The Role of Artificial Intelligence (AI) in Wealth Management
AI adoption has made steady progress in recent years. Initial use cases for AI appeared in the back and middle offices of financial service institutions (FSIs), where its ability to sift through troves of data, automate repetitive processes and accurately identify anomalies proved to be extremely valuable. From anti-money laundering (AML) to fraud prevention, AI is supporting financial institutions with streamlining their workflows and increasing service accuracy by enabling employees to focus on exception handling, thereby reducing the risk of human error.
Now, AI adoption is also increasing in the front and investment office, where wealth managers have traditionally been reluctant to integrate AI. At the same time, the attitudes of wealth management clients are shifting towards greater acceptance of AI in areas such as portfolio analysis or optimization. According to a recent survey conducted by Avaloq, among affluent to UHNW investors in Singapore, 64% of respondents would feel comfortable having AI support or fully lead the analysis of their portfolio data, 65% are open to receiving investment advice supported by AI, and 67% are willing to receive AI-assisted product recommendations.
The Current State of AI in the Front Office
The primary benefits of AI in finance are greater operational efficiency, higher relationship manager productivity and better data analysis. Considering these benefits, there are two key areas where AI is already established in the wealth management sector.
The first use case is virtual assistant technology to augment the relationship manager’s role. Smart virtual assistants can support relationship managers, for example, by providing instant suggestions to client requests for account statements, transfers and trade proposals. This works based on natural language processing (NLP) – akin to the technology that powers ChatGPT. The virtual assistant analyses client-adviser communications, understands the client’s requests and suggests the next course of action which the adviser can then recommend. This AI support enables the relationship managers to serve a larger and more diverse client base, while ensuring quicker responses to keep clients engaged.
The second is enhanced client lifecycle management. Wealth managers can employ network analytics to automate prospect mapping, while churn prediction engines can alert relationship managers when a client is at risk of terminating the relationship. Such tools can empower wealth managers to increase their client acquisition rate by up to 20% while preventing client attrition.
Local banks have also embraced the trend towards AI-augmented financial services. A leading Singaporean bank already uses AI as part of an intelligent banking framework. This approach combines predictive analytics, AI/Machine Learning (ML) and client-centric design to transform data into hyper-personalized, intuitive insights that guide clients when performing banking and investment transactions. These insights can in turn help clients better manage their spending, track payments, and make more informed investment decisions.
The Future of AI in Wealth Management
Advances in NLP will help drive conversational banking, such as multi-channel communications between clients and their relationship managers. An exciting future application of this technology is the combination of NLP with a voice-to-text solution. This would enable AI to recommend the best possible course of action in real time. Such actions can include suggesting trade ideas or recapping a meeting.
Balancing Innovation, Fairness and Compliance
With technological progress comes changes to regulation, including guidelines governing the use and ethics of AI. Singapore’s financial regulator, The Monetary Authority of Singapore (MAS), has published a paper on Fairness, Ethics, Accountability and Transparency to promote the responsible deployment and use of AI within the local finance industry. Such guidance is crucial for the industry, especially when dealing with emerging technology such as AI. This guidance will ensure decision-makers can safely and securely roll out AI tools to leverage their vast datasets, and ultimately promote innovation in the financial sector. Over time, as tools such as ChatGPT become more mainstream, individuals will warm to the idea of having such tools incorporated in the investment advisory process.