For financial services companies, the ability to tackle the convergence of sweeping regulatory changes and downward revenue pressure—and emerge stronger—could come down to a data-rich source hiding in plain sight: subledgers.
Despite being an essential repository for transactional data across operational systems, subledgers (also called subledger journals) haven’t been fully leveraged for richer analytics at most financial services companies. Reasons for this include the limitations of legacy systems and their complex data architectures, and prioritizing the development of digital customer experiences.
But with cloud technology and an accounting rules engine—software that allows finance to create and assign detailed accounting rules to business events—financial services companies can produce financial and operational reporting with subledger details intact. That means teams across the organization have the abilities to drill into profit drivers, resolve variances with drill back to the source data, determine risk, and support agility in financial forecasting, which are all necessary for navigating change and emerging stronger.
Legacy Solutions Are Inadequate but Hard to Replace
Insights into profitability are in subledgers, whether it’s a trading subledger, a banking product subledger, or a policy subledger. But pulling the data together from numerous subledgers is no trivial task. Legacy financial systems and their data architecture limit data-derived insights.
For example, banks and insurers have multiple operational systems, with different data formats and structures. Then mergers and acquisitions—common drivers for growth in financial services—add more operational systems and, consequently, more complexity to the organization’s data architecture.
Another source of complexity is the standalone systems and data architecture found at most financial services companies. A bank, for example, typically has one system to handle car loans, another to handle commercial loans, another for mortgages, etc. These operational systems are designed for a specific function and are typically isolated from other systems. So when these standalone systems transmit operational data to the general ledger for financial reporting, each loan system might send a different data file with different finance dimensions and level of detail depending on its integration with management reporting, finance, or risk systems.
This complexity drives many executives to say they’re not ready for a conversation about advanced analytics, a sentiment reflected in a conversation I once had with a banking chief information officer about their company’s analytics journey: “Analytics road map? I can’t even get my data organized in one place across lines of businesses.”
The lack of a central and reconciled data source pushes internal teams and IT to create their own solution by building detailed finance data in Excel or in another database. But this workaround actually creates more work all around the organization because internal teams created their own disparate sets of data, or multiple copies of the same data existed across the organization. These multiple “puddles” of data are just that—puddles that lack transparency and detail and, as a result, can’t be used or reconciled with financial reporting.