Automating Commercial Credit Monitoring

By Jeff R. Schmidt and Kristen N. Sharpe, CPA 
| 2/27/2017

Commercial lenders can save costs, improve operational efficiency, and reduce their exposure to risk by automating some of the manual processes that are used to verify borrowers are complying with loan covenants.

Instead of manually tracking borrowers’ financial statement submissions and then keying that data by hand into their spreading software, a growing number of commercial lenders are discovering that automating these processes allows their credit analysts to devote more of their time and attention to performing actual analyses. This article will examine the need for automation at the front end of the credit monitoring processes, the potential cost savings and risk management benefits an automated system can offer, and some critical features to look for when considering such a solution.

Business Challenges

Tracking, reviewing, verifying, and keying the periodic financial statement submissions of commercial borrowers can present lending organizations with significant challenges, causing them to incur unnecessary costs and risks, while encumbering credit analysts with time-consuming manual chores.

Some of the most common challenges associated with the manual tracking of financial statement submissions include:

  • Unclear borrower documentation expectations. Borrowers generally are not focused on keeping up with the details of statement submission requirements. When reminded, many simply update what they recall submitting in the previous cycle. Analysts often must spend considerable time chasing down delinquent submissions.
  • Inconsistent borrower submissions. Because statement submissions are periodic or intermittent, their formatting and structure can be inconsistent and variable, causing analysts to spend time normalizing or reformatting the data.
  • Lack of alerts and reminders. The statement submission process often can involve several handoffs among borrowers’ and lenders’ personnel. Systematic reminders and alerts can be needed to keep the process on track.
  • Lack of process auditability. Auditors often express concern over an inability to view, track, and verify the information that was used to make credit decisions.
  • Lack of secure borrower submission options. Because most submissions are now done electronically, inadequate security for email and data systems can be a concern for borrowers and lenders alike.

Once the financial statement data is submitted, relying on manual data entry processes can trigger additional challenges, including:

  • Waste due to data entry time. Keying data by hand into spreading software or a document management system inherently is time-consuming. In many instances the same data needs to be manually entered several times into disconnected systems.
  • Inconsistent or error-prone data entry. Manual data entry also inherently is subject to error. At a minimum, keying errors can trigger rework and added costs. In the worst case, a manual keying error could adversely affect critical credit decisions if undetected.
  • Lack of end-to-end tracking. Manual entry also makes it more difficult to track specific data throughout the overall credit management process.
  • Difficult handoffs in the event of employee turnover. Credit analysts constantly are turning over. When a trained credit analyst leaves, relationship managers often must expend considerable time over a period of several months familiarizing the replacement with the intricacies of each borrower’s manual statement submissions.

Quantifying the Costs

The shortcomings of manual data entry systems are widely recognized among lenders – but the costs often are not. For example, in a recent Crowe webinar on credit process automation, 60 percent of the participating lending executives cited data entry problems – either time wasted in data entry or inconsistencies and errors – as acute pain points in their operations (Exhibit 1).
FS-17012-029E-Fin-Statement_graphics-01However, when the same participants were asked about the total costs they incurred in manually keying borrower financial statement data, 61 percent said they didn’t know. Even a conservative estimate of 30 minutes of data entry time for a typical statement would suggest the costs and the associated resources required are significant.

In an economic downturn, when default risk increases, banks often are not prepared to invest the additional time necessary to monitor borrowers more frequently, even when such oversight is needed to prevent significant losses. Conversely, during periods of robust growth, banks can find that the burden of manually processing borrower data in their rapidly expanding portfolios imposes severe strains on their available resources. This burden can prevent banks from monitoring their borrowers on a more frequent schedule, which reduces the likelihood that the bank will catch an emerging issue in time to take preventative action against a loss.

The costs associated with manually tracking financial statement submissions can be even more difficult to quantify. Most lending organizations lack a comprehensive statement tracking system, relying instead on standard office spreadsheet software or document management systems. Yet such systems, which are designed for storing and organizing data once it’s collected, offer very little help with the actual collection of the data or the tracking of data submission requirements.

Shaping a Solution

In developing a technology solution to streamline the front end of the credit management process, it is important for lenders to make sure the solution offers clear visibility into various aspects of both the statement submission and data entry processes. The solution should provide clear answers to fundamental statement tracking questions, such as:

  • Which borrowers did not submit required information on time, and what actions were taken in response?
  • Which borrowers’ submissions were waived and why?
  • When was the required information submitted?

The solution also should provide visibility into critical aspects of the data entry process, answering questions such as:

  • What were the actual financial data values as reported by the borrower?
  • What decisions were made by the analyst during the spreading process?
  • How soon was the data available for review?

At a minimum, a comprehensive front-end system will employ a secure online portal for customer submissions, with automated tracking of submission status and automated follow-up notification of noncompliance to customers and credit analysts. The system also should convert customer-submitted data into the appropriate format for integration into the lender’s spreading system. These capabilities should be regarded as the bare-minimum requirements for an automated system.

Desirable System Features

Beyond the bare-minimum requirements, other desirable features to look for when choosing an automated financial statement tracking and data entry system include:

  • Advanced portal security for borrower submissions
  • Ability to either integrate with an existing lender portal or establish a dedicated new portal
  • Automated data extraction and spreading for financial statements and tax returns
  • Ability to integrate completely with existing spreading and risk management systems
  • Clear audit trails and tracking of data-mapping decisions
  • Strong customer support capabilities

Seizing the Opportunity

Industry experience suggests a well-designed and correctly implemented automated front-end system could enable a lender to achieve significant savings in time and resources.

For example, “teaching” the system – that is, mapping the borrower’s financial statement data to the right spread line items by performing the initial spread – generally can be accomplished in less than 10 minutes. Once that is accomplished, subsequent statements that might require exception handling typically require about two minutes of an analyst’s time for each statement.

Using that standard, compared to the estimated handling time of 30 minutes per statement that industry experience suggests, the time savings achieved through automated data entry of even a modest-sized portfolio of 5,000 accounts could be substantial, as demonstrated in Exhibit 2. 
FS-17012-029E-Fin-Statement_graphics-02 It also is important to note the data-entry cost savings are only one part of the total picture. Other benefits – such as reduced errors, greater analyst efficiency, and improved tracking and reporting capabilities – make the automation of financial statement tracking and data entry even more beneficial.

Every reporting cycle in which analysts must rely on cumbersome manual systems represents a significant cost – and an unnecessary and avoidable risk. 


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Jeff Schmidt
Jeff Schmidt
Kristen Sharpe
Kristen Sharpe