AI ILLUMINATIONS: How Data Mapping Helps Advisors, Firms, and Investors


In the Wealth Management industry, data mapping is not straightforward. Different technology systems are talking to each other—but each of them will identify their data elements differently. A brief explanation of data mapping is saying that in system one, data is called ‘A,’ and in system two, it is called ‘B.’ That is essentially what data mapping is.

But when we talk about multiple custodians, who may have large volumes of data, data mapping can be cumbersome because of their legacy systems. Each custodian identifies and accounts for data differently and has different attributes. And often, this enormous volume of data needs to be connected—a prime use case for data mapping.

“We know the Wealth domain, and that’s the value we bring to the table. We can take one data set and use artificial intelligence to directly map the data because we know how to identify each data point across multiple custodians and organizations,” says Afilash Azeez, VP of Project Management Office at, an Autonomous Enterprise Platform for the Wealth Management industry. “For example, identifying an account, an account holder, or an owner makes data mapping straightforward. Our technology enables the business user to load a file, and maps over data using artificial intelligence to format it into their system.”

Data Mapping and the Experience Factor

Today, organizations are looking for a better and more enhanced user experience. While seeking opportunities to acquire new clients, assets, or advisors, movement creates the need for data from multiple places, not just one data source. Remember that when firms need to move custody from one to another custodian, they are always on the lookout for a better experience for their advisors and investors. That is where can help.

“When moving assets, the transaction will most likely involve working with two different technology systems. What could be an ‘I’m stuck, and it’s a huge cost’ moment can be turned into a simplified process with doing 80% of the mapping without human intervention. The remaining 20% can be done by business users, and that does not need a technical team,” adds Azeez.

By enabling the business users to do the mapping instead of investing in an army of technical people trying to map between data sources,’s no-code, AI-powered automation platform and deep domain expertise bring transformative value to the process.

AI is contextual, but in the Wealth domain, an account and everything that pertains to it have specific attributes. AI identifies specific data points and their differences, and then maps the data where it needs to be. When AI maps data from one system to another, the original data is preserved in translation.

In data mapping, the advisor perspective is one consideration, and the end investment perspective is another. Azeez elaborates on these scenarios where data mapping is critical:

“Imagine that I am an investor, and you are my advisor—moving my investments should be a seamless experience. As an investor, I don’t care about the process and what it takes to move my assets, but I have certain expectations. Statements should come to me, I should have access to view my investments, where the transfer process is at should be viewable through a client experience portal, and there should not be any issues.”

But for an advisor, he adds, the context is different:

“I do not want to lose the trust of my client. My firm should not hinder my business when bringing over my client’s data. Now that I’ve moved my book of business from my old firm to yours, the client data must remain intact because the client has indicated they want to move with me.”

Transitioning Data Using Data Mapping

The primary thing needed for success from purely a business user perspective is understanding what the data source is; and where the data must go is critical as well. If you do not know where you are taking the data, that is when the trouble starts. The best tip for successful data mapping is to understand the data source and target. brings full transparency into the firm’s technology system for advisors and investors through dashboards. For example, when an advisor is transitioning,’s data mapping-enabled dashboard shows that the advisor has 50 investors, of which 40 are ready to move with them. There is complete transparency in the workflow since it shows all the details that pertain to these 40 investors: AUM, holdings, risk analysis… everything.

“Data mapping is an unexplored area in the Wealth industry. Advisor transitions is a key area has been focusing on. With several acquisitions taking place, organizations often need help. They don’t want their advisors to lose faith in the process or walk away during the transition. That’s where can help the organization show value to the advisors and their investors by enabling full transparency using data mapping,” Azeez adds.