Enhancing Delinquent Debt Collection Using Statistical Models of Debt Historical Information and Account Events
    21.
    发明申请
    Enhancing Delinquent Debt Collection Using Statistical Models of Debt Historical Information and Account Events 有权
    使用债务历史信息和账户事件的统计模型加强拖欠债务收集

    公开(公告)号:US20070156557A1

    公开(公告)日:2007-07-05

    申请号:US11683976

    申请日:2007-03-08

    IPC分类号: G06Q40/00

    摘要: 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.

    摘要翻译: 预测模型,例如神经网络,评估个人债务持有者帐户,并根据已知变量之间的学习关系预测每个账户上将收集的金额。 预测模型是使用违规债务账户的历史数据,用于收取账户中的债务的收集方法,以及收集方法的成功生成的。 在一个实施例中,使用概述帐户中的事件模式的拖欠债务账户的简档来生成预测模型,以及在每个账户中收集工作的成功。 在另一个实施例中,预测模型包括在每个帐户的收集期间创建的收集者的笔记的数学表示。