text": "A huge abundance of information that is underutilized by large swathes of the industry\nA highly competitive environment where asymmetric information and insights are always urgent and can bring large rewards\nA deep understanding of active risk management allows for management of risks associated with GenAI\nThe possibility to directly attribute revenues from the investment process to insights or productivity gains extracted by GenAI",
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      "text": "Wealth Management: Content generation/personalization, information synthesis\nBuy side: Data analysis and AI assistants for analysts\nSell side: Synthetic data creation, workflow automation, and error detection\nFMIs: AI assistants, data analysis, workflow automation",
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      "text": "Celent has identified several distinct use cases within banking and capital markets. Unsurprisingly, these use cases will become more impactful as the GenAI frontier is pushed out in the second and third waves of adoption. Banking has several key areas — credit, payments, and cash management — in which GenAI could unlock transformative services and customer journeys. Capital markets have an entire value chain link that is tailormade for GenAI — trading and investment management — which has abundant use cases. Celent anticipates that use cases will vary across the highly differentiated types of participants in capital markets.",
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      "text": "The capital markets sector is in a unique position because it includes trading and investment management, which has tremendous potential for GenAI because of:",
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      "text": "As a result, there are many capital markets-specific use cases for GenAI, including data monetization, understanding and predicting market information, investment decision-making, workflow optimization, and insight personalization. While the sector will generally see a high impact from adopting GenAI, results will vary by user and area. Examples of high-impact use case areas by participant type include:",
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      "text": "While many inroads have been made to digitize retail banking, there remains room for improvement. Credit, particularly complex products like mortgage loans, is a prime example. Employees could better serve customers via an AI assistant that suggests responses and next steps and pulls relevant information. In addition, synthetic data generation could improve a bank's ability to understand customer needs and tailor products and processes accordingly. Customer-facing use cases are also concentrated in the area of credit. The credit process remains relatively arduous for consumers, meaning an AI assistant that can guide them and expedite the process would be highly beneficial. GenAI could also greatly expand banks' role in consumer financial wellness. Depending on a customer's bank and third-party permissioned data, an AI assistant could help advise a customer on scenarios like creating a budget or handling an unexpected expense. Corporate banking involves many complex processes, myriad systems, and extensive integrations. GenAI could streamline existing paper-based processes and digital bottlenecks. AI assistants that further automate processes (client onboarding and credit processes) will drive competitive advantage. AI assistants could also play a more transformative role by helping banks develop customized products for key clients. Within their core payments business, banks could deliver more \"intelligent\" services by leveraging AI assistants for not only queries but also higher-value error prevention, detection, and correction. Banks could greatly improve their support of corporations by providing clients with AI assistants to further digitize and automate their financial supply chain. In the long run, GenAI could be the key to delivering \"self-driving\" treasury.",
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