Applying the 1-10-100 Rule to Loan Management

April 2nd, 2019

data-4-2-19.pngImplementing new software may seem like an expensive and time-consuming challenge, so many financial institutions make do with legacy systems and workflows rather than investing in robust, modern technology solutions aimed at reducing operating expenses and increasing revenue. Unfortunately, banks stand to lose much more in both time and resources by continuing to use outdated systems, and the resultant data entry errors put institutions at risk.

The Scary Truth about Data Entry Errors
You might be surprised by the error rates associated with manual data entry. The National Center for Biotechnology Information evaluated over 20,000 individual pieces of data to examine the number of errors generated from manually entering data into a spreadsheet. The study, published in 2008, revealed that the error rates reached upwards of 650 errors out of 10,000 entries—a 6.5 percent error rate.

Calculating 6.5 percent of a total loan portfolio—$65,000 of $1 million, for example—produces an arbitrary number. To truly understand the potential risk of human data entry error, one must be able to estimate the true cost of each error. Solely quantifying data entry error rates is meaningless without assigning a value to each error.

The 1-10-100 Rule is one way to determine the true value of these errors.

The rule is outlined in the book “Making Quality Work: A Leadership Guide for the Results-Driven Manager,” by George Labovitz, Y.S. Chang and Victor Rosansky. They posit that the cost of every single data entry error increases exponentially at subsequent stages of a business's process.

For example, if a worker at a communications company incorrectly enters a potential customer's address, the initial error might cost only one dollar in postage for a wrongly-addressed mailer. If that error is not corrected at the next stage—when the customer signs up for services—the 1-10-100 Rule would predict a loss of $10. If the address remains uncorrected in the third step—the first billing cycle, perhaps—the 1-10-100 Rule would predict a loss of $100. After the next step in this progression, the company would lose another $1,000 due to the initial data entry error.

This example considers only one error in data entry, not the multitude that doubtlessly occur each day in companies that rely heavily on humans to enter data into systems.

In lending, data entry goes far beyond typos in customers' contact information and can include potentially serious mistakes in vital customer profile information. Data points such as social security numbers and dates of birth are necessary to document identity verification to comply with the Bank Secrecy Act. Data entry errors also lead to mistakes in loan amounts. A $10,000 loan, for example, has different implications with respect to compliance reporting, documentation, and pricing than a $100,000 loan. Even if the loan is funded correctly, a single zero incorrectly entered in a bank’s loan management system can lead to costly oversights.

Four Ways Data Entry Errors Hurt the Bottom Line
Data entry errors can be especially troublesome and costly in industries in which businesses rely heavily on data for daily operations, strategic planning, risk mitigation and decision making. In finance, determining the safety and soundness of an institution, its ability to achieve regulatory compliance, and its budget planning depend on the accuracy of data entry in its loan portfolios, account documentation, and customer information profiles. Data entry errors can harm a financial institution in several ways.

  1. Time Management. When legacy systems cannot integrate, data ends up housed in different silos, which require duplicative data entry. Siloed systems and layers of manual processes expose an institution to various opportunities for human error. The true cost of these errors on an employee’s time—in terms of wages, benefits, training, etc.—add up, making multiple data entry a hefty and unnecessary expense.
  2. Uncertain Risk Management. No matter how many stress tests you perform, it is impossible to manage the risk of a loan portfolio comprised of inaccurate data. In addition, entry errors can lead to incorrectly filed security instruments, leaving a portfolio exposed to the risk of insufficient collateral.
  3. Inaccurate Reporting. Data entry errors create unreliable loan reports, leading to missed maturities, overlooked stale-dates, canceled insurance and other potentially costly oversights.
  4. Mismanaged Compliance. Data entry errors are a major compliance risk. Whether due to inaccurately entered loan amounts, file exceptions, insurance lapses or inaccurate reporting, the penalties can be extremely costly—not only in terms of dollars but also with respect to an institution's reputation.

Reduce Opportunities for Human Error
An institution's risk management plan should include steps intended to mitigate the inevitable occurrence of human error. In addition to establishing systems of dual control and checks and balances, you should also implement modern technologies, tools, and procedures that eliminate redundancies within data entry processes. By doing so, you will be able to prevent mistakes from happening, rather than relying solely on a system of double-checking.


Scott McCarthy is the Vice President of Sales at Built Technologies, leading Built’s sales organization in support of growing demand for Built’s construction lending software.