FiREapps’ PaaS and Saas applications provide a holistic offering around currency analytics, bridging the gap between disparate data sources and providing highly actionable currency analytics.
The platform derives its power from FiREapps’ proprietary analytical engine, the FiREapps FlexEngine™.
This core is equipped to analyze large volumes of currency data from multiple data sources. It removes the need for human input by providing automated and actionable insights and explains the results through reports and recommendations, providing valuable insights to all stakeholders.
Analytical Views & Modeling
FiREapps provides analytics that facilitate an understanding of the sources and drivers of your exposures on a consolidated, entity, account group, or currency level. We do this through extensible views which can be rendered on the basis of a number of additional dimension (see Flex engine and dimensions below).
The FiREapps platform is capable of rendering a multitude of analytical views, across a number of exposure data sets. The following is by no means a comprehensive list, though it is representative of many of the views we power for our clients on a daily basis.
- Consolidated & detailed exposure analytics
- Flexible period-over-period and trend analytics
- Flexible user-designed pivot analysis
- Dynamic gross & net VaR calculation and analytics
- Actual by Period & Multi-period Forecast of revenue and expense based exposure
- Flow, Impact & Variance Analysis of revenue and expense based exposure
- Variance Analysis & Comparison of balance sheet exposure
FiREapps FlexEngine™ brings an un-paralleled depth to exposure data analytics through a dynamic analytic environment that goes well beyond conventional business intelligence framework(s).
Traditionally, data extraction and exposure calculation(s) rely on the hierarchical data structures of ERP systems and data warehouses to calculate and model—constraining what can be done with the imported data. FiREapps’ FlexEngine™ intelligently digests existing hierarchies and separates the data into discrete units allowing for grouping, pairing and netting of exposure(s) across a wide variety of dimensions (business, cost/profit center, geography, time, etc).
Further Examples of the Power of Dimensions:
- View exposure across multiple hierarchies:
- E.g. group entities by region and/or business unit
- E.g. Product line, cost center or profit center
- View exposure at multi-level hierarchies
- E.g.: Corporate > Industry > Business > Sub-business > Entity
- Fine Grain (Transaction Level) Exposure Analysis
- E.g. import invoice number in a dimension to view VaR and Long/Short by transaction
- E.g. import customer ID to view exposure created from a customer
This proprietary process delivers a level of data interactivity un-paralleled by any market offering. In effect, the FiREapps’ FlexEngine™ powers analytics that can accommodate your institutions’ workflow.
FiREapps’ solution is architected to accommodate Currency programs built around actuals, forecasts and a hybrid of both.
The FiREapps platform is equipped to interface with and handle multiple disparate data sources and types including but not limited to general ledger data from ERP systems and data warehouses; manual/spreadsheet based forecasts; and data from treasury management systems, trading platforms and treasury workstations (see our connectivity page for more detail).
Accurate data is the foundation of effective exposure management. Thus a fundamental tenant of the FiREapps approach is to examine the integrity of the underlying data used in exposure calculation.
Unfortunately, in many cases the complexity of reporting environments and scale of many of our clients creates accounting idiosyncrasies, errors and errant balances in their financial data. As a result, simple extraction, without validation leads to inaccurate foundational data. These inaccuracies can, and often do, lead to the creation of more exposure despite happening in the process of trying to manage exposure down to an acceptable threshold.
The FiReapps platform has multiple approaches to data integrity analysis depending on the type and source of your exposure data. When working with balance sheet actuals (from your ERP or manually upload), we have a multi-step, rules based process that validates account balances, filters out unused accounts and checks for inconsistencies in transaction currency versus local currency balances. We can and do tailor this process to the nuances of your reporting process and environment. Additionally, when working with a forecast, we have multiple toolsets to perform and model variance analysis against your set of actuals or against a prior forecast. Whatever your process—we have data integrity tools to help you improve the quality and accuracy of your exposure data set.