Average Daily Balance Method – Definition

Cite this article as:"Average Daily Balance Method – Definition," in The Business Professor, updated April 7, 2020, last accessed May 27, 2020, https://thebusinessprofessor.com/lesson/average-daily-balance-method-definition/.

Average Daily Balance Method

The average daily balance is a method of calculating interest rate by factoring the balance owed or invested at the close of each day, rather than at the close of the week or month. This accounting method is commonly used by credit card companies to calculate interest charges on credit cards using the total balance due at the end of each day.

A Little More on What is the Average Daily Balance Method

The average daily balance has an effect on the financial charges of credit card user at the end of each month. When this method is used, the total daily amount or balance for a particular period is divided by the total number of days in the period and multiplied by the monthly interest rate to determine a customer’s financial charge. To calculate monthly interest, the annual percentage rate (APR) of a cardholder is divided by 12.

Effect on Balances

The average daily balance gives consideration to the balance owed or invested daily when calculating the average daily balance. The balance at the end of each day in a billing period takes precedence over the balance invested at the end of a week, a month, a quarter or even a year. The billing period that the average daily balance calculates when assessing a customer’s average daily balance is a period of 30 days.

Lenders and borrowers can use this method to calculate interest, especially if compounding takes place.

Reference for “Average Daily Balance Method”

https://www.investopedia.com/terms/a/averagedailybalance.asp

https://www.thebalance.com › … › Credit Card Basics › Finance Charges

www.investorwords.com/351/average_daily_balance_method.html

Academics research on “Average Daily Balance Method”

Some ethical issues in computation and disclosure of interest rate and cost of credit, Bhandari, S. B. (1997). Some ethical issues in computation and disclosure of interest rate and cost of credit. Journal of Business Ethics16(5), 531-535. Although the mathematics of interest is very precise, the practice of charging computing and disclosing interest or cost of credit is full of variations and therefore often questionable on ethical grounds. The purpose of this paper is to examine some of the prevalent practices which are incorrect, illogical, unfair or deceptive. Both utilitarian and formalist schools of ethical theory would find these practices to be inappropriate. The paper will specifically look at unfair practices in the areas of estimation of intrayear rates, use of 360 days in a year, the “rule of 78th”, interest rate (‘APR’) advertising, and computation of unpaid balance by credit card issuers to figure interest costs.

Rate Limitations, Interest and Usury, Higgs, J. H. (1977). Rate Limitations, Interest and UsuryBus. Law.33, 1043.

Consumer Credit-Computation of Revolving Credit Finance Charges-Death and Rebirth of the Previous Balance Method in New York, Johnstone, J. M. (1972). Consumer Credit-Computation of Revolving Credit Finance Charges-Death and Rebirth of the Previous Balance Method in New York. Cornell L. Rev.58, 1055.

A Bayesian latent variable model with classification and regression tree approach for behavior and credit scoring, Kao, L. J., Chiu, C. C., & Chiu, F. Y. (2012). A Bayesian latent variable model with classification and regression tree approach for behavior and credit scoring. Knowledge-Based Systems36, 245-252. A Bayesian latent variable model with classification and regression tree approach is built to overcome three challenges encountered by a bank in credit-granting process. These three challenges include (1) the bank wants to predict the future performance of an applicant accurately; (2) given current information about cardholders’ credit usage and repayment behavior, financial institutions would like to determine the optimal credit limit and APR for an applicant; and (3) the bank would like to improve its efficiency by automating the process of credit-granting decisions. Data from a leading bank in Taiwan is used to illustrate the combined approach. The data set consists of each credit card holder’s credit usage and repayment data, demographic information, and credit report. Empirical study shows that the demographic variables used in most credit scoring models have little explanatory ability with regard to a cardholder’s credit usage and repayment behavior. A cardholder’s credit history provides the most important information in credit scoring. The continuous latent customer quality from the Bayesian latent variable model allows considerable latitude for producing finer rules for credit granting decisions. Compared to the performance of discriminant analysis, logistic regression, neural network, multivariate adaptive regression splines (MARS) and support vector machine (SVM), the proposed model has a 92.9% accuracy rate in predicting customer types, is less impacted by prior probabilities, and has a significantly low Type I errors in comparison with the other five approaches.

Implementation of the Fair Credit and Charge Card Disclosure Act of 1988: The Regulatory Response, Gelb, J. W., & Cubita, P. N. (1989). Implementation of the Fair Credit and Charge Card Disclosure Act of 1988: The Regulatory Response. The Business Lawyer, 1427-1438.