Oracle has launched its new Solvency II application, Insurance Quantitative Management and Reporting for Hyperion Financial Management, which the company says streamlines compliance with Solvency II reporting mandates and enables carriers to better manage risk, finance and capital.
As part of the Solvency II regulation, European insurers must provide quantitative management reports to industry regulators, such as the recent Quantitative Impact Study 5 (QIS5) and pending Quantitative Reporting Templates (QRT).
Oracle’s new tool enables insurers create quantitative management reports automatically, eliminating the need for labor-intensive processes and error-prone manual spreadsheets, as well as improve their risk and financial processes and reporting systems.
These reports are provided to industry regulators, such as the recent Quantitative Impact Study 5 (QIS5) and pending Quantitative Reporting Templates (QRT), under the Solvency II regulation.
The application automatically downloads and collates financial, capital and risk data from multiple sources, including auditable spreadsheets and manual inputs.
Oracle Insurance Quantitative Management and Reporting also helps simplify the creation of other reports required under the Solvency II mandate including Solvency Financial and Condition Reports (SFCR) and Report to Supervisors (RSR).
Oracle notes that its new application provides currency translation, intercompany elimination, and equity elimination and reconciliation and provides automated workflow for standardised and auditable approval and sign-off.
Built on Oracle Hyperion Financial Management, the new offering integrates into existing reporting and financial systems, and meets the quantitative reporting requirements of Solvency II, with automated reporting, audit trails and dashboards.
The application gives insurers quicker insight into risk and capital information with reporting tools such as risk dashboards and key performance indicators, and with built-in validation routines including cross checks on input data, inconsistencies can be highlighted early in the process, increasing accuracy.
