Artificial intelligence (AI) has woven its way into conversations in just about every industry. The potential for AI and machine learning (ML) has broad implications for wealth management.
The trends in AI for general business activities currently focus on the flashy things it can do—write a business email, use a virtual digital assistant to get more done, or inspire a marketing pitch. It’s very plausible for wealth management to use AI in this way, however, the true value for firms is more in the practical realm.
The existing use cases for AI and how it can shape the future of wealth management are compelling. In fact, 90% of global fintech companies are already relying on the power of machine learning and AI.
AI will have a spot in most every investment firm’s toolbox in order to stay competitive and modernize operations. In this article, find out how trends in AI will deliver specific advantages for wealth management.
Automation and AI: From Process-Driven to Data-Driven
Every wealth manager wishes there was more time in the day. Manual work often takes more time than it should. Think like data reconciliation, searching for data, and building reports can be overwhelming. It keeps advisors in the weeds and way from the bigger picture.
The ability for AI to fuel automation fits a need within the investment industry, and it’s a welcome disruption. Most automation is currently rudimentary and mostly uses robotic process automation (RPA). Digital “bots” can replicate human actions like keystrokes and moving data. It works on repetitive tasks that don’t deviate from the norm. It gives an automation boost without the need for human intervention.
RPA combined with AI creates the possibility for intelligent automation, which is a step up. It expands capabilities and integrates ML and natural language processing (NLP) into the equation. Now, automation can be data-driven rather than just process-driven.
Intelligent automation ushers in the potential for more than basic tasks. It can support faster data analysis. It also makes forecasting more accurate and can also be used to spot some trends in numbers that might need further investigation.
Automation began as a productivity intensifier. With the latest AI tech wealth managers can make effective data-driven decisions more quickly.
Infusing AI into Trade Order Management
The next trend will involve how foundational platforms like trade order management systems (OMS and portfolio management systems (PMS) are integrating AI. For the wealth management industry, there is a need for order and execution management (OMS/EMS) platform to leverage practical AI tools.
By embedding AI, ML, and NLP, an OMS can construct andmodel trades, as well as rebalance portfolios with greater ease, accuracy, and speed, benefiting the wealth management firm’s end client investors.
The addition of AI into trading and portfolio management workflows has the potential to instantly modernize a stagnant and inefficient workflows for many investment firms. Any technology that integrates AI for the buy-side must be transparent about how it works. Peeling back the layers is crucial. What should be driving all this innovation is that AI tools like NLP and computational linguistics can now work with ML and statistical models. This combination enables the platform to work intelligently and recognize and understand language to be used to improve different workflows and processes
An AI-driven OMS/{MS will be an essential part of a wealth management’s future. Each component of AI within the overall platform augments human actions. It makes the entire system smarter and more efficient
Effective Data Analysis No Matter the Source or Format
The massive amounts of data from disparate and multiple sources that are used in wealth management are also a great candidate for AI applications. In wealth management, having an AI engine to work with and ingest data to consolidate it into useable information is a potentially revolutionary use case for AI.y.
Generative AI does the data crunching, creates the output that’s more easily accessible in summary form and virtually works in real time. There are potentially significant efficiency gains in this use case. The challenge is centralizing the data so that it can be used and also carefully checking the results..
Data Analytics Meets Machine Learning
Everybody’s talking about generative AI and its ability to analyze data quickly and accurately. There are different variations of how AI merges into data analytics, and ML is the key to this being a long-term benefit.
It starts with the data consolidation part, then moves to analysis and finding trends. That’s what ML models do. They also learn as they go. As the ML algorithms do their analysis, they see patterns, trends, and outliers. Those then translate into actionable insights.
Having this advantage of greater access to valuable data can improve the overall performance of wealth managers and the assets they manage.
Compliance to a Get a Hand from AI, Too
In any highly regulated field like investment management, compliance is ar the center of the firm’s investment operation.. It must be a bedrock for any investment process, with adequate control in place to de-risk investment operations. Compliance teams have begun to investigate the widespread potential that AI brings to the table. They’ve traditionally been slower in adopting new technology but have realized its potential value.
There are a few ways AI can provide support for compliance professionals..
AI can detect and flag events more quickly than traditional methods. AI can raise the flag on potentially concerning transactions, for example Compliance teams have constant demands in terms of new regulations.. AI can help them focus their efforts on those activities that need further investigation.
With AI tools based generative AI, NLP and ML, compliance managers can more effectively review documents and investment management agreements, financial statements,, and other documents. It can interpret and pull out effective summaries from these documents, saving a substantial amount of time.
ML models can also support the identification and classification of investment compliance rules. In this application, it can work as a very sophisticated digital assistant.
The Value of AI in Wealth Management Is More Than a Trend
As AI continues to evolve, so will its use cases. There are some strong ones discussed above, and those will grow and improve. While the industry shouldn’t be subject to trends—AI is not one to ignore. However, firms need to be guided by their fiduciary obligations to clients so transparency is absolutely necessary when implementing AI based solutions within wealth management.
About the Author
David Csiki is the Managing Director and President of INDATA, a leading industry provider of software and services for buy-side firms including trade order management (OMS), compliance, portfolio accounting, and front-to-back-office technology solutions. Prior to joining INDATA, Csiki was Manager of Marketing and Investor Relations at NYFIX, Inc. and was instrumental in developing the product concept and planning the successful launch of the company’s flagship product, NYFIX, a FIX broker network.