Enhancing delinquent debt collection using statistical models of debt historical information and account events

US7536348B2 · kind B2 · utility

Assignee

• Fair Isaac Corporation · US

Inventors

• Min Shao · Nanjing, CN
• Scott Michael Zoldi · San Diego, US
• Gordon Cameron · Cupertino, US
• Ron Martin · Ann Arbor, US
• Radu Drossu · San Diego, US
• Jenny (Guofeng) Zhang · San Diego, US
• Daniel Shoham · San Diego, US

Key dates

• Filing date: Mar 8, 2007
• Grant date: May 19, 2009
• Expiry date: Mar 8, 2027

Classification

• Technology area (CPC G): Physics
• CPC primary: G06Q40/08
• WIPO field: IT methods for management
• WIPO sector: Electrical engineering

Abstract

A predictive model, for example, a neural network, evaluates individual debt holder accounts and predicts the amount that will be collected on each account based on learned relationships among known variables. The predictive model is generated using historical data of delinquent debt accounts, the collection methods used to collect the debts in the accounts, and the success of the collection methods. In one embodiment, the predictive model is generated using profiles of delinquent debt accounts summarizing patterns of events in the accounts, and the success of the collection effort in each account. In another embodiment, the predictive model includes a mathematical representation of the collector's notes created during the collection period for each account.