Data Aggregation is Not Enough When it Comes to Investment Accounting

“Data is king.” -Quoth everyone, everywhere

This isn’t a new concept, and it is true across every part of the industry, particularly in the asset management space where the sheer breadth, depth and volume of data is ever-expanding alongside ever-more complex operational workflows.


Take investment accounting data, for instance. Think about fund managers who need to manage and reconcile their various activities across their investment book of record, performance book of record, risk management workflows and decision-making workflows. Layered into those workflows are different asset classes, jurisdictions, reporting requirements and more.


Traditionally, a fund manager deals with up to four distinct books of record for a single portfolio: 


  • The Investment Book of Record (IBOR) for tasks like trading and risk analysis
  • The Accounting Book of Record (ABOR) for calculating daily Net Asset Value (NAV) and producing financial records (which is often outsourced)
  • The Contingent NAV Book of Record (CNAV) for oversight and contingency
  • The Custodian Book of Record (CBOR) maintained by custodian banks for safekeeping assets and tracking real cash. 
  • The Performance Book of Record (PBOR), which is a superset of all the data that enables performance analysts to answer the tough questions of lineage, explain small differences across accounting basis (ABOR vs IBOR, for example), and uncover the true sources of alpha for a given strategy.


With each of these books of record traditionally kept on different accounting platforms, managed by separate teams, the daily reconciliation process, syncing data across the IBOR, ABOR, CNAV and CBOR and PBOR, becomes a complex and error-prone task.


Fortunately, in more recent years as the industry has come to embrace cloud technology, it is now possible to leverage cloud-native data aggregation and data manufacturing capabilities to help solve for the complexity of managing investment accounting data. 


In a previous blog we briefly touched on the difference between data aggregation and data manufacturing. Here we dive a bit deeper to discuss that distinction and why it matters specifically to investment accounting. 


First, a recap of the differences between Data Aggregation and Data Manufacturing


In the data aggregation process, information is meticulously gathered and organized into a concise summary or report, maintaining traceability to its original sources. While data aggregation offers advantages to many aspects of the investment management process, it also comes with notable drawbacks when it comes to more complex operational layers like investment accounting, where routine changes to underlying source accounting data must be constantly updated.


Unlike data aggregation which simply gathers existing data from across multiple sources, data manufacturing is the process of generating new data from raw data inputs. In the realm of investment accounting, data manufacturing can take raw accounting data and transform it into essential components such as positions, tax lots, general ledger balances, NAV records, and performance reports.


Fund managers who can effectively harness and manufacture data from their investment accounting operations will gain a competitive advantage, especially in their front and middle-office operations, and in managing their IBOR. And integration of this mission-critical data into middle and front-office decision support systems demands an investment accounting engine capable of processing data in real time and delivering comprehensive, granular information to the relevant applications. 


However, not all investment accounting solutions are created equal, so when assessing the actual “capital A” Accounting capabilities of your current and prospective providers’ solutions, the first question to be asked of any legacy, on-prem solution or modern cloud-native system is this: “Does your investment accounting system truly support multiple books and multiple asset classes on a single platform?” 


You might also ask: 


  • Does your investment accounting solution include exception controls that evaluate upon entry?
  • Is your investment accounting solution able to handle an ongoing stream of exponentially larger volumes of trades across different asset classes?
  • Does your investment accounting solution have an entry to end-point engine to generate all calculations? 
  • Can your investment accounting solution provide an all-in-one view of all books of record, all from 1 source, any day, any time?
  • Can your investment accounting solution calculate actual cash, contractual cash, traded cash and projected cash?
  • Within the general ledger of your investment accounting system, can you view granular updates per event & activity, including all portfolio details?  
  • Is your investment accounting solution able to create a new NAV/TNA upon every event and/or activity?


Can Your Investment Accounting Solution Do This?


FundGuard’s Product Management Director, Peter Muldoon, has created another handy heatmap to help distinguish between barely-there investment accounting engines and the capabilities of legacy investment accounting solutions vs. a modern, cloud-native engine.


A heatmap in shades of red/amber/green showing distinction of legacy vs modern tech investment accounting


FundGuard firmly believes that organizations should embrace an investment accounting approach rooted in a comprehensive data manufacturing model—one that doesn’t rely heavily on other accounting systems. This approach paves the way for achieving true real-time integration across the front, middle, and back-office functions, ultimately driving operational efficiency and scalability. 


Our cloud-native, multi-book investment accounting solution manufactures real time, clean and accurate investment accounting data from raw inputs, and then feeds that single source of truth across the enterprise, delivering fit-for-purpose data for different business views without additional steps by the fund manager to first engineer that data. 


Visit our product page to learn more and request a demo.


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