How AI Adds Value to Wealth & Wealth Management

The largest corporations and the wealthiest individuals seek the services of trained advisors to manage their money. However, despite their experience, expertise or loyalty, financial advisors are ultimately people. And human error is something you never want when dealing with your money or other assets. In addition, relying too much on your financial advisor can also leave you vulnerable to potential fraud. After all, your advisor has all your confidential financial information.

AI is not exactly a novelty in the financial sector and has multiple applications in areas such as fraud detection and auditing. AI-based applications can either increase human expertise by performing low-value tasks or proactively take on more strategic roles for businesses. Regardless, AI in asset management guarantees a significant level of accuracy in forecasting by analyzing billions of different scenarios and data points.

AI and asset management

Normally, your assets include all your financial assets. Wealth management typically deals with the treatment of specific investments, such as your bonds, derivatives, mutual funds and other similar assets in your portfolio. The most common uses of AI in asset management include portfolio-related decision-making, compliance management, and financial advice.

a) Portfolio management

The pattern recognition capabilities of AI and machine learning are put to good use to evaluate which stocks should stay in your portfolio and which not. Machine learning determines the relationship between risks and returns of each stock after assessing thousands of factors, such as the financial health of the company, your risk tolerance, and the historical or seasonal performance of stocks of a particular class. The suggestions become more and more effective through continuous learning and evaluating the trends in the stock market.

In addition to quantitative trends, AI-based wealth management tools also leverage qualitative data from the web, such as financial forecasts, news and social media posts. By considering the risk variables – such as loss of mortgaged property, bankruptcy – and the qualitative aspects, AI in asset management evaluates the types of stocks that could fall drastically without being likely to rise again. For example, a stock of a company that’s in the news for the wrong reasons – as perceived by the majority – will crash in the stock market, a fact that AI determines in advance using predictive analytics.

b) Compliance Management

AI empowers your business to manage risk in a way that achieves regulatory compliance. AI algorithms can be trained to identify regulatory information from public notices and prepare a report with the information. In addition, companies can use AI to detect changes in investment guidelines from official source documents available online, such as investment policy statements, IMAs, exemption orders, and similar others.

One of the key applications of AI in asset management, from a compliance perspective, is to reduce false alerts generated by standard, rules-based compliance alert systems. In 2018, false positive alerts accounted for approximately 90% of all alerts for legacy compliance alert systems across banks.

AI and machine learning capture, clean, and analyze multiple data elements to streamline compliance alert systems. This way, your company can save the unnecessary time and money spent researching large alert queues to find details about an alert. There are also other ways to save costs, such as the automation of complex governance processes that in various organizations still depend on manual work and paper documentation. According to a study, companies spend about 15-20% of their daily expenses on governance and compliance costs.

Beyond this, AI in asset management typically allows organizations to channel their human resources for the tasks that require a “human” approach, manage assets and investments effectively, and automate change management when there are regulatory changes (causing hefty penalties for non-compliance) and limit human error in asset management.

Using AI in asset management works exactly as you would expect it to work in a financial environment.

c) Robo Advice

Robotics, one of the key areas of AI, holds great promise in asset management. There are currently nearly 100 robot financial advisors in 15 countries. Financial forecasts predict that the number of assets managed by robo-advisors will be approximately $16 trillion. Robo-advisors use input from clients and consider factors such as risk appetite, liquidity and others before highlighting the best financial options out there before making an investment in stocks, bonds or other financial assets.

Robo-advisors have gone through four major evolutions. In the first phase, client investors received proposals for a single product based on an online questionnaire that clients would complete to provide information about their investment preferences. No broker API was involved. The second evolution involved the use of risk-based portfolio allocation and the concept of funds. The third evolution brought about the use of algorithms to rebalance proposals. The latest evolution automates financial investments with machine learning and uses AI and robotics to automate asset shifts. AI will continue to be heavily involved in robotic financial advisors.

AI and asset management

Unlike wealth management, which encompasses a finite number of things, wealth management is a much broader term. It looks at multiple factors that affect an individual or family’s overall finances before making recommendations to maximize their wealth. Certain qualities of AI in asset management, such as cost reduction and better decision-making, are also being used to optimize asset management.

Here are some of the key application areas of AI in the world of business and personal wealth management:

a) Tax planning

An example of AI-based automated tax planning is a tax planning assistant called Odele. The tool can be a valuable resource for businesses, entrepreneurs, high net worth families and similar other clients.

An AI-based tax planner like Odele autonomously compares tax assumptions, forecasts, and configurations. In addition, such a system analyzes data from past records and other financial sources to calculate amounts such as lost income due to taxes and other similar figures. Based on the analysis of previous years, the tool recommends optimal tax planning and configuration for customers. Factors such as personal lifestyle can also be considered. And finally, the system learns and updates itself with information from agencies like the IRS to create and adjust your tax policies.

Managing wealth efficiently depends a lot on how you manage your taxes. In general, taxation in any country offers ways to exempt yourself from paying in different ways. With an AI-based tax management tool, you know all the information about ways to save money.

b) Wealth planning

Like most traditional concepts that come under wealth management, estate planning is also generally done with the paperwork. Documentation includes the physical copies of ID documents. That way of estate planning slows down the whole process. Instead, AI can simply be deployed to simplify estate planning. The technology can provide insight into planning your estate while staying on the right side of federal or state laws.

AI is advanced enough to analyze a person’s complex situation and provide an optimal result regarding their ability. Moreover, AI can even create legal documents for such individuals. Factors such as real estate transfer decision-making can be automated with machine learning and AI.

Apart from this, wealth management has several other areas that can be optimized using AI. One of those areas is providing personal engagement and customer service to customers. Tools such as chatbots are already being used to improve the executive interface of customer service. Chatbots facilitate the autonomous solution of customer questions about personal wealth management.

AI in asset management analyzes various factors so that companies can select the best stocks or other assets in the financial market. Wealth management has a broader scope, financially speaking, covering topics such as tax planning, wealth planning and other factors. AI may be expensive to implement and use, but the convenience it brings to the financial market is unparalleled.