By Gus Sekhon, VP Strategy, FINBOURNE Technology.
For all the billions spent modernising investment technology, one question continues to linger for asset managers around what exactly do they own and where the risks lie?
While it may sound straightforward, the reality is that across the investment industry right now, producing a complete picture of portfolio exposures is a laborious exercise in stitching together information from multiple systems, external managers and spreadsheets. The result can take days to assemble and may already be outdated when it reaches the investment committee and, consequently, this is exposing a fundamental weakness in how the industry manages investment data.
Over the past decade, firms have invested heavily in specialist platforms covering portfolio management, trading, accounting and risk. Individually, many work perfectly well, but the problem is that each was designed to perform a particular function, rather than provide a complete picture of an increasingly complicated investment portfolio.
And those portfolios are becoming considerably harder to understand. Growing allocations to private markets mean investment managers must reconcile assets valued continuously with those valued quarterly, sometimes using information received weeks after the reporting date. Add derivatives, collateral arrangements and externally managed investments into the mix, and suddenly establishing a reliable picture becomes even more challenging.
Buy More
To date, the industry’s response has largely been to buy more technology. While some firms have pursued consolidation, replacing multiple systems with a single platform, others have built reporting layers that pull information from existing applications. Increasingly though, many investment managers are considering building their own data infrastructure, encouraged by advances in AI. While each approach has merits, neither one automatically solves the underlying problem. The real challenge is trying to get hold of the trusted information by understanding exactly when the data was accurate and how it has changed over time.
As a prime case in point, a private credit investment may have a valuation revision several weeks after quarter-end. In addition to understanding its latest value, an investment manager must also know when the revision became known and how that change affects historical exposures. A conventional database may simply overwrite the original figure which creates an issue as and when investment committees ask what the firm knew.
Maintaining Control
AI can certainly help by speeding up data processing and in identifying any inconsistencies. But it cannot manufacture reliable valuations or resolve conflicting records without reconstructing historical information that was never properly looked after. Indeed, deploying AI across fragmented, poorly governed data risks making the problem even worse. Faster analysis of unreliable information is still unreliable analysis and what investment managers need is a more disciplined approach to their underlying data architecture.
Ultimately, that means maintaining consistent records across public and private assets, identifying where information originated, recording when it became available and preserving historical changes. Crucially, firms must also retain sufficient control over their data to accommodate new asset classes, regulations and reporting requirements. The industry has spent years asking which technology can produce the best portfolio dashboard, so perhaps it is finally time to ask what good is a pretty screen over your investments if you cannot trust what you are looking at?
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