It’s All in the Details: How Data-Rich Subledgers Fuel Financial Agility

Accounting subledgers at financial services companies are data-rich resources hiding in plain sight, waiting to be tapped for insights. This article, the second in a two-part series, explores why combining operational and finance data is essential for business insights.

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. 

Legacy systems lack the technology to translate operational data into insights.

Understanding the Financial Impact of Day-to-Day Operations

Moving forward in a post-pandemic landscape, banks and insurers can no longer afford to rely on siloed workarounds. Most of all, these legacy solutions prevent finance from being a true strategic business partner—because it’s too busy gathering and cleansing data.  

“It’s no longer justifiable to maintain highly valuable resources generating backward-looking reports to the extent this has been done in the past,” say Ernst & Young (EY) writers in an article about the evolving role of the finance function in banking. “And although some members of the finance team may continue to be designated ‘business partners,’ trying to support business units through analysis and insight, the speed and focus of their support needs to meet expectations of time.”

Legacy systems can’t meet this need. Simply put, they lack the technology and architecture to translate operational data into insights without IT intervention.

Banks and insurers can no longer afford to rely on IT workarounds and manual reconciliation as a way to identify profit drivers and risks.

Intelligent Data Foundation Sets the Stage for Richer Insights 

Advances in cloud technology have paved the way for advances in data architecture. Among those advances is what Workday calls an “intelligent data foundation.” 

An intelligent data foundation enables financial services companies using Workday Accounting Center to easily ingest high volumes of operational data, add simple or complex calculations to enrich that data, create associated accounting, and provide rich self-service financial and operational analytics and visualizations—all while maintaining connection to the general ledger. Built on an analytic engine, in fact, it sets a new standard for insight and blends financial and operational data together for deeper analysis.

For a banking institution, a system with an intelligent data foundation maps data from every loan system into one view, greatly reducing the number of feeds and the reconciliation necessary for reporting and analytics. The same is true for insurance companies: A system with an intelligent data foundation creates one data source for policies, commissions, and claims accounting across actuarial, risk, and finance. 

This new standard for insight sets the stage for finance to produce richer reporting analysis, which means:  

  • Insurance companies can more easily analyze channel, product, and geographical profitability, and risk in a post-pandemic business landscape.

  • Banks can more nimbly conduct a risk analysis on customers by enabling their finance and risk teams to directly access data recorded at the loan level, such as FICO scores, delinquencies, and household identifiers.

  • Mortgage lenders are able to adapt to the changing requirements of regulatory and disclosure reporting, which is becoming increasingly complex due to the rise of loans in forbearance as a result of the pandemic and other natural disasters. 

  • With seamless access to historical data across operational systems, finance institutions can more easily comply with sweeping changes in regulations and accounting standards, such as Long-Duration Targeted Improvements and Current Expected Credit Losses.

  • Trading operations can more robustly support trade subledger reporting, product control, and daily trade reporting needs from one framework.

In a post-pandemic landscape, finance agility is in the details, and the details are in the subledger.

Moving Forward as a Future-Ready Organization

For a long time, financial services companies prioritized the digital transformation of their customer experiences. But in a post-pandemic landscape, finance agility is in the details, and the details are in the subledger. The ability to leverage this data-rich resource enables better reporting, better analysis, an audit trail, and more informed decision making across finance and risk. Armed with this capability, finance becomes a true strategic partner and, together with business leaders, can identify drivers of profitability, even amid a quickly shifting landscape. As a result, finance leaders can help the business thrive in a changing world.

This article is the second in a two-part series that explores why combining operational and finance data is essential for business insights. Read our first blog in the series, “How Banks and Insurers Can Turn Quantities of Data Into Quality Insights.”  

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