12 Jan A Cross-Report Reconciliation Success Story Case Study
In the increasingly complex, interrelated regulatory arena, financial institutions must submit correct data for individual reports and connect the dots between and among reports. To accomplish this, they must wrangle data from different lines of business at various levels of granularity and consolidation and make that data correctly land in multiple regulatory reports – with accuracy and consistency that stands up to intense regulatory and audit scrutiny for reconciliation.
Recently, US regulators audited many banks’ liquidity regulatory processes covering their FR 2052a submissions finding that many were not correctly reconciled to the comprehensive FR Y-9C report. Indeed, many large G‑SIBs and BHCs received matter requiring attention (MRA) letters – forcing them to prioritize how to address the lack of reconciliation. However, reconciling each exposure across numerous filings can be an ambiguous, time-consuming ordeal, which can result in audits that are difficult and costly to defend.
Collaborating closely with clients, AxiomSL recognized the extent of reconciliation challenges. Leveraging its expert knowledge of these complex reports, the team quickly identified the reconciliation points and created a new application to address these reporting obstacles.
Running on the ControllerView® data integrity and control platform, Cross-Report Reconciliation automates reconciliation between FR 2052a and FR Y-9C and performs reconciliation across additional prioritized regulatory reports. Its workbench functionality enables users to easily pinpoint and adjust granular differences between the reports in reconciliations, thereby enabling transparency and auditability.
With Cross-Report Reconciliation, clients were able to resolve regulatory matters related to FR 2052a and FR Y-9C reconciliation and establish an efficient, automated process to ensure ongoing compliance. Firms can also invoke reconciliation in alignment with their reporting-cycle needs, automating what previously were manually reliant, non-repeatable, yet crucial processes.