Asset servicing companies handle huge volumes of data, and today are leveraging Big Data technologies to transform it into valuable business insight services.
Data Lakes, the term for the massive pools of information generated by funds’ day-to-day activities, are the defining element of Big Data technology. However, the quality of this data is the foundation of the entire activity and much care must be taken to ensure that all sources of data are reliable in order to guarantee accurate results and generate a genuine data warehouse of standardised data.
Data lakes are not only populated from information generated within the asset servicer, but are also sourced from other service providers, fund managers themselves, external data providers, information on social networks, plus economic indicators or any other information that may be relevant. Data lakes generally hold up to several years’ worth of historical data, and are fed with information as close to real time as possible, which enables institutional investors and fund managers to query up-to-the-minute data.
Data Analytics can be deployed in many areas to assist institutional investors and fund managers, but a key area is financial reporting. The financial industry is particularly concerned by the exponential growth in data to be managed. In the wake of new financial reporting regulations, in particular MiFID, Basel III, Solvency II and AIFMD, institutional investors and investment management companies have had to confront massive data management and analysis tasks in order to meet transparency requirements in terms of information and reporting to the relevant national authorities. Big Data technology’s capabilities enable them to make queries and generate accurate reports on look-through, performance and risk, and regulatory ratios, quickly and reliably, permitting them to focus on their core business of generating investor value.
Data analytics can also be used to provide reference indicators for investment management companies in order to refine their sales strategy. For example, ex-post analysis of subscriptions and redemptions will allow the determination of a correlation between investor behaviour and a fund’s performance relative to its benchmark. This information can be of valuable assistance in establishing a fund’s commercial positioning. Such reports will enable investment management companies to achieve more in-depth marketing analysis (inflows, investor behaviour, distribution network etc.). In addition, analysis can distinguish by type of final investor, country and distributor, permitting investment management companies to better determine their target clients and the optimal distribution network.
Investor behaviour analysis gives investment managers even deeper insight into their customers’ actions. Utilising data from Social Networks such as Twitter and Facebook, data analytics tools can provide answers to soft questions on subjects such as brand visibility, both of the investment manager’s own brand and that of its competitors. The technology also enables sentiment analysis that helps asset managers answer questions such as ‘how are social networks talking about the brand?’ and ‘what is the resulting impact on order collection?’ Such insight becomes increasingly important as the younger, more internet-savvy population mature and become potential investors, who are keen to use the internet to seek information on and discuss investment opportunities via online platforms.
Along with financial reporting and fund distribution analysis, data analytics provides another advantage for those investment managers working with an asset servicing provider. They can generate Key Performance Indicators on the provider’s performance, allowing them to view statistics on NAV calculation performance and settlement performance in real time. The flexibility of the open platform enables managers to query the data in so many ways that the information can be precisely tailored to the manager’s needs, and the results are generated and displayed in clear and accurate reports in seconds.
The powerful tools that enable data analytics on big data use vast amounts of processing power locally, but can be securely accessed through a flexible web interface or even applications designed for mobile devices. This allows financial reporting, fund distribution and KPI analytics to be handled around the clock and no matter where in the world you may be.
Furthermore, the data lakes, analytics algorithms, and the systems it runs on are constantly growing more powerful, which means the possibility for insight opportunities and new services are constantly evolving. To stay ahead of the competition, and remain at the forefront of technology, data analytics are key, and service providers are keen to share their expertise in the field with the investment management community.
Companies such as CACEIS offer all such services to its investment management clients. Such clients are not, however, just provided with the tools and left to work out how best to put them to use. Our experts are on hand to assist users at all levels in defining their exact needs, and in setting up the reports using either the web-based tool or the mobile application.
The data analytics tool is very simple to use, so clients rapidly understand how best to benefit from its powerful capabilities to gain insight into their business and produce the reports to assist strategic decision-making. As innovation is a core tenet in CACEIS’s business development objectives, we are constantly adding new features to the data analytics service in order to increase its insight potential for the asset management community.
Arnaud Misset, Group Product Director, CACEIS
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