RBC Capital Markets has looked externally to help it consolidate data in one place, analyze it and better equip employees.

Starting in 2013, RBC Capital Markets began collecting all the data it could that was relevant to its clients, including e-commerce data, trades and portfolios. Problems arose, however, when the bank realized it had no clean way of presenting that data and making sense of it.

According to Kim Prado, global head of client insight at RBC Capital Markets, the disorganized data on its back-end systems made work incredibly difficult for bankers and, consequently, hampered efforts to reach clients. “The whole team would be at work until 2 or 3 in the morning on a Friday night,” she said during a presentation at Finovate Fall conference on Thursday. “Every time a new salesperson would start, we couldn’t train them. Nobody remembered how the system worked.”

To help solve this problem, the company turned to OpenFin, a company that builds operating systems for financial companies. From 2013 to 2017, the company was focused on collecting data, organizing it and mapping it. In turn, OpenFin was able to make all of its platforms work in-sync, tying together input from its recommendation engine and its CRM and ticketing systems. The upgrades allowed bankers to make asset allocations and portfolio trades more efficiently.

With all the information integrated, RBC Capital Markets has now added real-time analytics capabilities. “Users are able to enter any client, any security or any trading book, any sales code — anything they’re thinking about,” Prado said. “It’s enabled us to join the data together and serve it up in real time. Bankers can see what trades they have made, as well as what trades they might have missed that day.”

Prior to this upgrade, Prado said bankers would need to take four or five hours at the end of their workdays to bring the data together and analyze it, a tedious effort that would involve going through Bloomberg data manually and inputting it into Microsoft Excel.

RBC Capital Markets thinks about data in “buckets” or phases, according to Prado. The first phase is collecting the data, after which banks need to make the data “smart” through artificial intelligence or machine learning. The final phase is monetizing that data and serving it up to clients. For startups, Prado recommended using data and machine learning to analyze customer service tickets. In this way, a new company can pinpoint exactly where it is having problems.

Consolidation of legacy systems and data is a challenge other financial institutions are facing. For example, this week, Santander Bank tapped tech company nCino to replace 13 existing legacy systems with a single platform.

Despite the advances AI had made possible, Prado cautioned against hyping its capabilities. “I’m not a big fan of the term AI,” she said. “It’s been a bit of a buzzword but it is very real, and it’s about taking all the information you have and making it smarter.”

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