Expert: Philipp Zerhusen, Vice President, Director of Market Development, Factset
Facilitator: Dave Edwards, Founder, Esperto Business Solutions
- Identifying new prospects and deepening the knowledge pool around existing ones has traditionally been an unstructured, hit and miss, process that takes up a lot of time.
- Gathering additional data gives both breadth and depth to the adviser. The key is knowing where to look and what to then do with the various pieces of information.
- The AI joins up seemingly disparate bits of Information and makes linkages that the human would probably not pick up on – thus revealing new clients or investment opportunities with existing ones.
- Information gathering has to respect data privacy and GDPR. Wealth managers can also choose to exclude certain sources of information that they deem too sensitive or personal.
- The data mining task is a continuous one in terms of both gathering and assessing.
Having granular information about both prospects and existing clients makes for a more targeted approach and a deeper relationship going forward, This is a vast improvement from being reactive and knowing only what is required information-wise for KYC purposes and then what the client tells you.
Prospecting more efficiently
Identifying new prospects and deepening the knowledge pool around existing ones has traditionally been an unstructured, hit and miss, process that takes up a lot of time.
Relationship managers spend up to 20% of their time identifying new prospects and examining suitability issues for existing ones. It has traditionally been an unstructured process and has relied heavily on the use of informal networks.
The Holy Grail is to be able to identify new prospects and have the data and information to inform and decide how relevant and interesting they are, which segment they fall into, what they are likely to be interested in and whether there is any overlap with existing clients.
For existing clients, the idea is to deepen the relationship and have an in-depth and holistic understanding of them and their needs in order to promote better relationships and client retention, and thus increase the book of business.
Gathering additional data gives both breadth and depth. The key is knowing where to look and what to then do with the various pieces of information.
Knowing where to look to find various pieces of data on a person is key and having a system that then knows what to do with it and how to combine it with other data sets is valuable.
This can mean looking at structured data sets such as companies’ house, key executives within a company or sector, inheritances, weddings, divorces etc.
It can also mean mining unstructured data like social media or other public web content to find contextual information.
The aim is to combine the various data sets to come up with something meaningful and tangible that the relationship manager can use for a prospect or to deepen the relationship with an existing client.
Adding AI into the mix
The AI joins up seemingly disparate bits of information and makes linkages that the human would probably not pick up on – thus revealing new clients or investment opportunities with existing ones.
It is about overlaying this information with the client’s own finger prints that are on the system already.
The AI scores people according to how good a fit they are likely to be and identifies whether there are associations with other people i.e. existing clients too.
For existing clients, the AI can help to work out whether they are likely to like a given investment idea and where their interests and affiliations lie.
Information gathering has to respect data privacy and GDPR. Wealth managers can also choose to exclude certain sources of information that they deem too sensitive or personal.
Compliance with GDPR is a given. But mostly the system is looking for things that are already in the public domain and thus can be described as of legitimate interest.
Data mining should also respect privacy and crawling restrictions - for example LinkedIn does not permit crawling.
The wealth manager also needs to be able to set parameters around what is and isn’t acceptable to look for.
The approach can be either top down- looking for an individual, or bottom up, identifying groups of people with certain characteristic and sifting through them.
The data mining task is a continuous one in terms of gathering and assessing.
It is key to remember that the information has to be allowed to evolve and that the wealth manager must be willing to continually add to its bank or store of information. And be able to score which data is more or less relevant.
Read Original Article Here