Client reporting has long been one of Wealth Management’s (WM’s) most persistent and difficult challenges, especially when multiple custodian banks or financial services organisations are involved. Inconsistent formats, differing nuances, data delays, and the time wasted producing reports that are often out of date by the time the customer sees them continually hinder ongoing credibility.
AI data analytics can greatly assist and speed up inquiries and processes. However, accurate reporting in WM will only work if the investment data behind the analytics is ‘clean’ and fit for purpose—not just reconciled, but verified, enriched, and properly labelled. This includes promptly unravelling complex elements such as income, corporate actions, differing asset identifiers, transfers, rebooks, and other factors that can lead to errors and misleading conclusions if not handled properly.
At M2, we’ve seen that the difference lies in how AI is used. Many firms utilise AI analytics without addressing the underlying challenge of investment data quality, while others only tackle it after a laborious manual process—verifying accuracy can take days, weeks, or even months. Without clean, verified, and labelled investment data every day, the output lacks reliability. By contrast, when analytical AI is layered on top of daily verified positions (cash and assets)—fully reconciled and enriched—it becomes a powerful engine for accurate, timely reporting and decision-making.
The future of client reporting is not about producing more pages or flashier graphics—it’s about delivering intelligence that adds value to the client conversation. Done properly, AI allows wealth managers to spend more time advising, strengthening relationships, and building trust.
In this sense, M2 is not just adopting AI—we’re setting the standard for how it should be applied to investment data: responsibly, precisely, and always with the end client’s value in mind.