Larger companies and wealthier individuals rely on the services of qualified advisers to manage their money. However, financial advisers, despite their experience, expertise or loyalty, are ultimately human. And human error is something you never want in managing your money or other assets. Plus, relying too much on your financial advisor can also make you vulnerable to potential fraud. After all, your advisor has all of your confidential financial information.
AI isn’t exactly new to the financial industry and has several applications in areas such as fraud detection and auditing. AI-powered applications can either increase human expertise by handling low-value tasks or proactively take on more strategic roles for businesses. Either way, AI in asset management guarantees a high level of precision in forecasting by analyzing billions of different scenarios and data points.
AI and asset management
Typically, your assets include all of your financial holdings. Asset management generally deals with the management of specific investments, such as your bonds, derivatives, mutual funds and other similar assets in your portfolio. The most common applications of AI in asset management include portfolio 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 assess which stocks should stay in your portfolio and which shouldn’t. Machine learning determines the relationship between the risks and returns associated with each stock after evaluating thousands of factors such as the financial health of the company, your risk tolerance, and the historical or seasonal performance of a certain stock’s performance. class. Suggestions are constantly improving their effectiveness through continuous learning and assessment of stock market trends.
In addition to quantitative trends, AI-powered asset management tools also make use of qualitative data from the internet, such as financial forecasts, reports, and social media posts. By taking into account risk variables, such as loss of mortgaged property, bankruptcy, and qualitative aspects, AI in asset management assesses the types of stocks that can fall drastically without any probability of rising. . For example, a stock belonging to a company that makes the news for the wrong reasons, as perceived by the majority, will collapse on the stock market, a fact that the AI ââdetermines beforehand using an analysis. predictive.
b) Compliance management
AI enables your business to manage risk in a way that meets regulatory compliance. AI algorithms can be trained to identify regulatory information from public notices and prepare a report with that information. Additionally, companies can use AI to detect changes to investment guidelines from official source documents presented online, such as investment policy statements, AMIs, exemption orders, and more. .
One of the primary uses of AI in asset management, from a compliance perspective, is the reduction of false alerts generated by standard rule-based compliance alert systems. As of 2018, âfalse positiveâ alerts made up about 90% of all alerts for legacy compliance alert systems at several banks.
AI and machine learning capture, cleanse, and analyze multiple pieces of data to streamline compliance alert systems. This way, your business can save unnecessary time and money investigating large alert queues to find details about an alert. Costs are also saved in other ways, such as automating complex governance processes that still rely on manual labor and paper documentation in multiple organizations. According to a study, companies spend around 15-20% of their daily expenses on governance and compliance costs.
Apart from this, AI in asset management generally allows organizations to channel their human resources for tasks that require a âhuman touchâ, to effectively manage assets and investments, and to automate the management of changes each. times there are regulatory changes (and thus saving heavy fines for non-compliance) and the mitigation of human errors in asset management.
Using AI in asset management works just as you’d expect it to work in a financial context.
Robotics, one of the major subsets of AI, holds promise in the field of wealth management. There are currently nearly 100 financial advisor robots in 15 countries. Financial forecasts predict that the number of assets managed by robo-advisers will be around $ 16 trillion. Robo-advisers use client feedback and take into account factors such as risk appetite, liquidity, and others before highlighting the best financial options available before investing in stocks, bonds or investments. ‘other financial assets.
Robo-advisers have undergone four main developments. The first phase involved client-investors receiving unique product proposals based on an online questionnaire that clients would complete to provide information on their investment preferences. No broker API was involved. The second evolution included the use of a portfolio allocation based on risk and the concept of funds. The third evolution involved the use of proposal rebalancing algorithms. The final evolution automates financial investments with self-learning and uses AI and robotics to automate asset transfers. AI will continue to be heavily involved in financial advisor robots.
AI and wealth management
Unlike asset management, which is a finite number of things, wealth management is a much broader term. It examines several factors that affect the overall finances of an individual or a family before providing recommendations for maximizing their wealth. Some qualities of AI in asset management, such as cost reduction and better decision making, are also used to optimize wealth management.
Here are some of the main areas of application of AI in business and personal wealth management:
a) Tax planning
An example of automated AI-based tax planning is a tax planning assistant named Odele. The tool can be a valuable resource for businesses, entrepreneurs, wealthy families, and other similar clients.
An AI-powered tax planner like Odele autonomously compares tax assumptions, projections and configurations. In addition, such a system analyzes data from past records and other financial sources to calculate amounts such as loss of income due to tax and other similar figures. Based on analysis from previous years, the tool recommends optimal tax planning and setup for clients. Factors such as personal lifestyle can also be taken into account. And finally, the system learns and updates itself with information from agencies such as the IRS to create and modify your tax policy.
Effective wealth management is highly dependent on how you manage your taxes. Typically, with taxation in any country there are ways to exempt you from paying it in different ways. Having an AI-based tax management tool lets you know all the information on how to save money.
b) Estate planning
Like most traditional concepts that come under wealth management, estate planning has also typically been done with paperwork. The documentation would include the physical copies of the proof of identity documents. This way of planning for succession slows down the whole process. Instead, AI can be easily leveraged to simplify estate planning. Technology can provide insight into your estate planning while staying on the safe side of federal or state laws.
AI is advanced enough to analyze a person’s complex situation and provide an optimal outcome regarding their estate. Moreover, AI can even create legal documents for these people. Factors such as decision-making regarding the transfer of ownership can be automated through machine learning and AI.
Apart from these, there are several other areas in wealth management that can be optimized using AI. One of those areas is providing personalized customer engagement and service to customers. Already, tools such as chatbots are being used to improve the customer service executive interface. Chatbots facilitate the autonomous resolution of customer queries regarding 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, with topics such as tax planning, estate planning and other factors. AI can be expensive to implement and run, but the level of ease it brings to the financial market is unprecedented.