公开(公告)号：US08099356B2

公开(公告)日：2012-01-17

申请号：US12027250

申请日：2008-02-06

申请人： Jeffrey Allen Feinstein ,  Gary J. Sullivan ,  Jennifer Elizabeth Jack

发明人： Jeffrey Allen Feinstein ,  Gary J. Sullivan ,  Jennifer Elizabeth Jack

IPC分类号： G06Q40/00

CPC分类号： G06Q40/00 ,  G06Q20/10 ,  G06Q20/108 ,  G06Q40/025 ,  G06Q40/06

摘要： Predicting impact of future actions on subsequent creditworthiness involves developing a prediction model that predicts a statistical interaction of performance expectation with likely post-scoring behavior. Including sensitivity to new, post-scoring date credit behaviors in the analytic solution greatly improves snapshot score predictions. The modeling approach involves multiple snapshots: predictive and performance snapshots, plus an intermediate snapshot shortly after the predictive snapshot to quantify interim consumer behavior post-scoring date. Predictive interaction variables are calculated on the predictive data using simulated consumer profiles before and after assuming a sizeable simulated balance to infer the consumer's tolerance for incremental future debt. Using an adjustor approach in predicting capacity allows isolation of the confounding effect of risk from the capacity determination. A resulting capacity index can be used to rank order originations and line increases according to capacity in consumer, bankcard, automobile and mortgage lending.

摘要翻译： 预测未来行动对后续信誉的影响涉及开发预测模型，预测绩效预期与可能的后评分行为的统计交互。 包括对分析解决方案中新的，后评分日期信用行为的敏感性大大提高了快照分数预测。 建模方法涉及多个快照：预测性能快照和性能快照，以及预测快照后不久的中间快照，用于量化中期消费者行为后评分日期。 预测性交互变量使用模拟消费者概况在假设相当大的模拟平衡之前和之后根据预测数据计算，以推断消费者对增值未来债务的容忍度。 使用调整方法来预测能力，可以将风险与容量确定的混杂影响隔离开来。 根据消费者，银行卡，汽车和抵押贷款的能力，可以使用最终的能力指数对订单发起和线路增长进行排序。

公开(公告)号：US08484132B1

公开(公告)日：2013-07-09

申请号：US13491786

申请日：2012-06-08

申请人： James Alan Christiansen ,  Fang Yuan ,  Michael James Woodberry ,  Brent Wayne Sorenson ,  Jeffrey Allen Feinstein

发明人： James Alan Christiansen ,  Fang Yuan ,  Michael James Woodberry ,  Brent Wayne Sorenson ,  Jeffrey Allen Feinstein

IPC分类号： G06Q40/00

CPC分类号： G06Q40/00

摘要： Certain embodiments of the invention may include systems, methods, and apparatus for determining fraud risk associated with a credit application. According to an exemplary embodiment of the invention, a method is provided for receiving applicant information associated with the application; searching one or more consumer identity repositories for prior usage of the applicant information; generating a plurality of identity characteristics corresponding to the prior usage of the applicant information; assigning the application to one of a plurality of segments based at least in part on the searching; scoring the application with a predictive scoring model to determine a risk score based at least in part on the identity characteristics; determining identity fraud risk types associated with the application; and outputting the risk score and one or more indicators of the determined identity fraud risk types.

摘要翻译： 本发明的某些实施例可以包括用于确定与信用申请相关联的欺诈风险的系统，方法和装置。 根据本发明的示例性实施例，提供了一种用于接收与应用相关联的申请人信息的方法; 搜索一个或多个消费者身份存储库以便先前使用申请人信息; 产生与申请人信息的先前使用相对应的多个身份特征; 至少部分地基于所述搜索将所述应用分配给多个片段中的一个片段; 用预测评分模型对应用程序进行评分，以至少部分地基于身份特征来确定风险评分; 确定与申请相关的身份欺诈风险类型; 并输出所确定的身份欺诈风险类型的风险分数和一个或多个指标。

公开(公告)号：US20090037323A1

公开(公告)日：2009-02-05

申请号：US12027250

申请日：2008-02-06

申请人： Jeffrey Allen FEINSTEIN ,  Gary J. Sullivan ,  Jennifer Elizabeth Jack

发明人： Jeffrey Allen FEINSTEIN ,  Gary J. Sullivan ,  Jennifer Elizabeth Jack

IPC分类号： G06Q40/00

CPC分类号： G06Q40/00 ,  G06Q20/10 ,  G06Q20/108 ,  G06Q40/025 ,  G06Q40/06

摘要： Predicting impact of future actions on subsequent creditworthiness involves developing a prediction model that predicts a statistical interaction of performance expectation with likely post-scoring behavior. Including sensitivity to new, post-scoring date credit behaviors in the analytic solution greatly improves snapshot score predictions. The modeling approach involves multiple snapshots: predictive and performance snapshots, plus an intermediate snapshot shortly after the predictive snapshot to quantify interim consumer behavior post-scoring date. Predictive interaction variables are calculated on the predictive data using simulated consumer profiles before and after assuming a sizeable simulated balance to infer the consumer's tolerance for incremental future debt. Using an adjustor approach in predicting capacity allows isolation of the confounding effect of risk from the capacity determination. A resulting capacity index can be used to rank order originations and line increases according to capacity in consumer, bankcard, automobile and mortgage lending.

摘要翻译： 预测未来行动对后续信誉的影响涉及开发预测模型，预测绩效预期与可能的后评分行为的统计交互。 包括对分析解决方案中新的，后评分日期信用行为的敏感性大大提高了快照分数预测。 建模方法涉及多个快照：预测性能快照和性能快照，以及预测快照后不久的中间快照，用于量化中期消费者行为后评分日期。 预测性交互变量使用模拟消费者概况在假设相当大的模拟平衡之前和之后根据预测数据计算，以推断消费者对增值未来债务的容忍度。 使用调整方法来预测能力，可以将风险与容量确定的混杂影响隔离开来。 根据消费者，银行卡，汽车和抵押贷款的能力，可以使用最终的能力指数对订单发起和线路增长进行排序。