The Buy Side's quest for 'look through' to understand investment flows & underlying investors

The Buy Side’s dilemma The existential question facing the buy-side is how to grow assets and service clients at lower cost. This drive for operational efficiency is dependent on a capability to collate accurate data about investors and decision makers, including their influence over the flow of money into their investment products. Industry leaders are investing in the capability to ‘look through’ omnibus accounts and fund distributors to create differentiated client experiences. Meanwhile, laggards are struggling with a partial view of sales performance and client engagement. "It costs us thirty dollars each year to disaggregate or 'look through' a million dollars of omnibus assets to the underlying investors and decision makers. We need that level of data precision to make the analytics powering our client insight really sing" - Managing Director, Global Asset Manager Specifically, the challenge is to assemble economic data about the activity of the end investor and decisions makers in a form that supports 160+ use cases across the enterprise which Aiviq has identified through its research. An example of one of these 160+ use cases is the regulatory requirement to understand revenue concentration in a fund by decision maker. Investor flows and assets under management then have to be combined with demographic data about the investor organisation and the decision makers within them. This is a more complex problem than it first appears for the following reasons: • Intermediation between fund managers and their investor clients by an increasing number of fund platforms and distributors. Now investment managers and asset owners are obliged to process hundreds of different data sources - typically monthly but in some cases weekly or daily - to build a composite picture of total assets and flows by client and by product. • Poor data quality and consistency - inconsistent and incomplete data about investment flows and holdings across the 'hundred plus data sources' that need to be enhanced and combined each month before they are suitable for analytics • The widespread use of the financial 'account' as the data entity to capture data about investment holdings and flows. Particularly in Europe, this has created an expensive 'data matching' problem for the buy-side to link 'account’' data to ‘client’ data – often by inference rather than explicit keys. “The investment industry’s data structures are hard wired to operational ‘accounts’ but creating client insight requires organising data around a commercial view of ‘client’ that requires a massive amount of data manipulation and costs the industry USD 100mn each year in avoidable inefficiency” - Greg Glass Head of Revenue, Aiviq. • The introduction of the omnibus account which, without further data processing aggregates and 'masks' the identity of the underlying investor and decision makers • Lack of agreement on industry standards for data - particularly those describing clients, products and investment flows Leaders are investing in strategic data management and analytics platforms So how are leading buy-side firms solving this forensic data conundrum to understand the economic performance of end investors? Industry leaders are buying data management andanalytic platforms with the following components and capabilities: • Straight-through ETL - Actionable buy-side investor insights need to be accurate and timely. The industry norm of monthly reporting is no longer fit for purpose. Aiviq's highly-tuned ETL layer caters for the thousands of permutations transaction data can take across the 200+ sources we typically process. Our orchestrators trigger complex transformation and enrichment rules to run as trades arrive to avoid missed client activity or surprises close to business or regulatory reporting deadlines. • Extensible data quality framework - With a web of investor positions and flows to reconcile across different investment vehicles, data quality needs to be in focus before advanced analytics use cases can be tackled. The ability to add and re-configure hundreds of data quality controls means the Aiviq platform can stop repeat offenders and learn as our data coverage grows. • Intelligent account matching engine - Time spent matching financial and omnibus account data to internal client lists can be worthwhile for high-value investors. However, the Aiviq platform makes this an optional enrichment activity rather than painful pre-requisite though the deployment of our trained matching algorithms and client reference datasets we maintain to cleanse and consolidate key information on the major buyers of investment funds across the UK, Europe and APAC. “Matching ‘account’ to ‘clients’ without any explicit key is a complex data problem and one of the key features of the Aiviq platform that resonates with our clients” - Luke Tones, Head of Product, Aiviq • APIs and integrations - Timely, accurate data needs to be available to consumers at the point of use. For many of Aiviq's investment managers, the point of use is either CRM, Enterprise Data Warehouse, Business Intelligence or Financial Planning and Analysis (FP&A) solutions and Aiviq has documented integration patterns that deliver tailored insights to business consumers at the most relevant point of use. • Use-case driven analytics - Management dashboards and analytics platforms are proliferating. However it’s critical to understand which performance indicators are worth measuring. Aiviq has experience working across the data and analytics value chain. This, combined with Aiviq’s deep understanding of 160+ use cases across finance, distribution, product, risk and the front office al. • Security-aware, cloud-native architecture –There are few information assets more sensitive to buy-side firms than their clients’ data, including positions, flows, fees and sensitive account and portfolio information. The Aiviq platform is fully ISO:27001 certified and deploys our advanced security framework across the breadth of our cloud-native architecture to steward sensitive data, providing managers with the confidence to outsource the solution to this common industry challenge.