Finance and accounting departments provide mission critical services in every business — financial institutions as well as their clients. These departments generate financial statements, file tax reports, and support back-office operations like payroll, along with a host of other responsibilities.
The larger the organization, the greater the complexity of its financial reporting infrastructure. Public companies face higher degrees of scrutiny, stricter deadlines, and an expectation to hear from the CFO regularly about current performance and projections for the rest of the year.
Given their importance, it may be surprising to learn that many finance teams still perform key reporting tasks using manual processes. While it is true that most enterprise resource planning (ERP), core banking application, and other systems support at least partially automated workflows, it is quite common for accounting teams at banks or credit unions to use spreadsheets to “bridge the gap” between various systems and their final reports.
These manual efforts impact timely reporting data quality, incur hidden costs in employee morale and increase financial statement risk. Consider these statistics from The Hackett Group showing the difference in results between “World Class Finance Organizations” and their peers:
- 60% reduction in costs.
- 60% fewer invoicing errors from manual interventions.
- 17% more time analyzing data versus gathering data.
- 17% faster completion of budget cycle.
- 7% more FTEs with business acumen to engage with business partners.
Over the years, we have found that accounting teams are often the first within an institution to adopt a data preparation strategy, which consists of people, process and technology. They recognize the benefits of replacing error-prone manual processes dependent on spreadsheets with more robust and efficient solutions.
Automating data preparation tasks in bank accounting departments not only results in substantial savings, it greatly reduces the potential for errors to creep into final reports.
Here are three examples of how financial institutions can transform their financial operations by implementing a data preparation strategy.
1. Daily Settlement for Card Services
Consumers use debit cards and credit cards to purchase goods and services for their daily needs. It is a relatively simple concept for the consumer, and they are largely unaware of the complex ecosystem of issuers, networks, processors, acquirers and others required to support these transactions. Card issuers perform daily settlement to ensure that all necessary transactions are successfully posted to each card account. This in turn requires that correct values be posted to their general ledgers that roll up to their balance sheets and income statement.
This process is not often automated because it requires gathering data from many different sources in order to accurately report interchange (income), disputes, chargebacks, cash advances, and interest income. This data often resides in reports that originate with the card processor which makes them difficult to work with. This settlement process can take an accountant as much as 45 minutes per day to gather data from the reports, allocate them to general ledgers, and create the output for the Journal Entry. With a data preparation strategy, the process can be completed in approximately 5-10 minutes.
2. Investor Reporting for Mortgage Servicing
Mortgage servicing begins once a homeowner starts to pay down their mortgage balance. At that time a homeowner’s primary point of contact is the mortgage servicer who is responsible for obtaining mortgage payments and using them to pay homeowner’s insurance, taxes, and investor remittances.
Mortgage investors like Fannie Mae and Freddie Mac have strict guidelines for investor reporting and due dates for remittances. For example, they require loan level reporting on principal and interest, the exclusion of paid-off loans, exclusion of foreclosures, and immediate resolution of errors identified from prior reporting periods. The data to facilitate these tasks often reside in multiple sources and can take an accountant as many as 5-10 hours at month end to generate accurate and complete investor reports. With a data preparation strategy, the process can be completed in approximately 1-3 hours.
3. Payroll – 401(k) Plan Census
Financial institutions providing 401(k) plans must complete detailed census reports for their third-party administrators for compliance purposes, like non-discrimination tests. Inaccurate or incomplete data can result in a disqualification of the plan and the loss of an institution’s tax-exempt status.
Census data consists of personal information (name, address, date of birth, and the like), employment data (hire date, start date, base salary, etc.), and the contribution amount. The challenge is to efficiently gather this data from a variety of different sources and perform data validation and reconciliation.
For example, an accounting team can spend up to two hours to gather data contained in 401(k) reports and compare them against year-end payroll data and resolve any discrepancies. They must also identify highly compensated employees for non-discrimination testing. With a data preparation strategy, the process can be completed in approximately 30 minutes.
Altair’s 30-year history means we understand how analytics can help you unlock your data potential for added business value.