Quick Stab Collection Agency: a Regression Analysis
Quick Stab Collection Agency: A Regression Analysis
Gerald P. Ifurung
04/11/2011
Keller School of Management
Executive Summary Every portfolio has a set of delinquent customers who do not make their payments on time.
The financial institution has to undertake collection activities on these customers to recover the amounts due. A lot of collection resources are wasted on customers who are difficult or impossible to recover. Predictive analytics can help optimize the allocation of collection resources by identifying the most effective collection agencies, contact strategies, legal actions and other strategies to each customer, thus significantly increasing recovery at the same time reducing collection costs. A random sample of accounts closed out during the month of January through June will be used in determining if the size of the bill has an effect on the number of days the bill is late. The statistical analysis of the data involves the application of regression analysis. Based on the calculated value of correlation coefficient, there is no relationship between the size of the bill and the number of days to collect. .
Introduction The author was hired by the Quick Stab Collection Agency (QSCA) on a contractual basis to assist the company in auditing potential business in buying the rights to collect debts from its original owners. QSCA is a collection agency that specializes in very profitable small accounts and avoids risky collections. Profitability at QSCA depends critically on the number of days to collect the payment and the amount of bill, as well as on the discount rate offered. A random sample of accounts closed out during the month of January through June will be used in determining if the size of the bill has an effect on the number of days the bill is late. The statistical analysis of the data will involve the application of regression analysis. The analysis will determine if the size of the bill
Gerald P. Ifurung
04/11/2011
Keller School of Management
Executive Summary Every portfolio has a set of delinquent customers who do not make their payments on time.
The financial institution has to undertake collection activities on these customers to recover the amounts due. A lot of collection resources are wasted on customers who are difficult or impossible to recover. Predictive analytics can help optimize the allocation of collection resources by identifying the most effective collection agencies, contact strategies, legal actions and other strategies to each customer, thus significantly increasing recovery at the same time reducing collection costs. A random sample of accounts closed out during the month of January through June will be used in determining if the size of the bill has an effect on the number of days the bill is late. The statistical analysis of the data involves the application of regression analysis. Based on the calculated value of correlation coefficient, there is no relationship between the size of the bill and the number of days to collect. .
Introduction The author was hired by the Quick Stab Collection Agency (QSCA) on a contractual basis to assist the company in auditing potential business in buying the rights to collect debts from its original owners. QSCA is a collection agency that specializes in very profitable small accounts and avoids risky collections. Profitability at QSCA depends critically on the number of days to collect the payment and the amount of bill, as well as on the discount rate offered. A random sample of accounts closed out during the month of January through June will be used in determining if the size of the bill has an effect on the number of days the bill is late. The statistical analysis of the data will involve the application of regression analysis. The analysis will determine if the size of the bill
References: Weaver, B. (2004) Online Source. http://www.angelfire.com/wv/bwhomedir/notes/linreg.pdf