Drive Wealth Management Via AI Personalization

Understanding the context, including strategy, customers, corporate culture, and other factors, is necessary to comprehend the application of AI to business. Wealth management is one application that must be researched across enterprises. Some banks and investment companies are attempting to utilize AI to enhance that management, either by completely replacing human wealth managers or, far more frequently, by supplementing their efforts. 

The wealth of high-net-worth people is increasing in double digits in certain areas, such China, Taiwan, and Hong Kong, and on average, across the Asia-Pacific (APAC) region, investable assets are rising significantly. This increasing tide may not, however, benefit all wealth managers (WMs) in the area equally. Using data and AI might be a crucial difference for success while opting for digital transformation solutions.  

Enhancing The Feed

Why is AI predicted to have such a significant impact on the sector? 

Contrary to discretionary mandates, which are more popular in the US and Europe—along with the potential of more frequent transactions—clients in the area prefer to self-direct their own investments. In this situation, relationship management must place a greater emphasis on offering customers research and trading guidance that is tailored to their unique investing preferences and life phases. 

AI has the potential to be very effective at this form of personalization. The use of Artificial Intelligence in the front office might have a significant influence on income, even while it also has significant application potential in many back-office applications to help reduce expenses. 

An AI-enabled app that is aware of a client's current investment preferences and holdings might share pertinent internal research with them as well as search through investor reports and unstructured web headlines for relevant information. Following that, a relationship manager might swiftly relay the advice to the clients after reviewing them. As a result, increased lead creation and client retention depend on AI's scalability. 

Playing Catch-Up

Leading the AI drive have been many banks. Examples of this includes:  

• UBS Global Wealth Management uses AI to provide customers in Asia with practical insights and trading examples. 
  
• In order to give its clients a highly tailored service, Credit Suisse has implemented AI capabilities for its relationship managers to provide pertinent, actionable information. Credit Suisse is also aiming to expand its data and AI capabilities. 
• With the automated, algorithm-based Personalized Investment Ideas tool from Standard Chartered, relationship managers can quickly create and distribute personalized advice based on clients' risk profiles and the bank's market views, giving priority to banking customers what the company calls a "digital with human touch experience."

Notwithstanding these developments, a lot of wealth managers still fall behind, at least as compared to the US. Most regional players are currently developing their AI offerings. With the COVID epidemic, this technology has only grown more useful.  

Digital proximity has become essential due to the disruption of traditional relationship-management touch points like in-person meetings and conferences. Consider the fact that Morgan Stanley's AI-powered NBA recommendation engine was utilized 11 million times in the first two months of the year. 

Instead of triaging by calling the biggest and most active clients while leaving the others uncontacted, AI would enhance human relationships by making it easier for relationship managers to connect all the information about a customer to provide the right recommendation at the right time using the right channel. 

According to research, successful businesses invest in AI at a 3x higher rate of return than those whose projects are stalled in the proof-of-concept stage. Three key success indicators might assist asset managers in overcoming obstacles and reaping the full rewards of AI: 

Business strategy, not technology, is where leaders draw their inspiration for AI. Develop a cadre of evangelists by starting projects in a few areas to get traction. Establish a value-measurement operational model with owners, owners' processes, and financing levels that are acceptable. Leaders could execute effective AI initiatives 3-5X quicker if there was explicit responsibility. 

Leaders are better able to incorporate information from both internal and external sources. The correct AI tools, such as cloud-based data lakes and data science workbenches with model management, may improve "trusted AI" governance and model explainability in addition to enabling data upkeep and consumption. 

As the data landscape and underlying technology change, AI is not a one-time event but rather a continuous and iterative process. Executive sponsorship is insufficient, and effective scaling necessitates the integration of multidisciplinary teams across the whole firm, according to leaders. A continued commitment to company value is reinforced by the more sustainable outcome that results from a higher skill combination

How is AI Adding Value to Wealth and Asset Management? 

During the past 10 years, the financial services sector has become more interested in artificial intelligence (AI) and machine learning (ML) technology. The wealth and asset management sector has been a major user of AI, with several companies putting it to use at various phases of the investing process, from data collection and pre-trade analysis to post-trade reporting and client involvement. 

These are a few instances where AI has already improved wealth and asset management. 

The portfolios of specific clients are being examined by AI algorithms for improved management and help. AI may be used, for instance, to make stock-related judgments. By analyzing hundreds of elements, machine learning may be utilized to evaluate the link between risks and returns related to a company's stock.  

Including both qualitative and quantitative trends, as well as financial projections, news articles, and social media posts. AI in asset management analyzes the types of stocks that can fall dramatically without any likelihood of rising again by taking risk factors such as losing mortgaged property, insolvency, and the qualitative characteristics into account. With ongoing research and analysis of stock market movements, these recommendations continue to be more successful. 

In order to study investment rules to reduce risks and expensive charges, AI algorithms are taught to recognize regulatory information from public announcements and discover modifications recommended in current investment policy declarations. Businesses may be able to use AI to clean and interpret different data pieces to expedite routine governance and compliance tasks. 

In order to minimize human mistakes and make the best investment decisions, companies and independent asset managers may often navigate through multiple data inputs with the help of AI in wealth and asset management. Financiers may make data-driven judgments that will maximize their wealth at any given time with the use of applied AI forecasting and fraud detection tools. 

A tax planner powered by AI examines various tax suppositions, predictions, and setups. In order to determine sums like lost income owing to taxes and other comparable numbers, it also analyzes data from records and other financial sources. The tool suggests to customers the best tax arrangement and planning based on the study of prior years. 

Several estate planning choices may now be automated with the use of artificial intelligence (AI) and machine learning (ML). AI may provide several advantages, from evaluating a person's complicated condition to delivering the best possible result for their inheritance. To make the process simpler for the person making the decisions as well as others who will be impacted by them, AI may even prepare legal papers for such individuals. 

Robo-advisers are increasingly playing a significant role in financial advisors' daily activities. Robo-advantages advisor's include producing single-product recommendations for possible client investors, using API solutions to track risk-based portfolio allocations and manage concept funds, and ultimately implementing AI to make investment choices automatically. 

AI and the Future of Wealth Management 

By the end of 2024, one-third of businesses will go from the pilot stage to operationalizing AI in wealth management, predicts Gartner. Early adopters of AI have up till now concentrated on issues related to risk and compliance. 

Yet, corporations have just begun to see the expansion of AI in improving market forecasts and services. By recognizing and predicting clients' requirements, AI-driven analytics may assist businesses and client-focused personnel in providing customized recommendations. 

Several target business units, including the creation of proof of concepts, have been linked with AI initiatives, according to the great majority of financial sector companies. In the future years, it is anticipated that AI usage will increase at a possible rate. The different applications of this technology may be used to increase the productivity of businesses. 

Financial advisors nowadays find it challenging to select the best tools that suit their needs and enable them to obtain a greater value because of the wide range of different pilots that are currently available for various use cases. We can see further chances for better integrating AI into current wealth management systems and enhancing client perception as knowledge of AI in wealth management continues to rise.

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TransformHub, an AI-driven data analysis and service provider, offers a range of wealth management products. By leveraging our digital transformation services, we take complete accountability to assist you in identifying and assessing the requirements for your company's improved wealth and asset management. Let’s get in touch today.