AI in wealth management is empowering advisors and adding client value through the use of big data tools such as Intelligent Tagging and Knowledge Graphs to uncover previously hidden connections and insights.
- The first step towards AI in wealth management is for firms to leverage the data they have and to visualize connections between data sets.
- Intelligent Tagging and Knowledge Graph technology optimizes data for machine learning and personalization on news and research.
- Download the full Celent report to discover how AI-driven tools and data discovery are impacting the wealth management industry.
In recent years, wealth management has focused on investments in big data and advanced analytics in order to offer clients relevant products based on demographics and goals.
Advisors now see artificial intelligence (AI) as a tool offering the ability to assess their clients and their assets, as well as to provide insights concerning to whom they should focus on, and why.
They want the ability to prove they are at the top of their game, not only for the clients they are managing today but for the far greater number of clients they may be managing in the near future.
The first step towards AI in wealth management involves firms leveraging the data they have.
This ability to establish and visualize connections between data sets confers a powerful information advantage.
Of particular value to the wealth manager are signals or insights on consumer behavior and purchasing decisions.
This intelligence derived from Knowledge Graph technology will turn existing data into a wealth management asset.
The appeal of technologies such as Knowledge Graph or Intelligent Tagging (in which each data point is assigned its own ID) highlights the need to bring order to and make sense of data.
This can be done through natural language processing (NLP), natural language generation and other machine learning and AI-based tools.
Intelligent Tagging (TRIT) uses natural language processing, text analytics, and data-mining technologies to filter and classify volumes of data.
TRIT tags people, places, facts and events across millions of documents at a far greater speed than humans could ever hope to achieve.
Intelligent Tagging enriches content by attaching ‘relevance scores’ to data so that it becomes readily searchable, meaning that advisors are empowered to find the information they need.
Machine learning in the form of Intelligent Tagging helps advisors to make sense of big data, and harness the power of an information-rich environment that could otherwise be overwhelming.
In today’s complex digital world, the ability to organize and establish links between diverse data types has the potential to solve real business challenges.
The growth in volume and varieties of data sets is prompting firms across all industries to look for automation to generate insight and decisions from this data.
One way to achieve this is by applying Natural Language Processing (NLP) and Knowledge Graph technology to data sets, in order to label, tag, and present in order to uncover previously hidden connections and insights.
The Knowledge Graph feed helps with data relationships, discovery, and exploration needs across a range of business requirements.
Information discovery through this use of intelligent tagging and knowledge graphs will help optimize existing data for machine learning and personalization on news and research.
Applying AI in wealth management
The future of AI in wealth management is promising.
We think it is essential in the advisory workflow to complement how advisors connect with their customers and improve efficiency.
It’s also pivotal for supporting self-directed investors, meaning AI should be a critical component of any wealth firm’s growth strategy.
Interested in learning more about the use of big data and AI in wealth management?
Download the full Celent report to discover how AI-driven tools and data discovery impact on the wealth management industry.