Accounts Receivable (AR) can be a source of financial difficulty for firms when they are not efficiently
Question:
Accounts Receivable (AR) can be a source of financial difficulty for firms when they are not efficiently managed. Research shows that effective management of AR and the overall financial performance of firms are positively correlated. As such, the problem of reducing outstanding receivables through improvements in the collections strategy has drawn the attention of scholars as well as practitioners in the accounting field.
Your task is to develop a Decision Tree model that can predict with high accuracy if an invoice will be paid on time or not and provide estimates of the magnitude of the delay. For a better assessment of your model, use Cross-Validation in your model. Include all variables in the model.
Use the Optimize Parameters operator and/or the MetaCost operator to reach the best results
By using these operators, I reach to overall 89.10% accuracy (Recall: 82.33% for on time, 96% for 1-30 days late, 100% for 31-60 days late, 99.25% for 61-90 days late, and 100% for 90+ days late)
Variables in the dataset:
- Pay class (Label): five categories (on time, 1-30 days, 31-60 days, 61-90 days, 90+ days) - Polynomial
- customer: customer ID - Polynomial
- month: month in which an invoice was issued - Integer
- base amount: the amount of invoice in dollars - Integer
- dispute: Whether or not an invoice was disputed (Yes or no) - Polynomial
Question
Part 1: Business Understanding (Problem):
In a few sentences, briefly describe what you aim to predict and how this model helps management.
Part 2: Data Understanding
Describe the sample, including the sample size and variables in the dataset, and attach the appropriate tables for both scale and nominal variables (similar to what you did for the previous assignments).
Strategic Management Text and Cases
ISBN: 978-1259196553
7th edition
Authors: Gregory Dess, Tom Lumpkin, Alan Eisner, Gerry McNamara