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Last month we noted that Amazon is disrupting the industry's distribution model by soliciting Chinese OEM producers to bypass the distributor and sell direct to consumers, using the Amazon website [...]

Semi-customized credit-scoring systems provide an alternative to traditional credit-scoring systems. Traditional credit-scoring systems are expensive, aggravating, dehumanizing and have a banking orientation. A semicustomized credit-scoring system is more powerful than a traditional scoring system since it can increase the sum of charge-off savings, delinquency savings and loan-portfolio revenues, produces membership change and keep data integrity., The increasing incidence of charge-offs and bankruptcies within the credit union movement is well-documented. The median 1992 credit union charge-off rate of 0.65%--a figure well below that for banks--conceals a [...]

To make good commercial lending decisions, loan officers need useful information about certain critical variables. The judgment of whether a customer is creditworthy is based on a lender's assessment of a borrower's ability to repay the loan and to repay it within a certain time period. The judgments are based on information the lenders have at their disposal. In this article, the authors examine the information needs of commercial lenders and how this information is used. (Reprinted by permission of the publisher.)

The purpose of the paper is to investigate the predictive power of feedforward neural networks and genetic algorithms in comparison to traditional techniques such as linear discriminant analysis and logistic regression. A particular advantage offered by the new techniques is that they can capture nonlinear relationships. Also, previous studies and a descriptivedata analysis of the data suggested that classifying loans into three types—namely good, poor, and bad—might be preferable to classifying them into just good and bad loans, and hence a three-way classification was attempted.Our results indicate that the traditional techniques compare very well with the two new techniques studied. Neural networks performed somewhat better than the rest of the methods for classifying the most difficult group, namely poor loans. The fact that the Al-based techniques did not significantly outperform the conventional techniques suggests that perhaps the most appropriate variants of the techniques were not used. However, a post-experiment analysis possibly indicates that the reason for the new techniques not significantly outperforming the traditional techniques was the nonexistence of important consistent nonlinear variables in the data sets examined.

Although credit-scoring models represent a widely used managerial aid for large financial intermediaries, the vast majority of U.S. credit unions—relatively small cooperatively owned retail intermediaries, constrained by sample and funding limitations—have yet to adopt such techniques. Lovie & Lovie (1986) have theorized that the flat-maximum effect or curve of insensitivity associated with linear scoring models could be advantageous in areas of applied prediction such as credit scoring. In this context, we reported the relative predictive power of generic credit-scoring models versus customized models in an earlier paper (Overstreet et al. 1992). Unfortunately, these findings were not readily adaptable to the credit-union industry due to a dated sample with incomplete credit-bureau information. Consequently, from 1988 to 1991, we gathered a refined database from which to further develop and field-test generic scoring models in the credit-union environment. The results reported herein not only confirm, but amplify, the relative predictive power of such models found earlier. Relative costs and benefits of generic versus customized models are modelled for a representative credit union. Future research directions are set forth in the conclusions.

The addition of a second expanded-duty dental assistant to a practice can substantially increase a dentist’s productivity. However, the greatest incremental increase in revenue was found in the addition of one expanded-duty assistant and one chairside assistant to the practice of a dentist employing two traditional assistants. The average theoretical revenue per day in the surrogate practice involving one dentist, two expanded-duty assistants, and four chairside assistants was almost 64% greater than that produced by the dentist with two chairside assistants, and 19% greater than that produced by one dentist, one expanded-duty assistant, and three chairside assistants. The addition of one expanded-duty assistant enabled the dentist to treat 4.58 more patients a day, whereas the addition of two assistants enabled him to treat 10.0 more patients a day.

Comparisons of time, productivity, revenue, costs, and income for a dentist working with two chairside assistants, or a dentist working with one therapist and three chairside assistants were made in this analysis of a surrogate private practice. Computations show that a team composed of a dentist, a therapist, and three chairside assistants can produce 26% more services than a dentist and two chairside assistants can provide. The income of a dentist grossing more than $120,000 can be increased by 37% if one therapist and one chairside assistant are added to the staff and sufficient patient demand exists.