Generative artificial intelligence applied to wealth and asset management: The craft of possibilities
By analyzing daily news, trading activities, and recommending real-time portfolio adjustments, Generative AI (GenAI) is poised to revolutionize asset and wealth management. Within the next six to twelve months, 50% of global business executives are anticipated to allocate budgets for GenAI, thereby expediting its adoption.
Below are the four areas in which GenAI could have the greatest impact, as identified by asset and wealth managers.
Nevertheless, investment firms encounter a variety of obstacles, including the legality of GenAI outputs, the threat of data privacy, the scarcity of qualified talent, and the dangers associated with investment costs. In order to mitigate these risks, asset and wealth management companies must implement a multidisciplinary strategy that encompasses representatives from cybersecurity, IT, legal, compliance, and risk.
Wealth and asset management institutions are progressively incorporating artificial intelligence (AI) into their primary business operations. Generative AI (GenAI) is expected to provide exceptional performance in unstructured content information search, retrieval, and synthesis tasks, as well as content generation capabilities. The C-suite has been motivated to imagine how GenAI will disrupt business value chains, position enterprises for the future, and generate human-friendly responses by its capacity to process large quantities of information and synthesize within context. This has the potential to create value for all stakeholders. Service operations, sales and marketing, legal and risk, and technology are all affected by GenAI, with early benefits resulting from the optimization of operational processes and efficiency enhancements. Nevertheless, the personalization of user engagement and the enhancement of the consumer self-service experience will result in substantial value creation.
Wealth Management
Financial advisors (FAs) are being offered intelligent insights, improved productivity, and an improved experience through the reimagining of advisor desktops by wealth management. Firms are being led to reconsider their GenAI strategy by market forces, as wealth clients are more inclined to collaborate with FAs who are more adept at utilizing AI during client engagement and practice management. The deployment of GenAI-powered solutions will result in a greater level of meaningful client engagement, business growth, new client acquisition, and an increase in the revenue share of existing clients. The integration of CRM platforms to provide next-best-action recommendations will enhance productivity, meeting preparation, and lead generation by automating manual administrative tasks, as GenAI allows for this.
Asset Management
With the implementation of AI, asset management is experiencing a substantial transformation, which includes the implementation of advanced analytics for portfolio optimization, asset allocation, algorithmic trading, and risk management. GenAI can assist asset managers in the processing of substantial quantities of unstructured information, offering curated insights and market intelligence in real time. By continuously monitoring market conditions and providing early signals, GenAI can proactively manage risks. It can also enhance client engagement and reduce costs by implementing dynamic electronic know-your-customer (e-KYC) on self-service channels or bot-assisted client onboarding. GenAI's capacity to generate custom products or content, automate compliance review, and generate personalized client insights can be advantageous to asset management and wealth management sales, marketing, and distribution (SMD) functions. GenAI models can be incorporated with market attribution models and open-source data to produce tailored meeting preparation and targeted marketing.
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Retirements
The retirement industry, which presently depends on large contact centers to manage retirement plan inquiries and intricate transactions, could be substantially disrupted by the adoption of GenAI. The retirement industry's complexity is further exacerbated by the prevalence of legacy systems, the complexity of jurisdictional regulations, and the variety of investment options and plans. The cost of service is frequently greater than the service fees as a result of these factors. GenAI has the potential to improve service operations in the near future by digitizing downstream processes or providing knowledge support. Advisors may receive assistance from virtual assistants and screen aides during intricate interactions in the medium term. Finally, GenAI has the potential to facilitate the digital processing of the majority of transactions and fulfillment requests by self-driving operations.
GenAI: The Technology Impact
In order to optimize the use cases and return on investment (ROI) of GenAI, organizations must align their enterprise AI strategy with it. This entails the establishment of comprehensive operational model playbooks, the definition of use cases, the quantification of performance indicators, and cross-team collaboration across business divisions.
In order to deploy GenAI, it is essential to invest in infrastructure that can scale capabilities, such as cloud-based platforms, computational resources, software frameworks, or vendor partnerships. Additionally, it is imperative to establish a comprehensive large language model operations process that includes improved monitoring capabilities. Firms should prioritize risk and governance, and they should update their enterprise legal, risk, and compliance (LRC) policy and framework. Firms should evaluate their exposure to legal, reputational, and financial risks, as well as the increased risks associated with the use of large language models, by benchmarking against extant AI guidance, such as the Federal Reserve SR 11-7 (Guidance on Model Risk Management).In order to guarantee sustainable access to the data inputs required for GenAI models, financial institutions must also enhance existing data assets and update data standards.
For the responsible deployment of AI, it is imperative to have data that is sustainable, reliable, and accurate. The current enterprise data management strategy must be revised to accommodate emergent risks, including copyright infringement. Firms will persist in their pursuit of GenAI models that are specifically designed for a particular domain or use case as GenAI capabilities develop. They have the option of selecting from a variety of closed or open-source large language model options and vendor products.
Firms should optimize for operating costs, risks, and performance while exploring and defining fit-for-use patterns. Firms can maintain their competitive edge by establishing centers of excellence or AI laboratories. Workforce readiness is an additional essential component of the GenAI technology adoption process. Firms can anticipate performance enhancements in GenAI solutions by improving the quality of prompts, upskilling end users, and constructing technical capacities to deploy GenAI-enabled applications in order to optimize the ROI of GenAI investments.
Conclusion
Although wealth and asset management firms are incorporating AI into their primary operations, the maturation of use cases for generative AI (GenAI) is still in the process of evolving. In order to increase adoption, financial institutions require an integrated enterprise-wide strategy. Wealth and asset managers should exercise caution when deploying GenAI in a highly regulated sector, taking into account potential hazards such as the cost of building and operating solutions, as well as the application of GenAI too broadly and prematurely. Before implementing GenAI at scale, financial institutions should adopt a pragmatic approach, acquire experience with no regrets use cases, and develop a robust risk and governance framework.