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Data overhaul paid off this year, says AIA’s Konyn

Mark Konyn, group CIO at AIA, says asset owners need top-to-bottom infrastructure to manage data.



Mark Konyn, AIA

Large asset owners such as insurance companies are devouring data like never before – which requires a firmwide strategy to manage.

“Data needs total integration,” said Mark Konyn, group chief investment officer at AIA, the Hong Kong-based insurer. AIA operates across 18 markets with group assets under management standing at $284 billion as of end 2019.

The firm has spent the past four years investing in the infrastructure and governance around how it sources, verifies, stores, organizes and utilizes data.

That work paid off earlier this year, when markets were roiling under the impact of the COVID-19 pandemic. Fund managers had to make important trades in very short periods of time, which meant they needed the means of analyzing the right kind of data, and having it move efficiently through the organization to act on the investment team’s insights.

Flexible foundations

Getting to this point is a monumental task that in AIA’s case has led it to completely overhaul how it operates. Konyn explains traditionally financial institutions relied on proprietary systems, or vendor solutions that were designed bespoke. The idea was that vendors should adapt their products so a customer didn’t have to change its operations to use it. This led to expensive, multi-year implementations – and a huge overhead.

Although this approach could give big firms a powerful system, it meant they’d be left behind whenever there was an innovation in the marketplace, or a new set of regulations to adhere to.

“When you’re the only user of your system, you only find out about problems when they arise,” Konyn said. “Bespoke systems become an albatross.”

The software-as-a-service model has turned this on its head. Now firms are framing their systems around vendor platforms so that data can flow through the organization seamlessly.

Todd Hartmann, senior director of strategy, content, and technology solutions at data vendor FactSet, says the company has responded to this trend by moving from an I.T. focus to a business focus, providing enterprise solutions to chief data officers beyond just providing data to various siloed departments.

The rise of alternative data

The influx of alternative data sets has made this even more important. Vendors now spend a lot of time on symbology: putting consistent, uniform identifiers at the level of the underlying security, like I.D. building blocks. This helps ensure investors receiving data from an array of sources can readily integrate it into their own systems.

“Investors spend a lot of time wrangling with the challenge of connecting new data sets,” he said.

Konyn says making sure data is clean is one of the biggest challenges to an organization. Consistency is another: for example, data for evaluating a stock isn’t the same as that used to price a derivative on it. Even something simple as a stock price is actually complicated, depending on factors such as whether the price was struck mid-day or at a market’s close.

“Downstream analytical tools need consistent management of data,” Konyn said. “It’s coming from different vendors, who use different methodologies.”

Bryan Lenker, vice president of content and technology solutions at FactSet, says vendors spend effort on “condordance”, ensuring data fits with existing master-security models – a task that has become more important with the rise of alt-data sets.

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Konyn says asset owners must ultimately put in place their own governance models to get the most out of data, which includes new standards to evaluate vendors as well as upskilling the firm’s people. AIA now has a data-management office function within its investment team.

“The use of data requires a design, knowing what’s required, and investing in governance, management, and systems,” Konyn said. Doing so allows organizations to know where to find what they need, and pull things together quickly.

It also allows investors to put controls on data, which means building in safeguards so that A.I.-driven investment decisions don’t breach risk budgets or portfolio mandates.

“We have vast pools of liabilities in different locations, matching different product lines,” Konyn said. “If we tried to do this manually, we could not implement investment decisions at scale.”

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