Using Machine Learning to Predict Success or Failure in Chapter 13 Bankruptcy Cases

44 Pages Posted: 8 Jun 2018 Last revised: 24 Jun 2018

Warren Agin

Analytic Law, LLC; LexPredict, LLC; Boston College Law School

Date Written: May 22, 2018


Obtaining a chapter 13 bankruptcy discharge is notoriously difficult. Past empirical studies conclude that only one-third of chapter 13 debtors complete their obligations under their plans and obtain a chapter 13 discharge. Many cases end up dismissed, or converted to a case under chapter 7. New data recently made available by the Federal Judicial Center, shows that in recent years only about 39% of chapter 13 filers successfully obtain their chapter 13 discharges. These are low numbers.

This project goes beyond such descriptive statistics. Using machine learning algorithms – so-called artificial intelligence – it describes a model that can predict, using data from the Federal Judicial Center's Integrated Database, whether a debtor will obtain a chapter 13 discharge based only on information provided in the initial petition and summary of schedules. The model is able to predict case results with 70% accuracy overall – and for about 25% of cases can predict results with more than 90% accuracy. When case predictions are cross-referenced against actual case results, the model can assign to specific cases a highly accurate probability of success. The model uses a random forest decision tree algorithm to achieve its results, although nearly similar results were also obtained using a neural network.

Keywords: Chapter 13 plan, legal artificial intelligence, random forests, legal AI, legal prediction

JEL Classification: K35

Suggested Citation

Agin, Warren, Using Machine Learning to Predict Success or Failure in Chapter 13 Bankruptcy Cases (May 22, 2018). Available at SSRN: or

Warren Agin (Contact Author)

Analytic Law, LLC ( email )

50 Milk Street, 16th Floor
Boston, MA 02109
United States

LexPredict, LLC ( email )

United States

HOME PAGE: http://

Boston College Law School ( email )

885 Centre Street
Newton, MA 02459-1163
United States

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