2026-05-18T10:51:59Zhttps://keep.lib.asu.edu/oai/request

oai:keep.lib.asu.edu:node-2010092025-05-14T18:38:10Zoai_pmh:alloai_pmh:repo_items

201009 https://hdl.handle.net/2286/R.2.N.201009 http://rightsstatements.org/vocab/InC/1.0/ http://creativecommons.org/licenses/by-nc-sa/4.0 2025-05 58 pages Cencimino, Michael Rigoni, Adam Kerner, Hannah Barrett, The Honors College School of Earth and Space Exploration School of Politics and Global Studies Walter Cronkite School of Journalism and Mass Comm This thesis explores the application of artificial intelligence (AI) and machine learning (ML) to predict the outcomes of certain consumer bankruptcy cases, specifically whether or not the debtor will successfully obtain a discharge at the end of the case. The thesis will cover the intersection of AI and consumer bankruptcy law, with a specific focus on Chapter 13 filings since Chapter 13 filings have a notably lower success rate in achieving discharge than Chapter 7 filings. This research aims to address this disparity by analyzing how AI and machine learning algorithms can be used as a tool to both improve the efficiency of debtor counsel and maximize creditor returns, ultimately resulting in an increased likelihood of discharge. By focusing on Chapter 13 filings, the research highlights the potential for AI and ML tools to both identify patterns and analyze risk factors that influence case outcomes. In doing so, this work aims to demonstrate how AI-driven insights can enhance debtor outcomes, promote fairer outcomes, and improve the overall efficiency of the bankruptcy system. Bankruptcy Law Machine learning Technology Predictive Analytics in Consumer Bankruptcy: Enhancing Chapter 13 Outcomes through Artificial Intelligence and Machine Learning