Generative AI has the potential to completely transform the way financial businesses operate and connect with consumers.
As the buzz surrounding robo-advisers spreads into private banking, the subject of how relationship managers' (RMs’) roles will evolve becomes more pressing. It has become evident that a future without RMs is unlikely. However, numerous proposals suggest the need to augment their skills. What do we mean by such capability enhancements in the era of generative AI? Can we advocate for a contextualised advisory process and human intervention to handle exceptional situations?
According to a McKinsey study in 2023, banks promptly revised their technology strategies, increasing expenditure and accelerating adoption of advanced methodologies. The imperative to realign operating models with this elevated technological standard has coincided with a shift in the economics of private banking. The same study highlighted that private banks, which successfully increased productivity of their RMs, experienced 12 per cent annual growth in assets under management (AUM) per manager, in contrast to the industry's 5 per cent. Let us take a step back and understand how we can enhance productivity.
Hyper-personalisation
According to Bilal Jaffery, AI practice leader at Deloitte, achieving this objective entails crafting bespoke and targeted experiences utilising data, analytics, AI, and automation. Through hyper-personalisation, businesses can send highly contextualised communications to individual customers at the right location and time, and through omnichannel experience. This technique offers RMs an effective way to gather detailed insights about high net worth individuals, enabling them to provide customised solutions for their unique needs.
Intelligent client profiling
The imperative to profile and grasp customers’ needs remains ongoing; this propels wealth firms to regularly update customer profiles and observe changes in their transactions and investment patterns through demographics, psychographics and behavioural analytics. Customer profiling demands a focused data mining (using SAS, Python, R, RapidMiner, Weka, etc.) approach, using data from existing enterprise databases and systems to establish 360-degree customer profiles for diverse customer groups. Nonetheless, many businesses want assistance efficiently utilising and comprehending their customers’ profiles and transaction behaviour. This complexity stems from the dispersed nature of this information, making integration and summarisation daunting, if not infeasible.
Dynamic portfolio optimisation
It's important to emphasise that selecting a suitable portfolio optimisation model depends on the specific optimisation requirements of the portfolio, the characteristics of the available data, and intricate market dynamics being modelled. There are plenty of helpful machine learning and deep learning algorithms, such as GANs, VAEs, RNNs, LSTMs, DRL, and Generative AI models such as Llama2, GPT, Titan, Claude2, that have been used to identify the customer persona and accordingly provide appropriate investment recommendations. Wealth management firms frequently blend these models and techniques to establish all-encompassing, flexible portfolio optimisation techniques, considering the probabilities and uncertainties associated with each choice.
Predictive analytics
In investment management, the synergy between predictive analytics and generative AI is heralding a new era of intelligent decision-making. Predictive analytics, through its sophisticated algorithms, sifts through vast datasets, discerning patterns and trends that guide future predictions. By utilising the power of generative AI, wealth management firms gain the ability to explore multiple potential futures, allowing for more nuanced, agile and proactive strategies. Deep Q-Networks (DQN) and Proximal Policy Optimisation (PPO), in combination with generative AI, can ease out optimal decision-making recommendations in complex environments on the go. In predictive analytics, these models can be used for dynamic decision-making tasks where actions direct future states, such as stock and foreign exchange trading.
Digital demands
High net worth clients demand personalised and customised financial services. Enhanced capabilities empower relationship managers to delve deeply into individual client requirements, offering tailored solutions that align with their financial objectives. The intricacies of the economic landscape, including a myriad of investment choices, tax regulations, and market fluctuations, necessitate more focused abilities for relationship managers. This proficiency is critical in guiding clients through the complexity, ensuring their decisions are well-informed to optimise their investments.
In an era of innovation and competitive advantages, firms that invest in elevating their RMs’ skills gain a significant edge. Clients tend to remain loyal to firms that provide advanced, technology-driven decisions coupled with hyper-personalisation. This combination not only fosters increased business but also enhances client retention rates substantially. Thus, integrating deep skill capabilities not only meets client expectations but also positions wealth management firms at the forefront of the industry, ensuring long-lasting client relationships and sustained business growth.
Moreover, the financial markets industry operates within a heavily regulated environment. Exponential technologies like generative AI play a pivotal role in assisting relationship managers in staying compliant with the ever-evolving regulations. These enhanced skills ensure that client's investments adhere meticulously to legal requirements and ethical standards, instilling trust and confidence in the firm’s services. Compliance with regulatory norms is fundamental. Generative AI capabilities enable RMs to navigate the complex web of regulatory rules effectively, guaranteeing the integrity of financial transactions and reinforcing the credibility of the wealth management firm in the eyes of its clients.
RMs are essential to expanding any financial institution, including private banks. Developing, managing, maintaining, and growing client relationships within the affluent market segment is this position's primary goal, including revenue generation, loan and deposit development, and client retention. Given the client-facing nature of the work, RMs must be able to accurately assess consumer sentiment and tone in real-time and take appropriate action. This is where generative AI-driven processes may enhance their abilities and support expanding financial institutions' businesses. It is interesting to know how generative AI can help achieve the same.
After converting the speech to text, while RMs talk to clients, generative AI helps understand tonality and classify sentiment on different predefined KPIs, before recommending subsequent step suggestions. By helping RMs understand individual client needs, generative AI can produce a ‘knowledge graph’ and suitable investment or fund recommendations, as well as helping the advisers comply with regulatory policies and guidelines. Finally, generative AI, in combination with deep learning algorithms such as Convolutional Neural Networks, helps RMs to ensure the individual’s identity.
GenAI, in combination with AI and machine learning algorithms in particular, has the potential to completely transform the way financial businesses operate, and connect with their consumers. More precisely, the AI-sourced, AI-managed, and AI-orchestrated customer data, fed into cutting-edge GenAI-driven customer relationship management technologies, has the potential to alter the way financial institutions interact with their customers drastically.
But remember, with great power comes great responsibility. Developing a loyal consumer base requires companies to earn trust by consistently demonstrating appropriate data usage. To achieve this goal, one must provide users with streamlined, tailored experiences that are also relevant and tasteful. Businesses must lay forth rules for the collection, storage, and utilisation of data. Even though AI innovation necessitates the democratisation of AI, these processes should be centralised with a cross-functional team from across the firm that can set policies and assure appropriate governance. Then, and only then, financial organisations can earn the trust of their customers, irrespective of race or ethnicity, and make a real difference in the world.
Ken Schoff, principal partner technical specialist, IBM
Raja Basu, financial markets leader SME, IBM
Anjanita Das, director of Artificial Intelligence & analytics at Gen AI, Cognizant
