Could decision trees help improve Farm Service Agency lending decisions

ABSTRACT This study examines whether a statistically derived decision tree could serve as a means to improve U.S.A. Farm Service Agency lending decisions. The study is a substantial extension and reanalysis of an earlier work by Barney, Graves and Johnson, (1999). Results indicate that a decision tree could be a valuable tool for Farm Service Agency employees in their lending decisions. The decision tree provides as good or better predictive accuracy than neural networks and logistic regression models at reasonable cutoff levels of Type II to Type I costs of lending. The decision tree also meets the transparency criteria for Farm Service Agency purposes by providing logical, understandable rules for lending decisions. (ProQuest: . denotes formulae omitted.) INTRODUCTION The Farm Service Agency (FSA) directly loans, or guarantees loans to farmers totaling billions of dollars. The need for an understandable, accurate decision tool to assist FSA employees in their lending decisions is as great today as in the past. This article describes a substantial extension and reanalysis of an earlier work by Barney, Graves and Johnson (1999) examining Farmers Home Administration (FmHA) (predecessor of the FSA) lending decisions. Also, see Barney (1993) for a full description of the background and analysis. We do not recommend a loan classification system for immediate FSA use. Rather, we test whether a decision tree could potentially improve FSA lending practices, make lending decisions more transparent and be easily understood by applicants and the FSA staff. This study extends the earlier work by examining additional logistic regression models and neural networks and by investigating whether a decision tree could improve FSA lending decisions. This new analysis indicates that a decision tree could aid FSA employees in their lending decisions. The decision tree provides as good or better predictive accuracy than other methods, and provides logical, understandable rules for lending decisions. Section 2 ties this study to the prior Barney, Graves and Johnson (1999) study, briefly reviews FSA lending, and summarizes relevant literature. Then, research methods are described in Section 3, followed by discussion of results in Section 4 and conclusions in Section 5. LITERATURE REVIEW Relationship of This Study to Barney, Graves and Johnson (1999) The authors of the 1999 study used the newest methodology of that time (i.e. neural networks) to develop a model for FmHA use. This study investigates whether a better, possibly more accurate, fully transparent and interpretable methodology could now be applied by the FSA. This work extends the earlier work of Barney, Graves and Johnson (1999) by comparing a data mining technique, the decision tree, with the methodologies used in the 1999 study. Also, different logistic regression models and neural networks than those used in the 1999 study are developed. Two factors are central to a technique's usefulness for the FSA: (1) ability to clearly and accurately categorize potential farm borrowers between those who will make scheduled debt payments and those who will not make timely debt payments, and (2) transparency and understandability to borrowers and FSA employees. The FSA is subject to the provisions of the Equal Credit Opportunity Act (1975) and therefore must be able to provide a clear explanation to borrower applicants when the FS A denies them a loan. The 1999 study found that the neural network produced predictive accuracy superior to criteria developed internally by the FmHA (FSA), criteria developed by Price Waterhouse, logistic regression and ordinary least-squares regression models. Even so, operation of the neural network model was not transparent to FSA employees and borrowers. A neural network tends to work as a "black box" which would render lending decisions less subject to manipulation by loan applicants. However, that aspect of neural networks would make justifying a loan denial more difficult because FSA employees could not point to particular criteria as reasons for the denial. …