公开(公告)号：US20110047058A1

公开(公告)日：2011-02-24

申请号：US12731912

申请日：2010-03-25

申请人： William C. Erbey ,  Ashish Pandey ,  Amanjeet Saluja ,  Ankush Gupta ,  Deepak Dhayanithy ,  Raman Guglani ,  Saket Giri ,  Sulabh Dhall

发明人： William C. Erbey ,  Ashish Pandey ,  Amanjeet Saluja ,  Ankush Gupta ,  Deepak Dhayanithy ,  Raman Guglani ,  Saket Giri ,  Sulabh Dhall

IPC分类号： G06Q40/00

CPC分类号： G06Q40/02 ,  G06Q40/00

摘要： A method, system, and computer program product for generating a model for predicting loan behavior, including receiving loan data for a plurality of loans; preparing the loan data for analysis; grouping the loans into a plurality of hierarchical segments based on shared characteristics; generating a logistic regression model for each segment; and generating an overall prediction model for at least one of prepayment, delinquency, and default across the plurality of segments. Grouping the loans into a plurality of segments based on shared characteristics may include grouping the loans based on loan type, change in Housing Price Index (HPI) since origination, and loan age. Generating a logistic regression model for each segment may include generating a regression model for the probabilities of each of prepayment, default, and delinquency for each of the segments.

摘要翻译： 一种用于生成用于预测贷款行为的模型的方法，系统和计算机程序产品，包括接收多个贷款的贷款数据; 准备贷款数据进行分析; 基于共享特征将贷款分组成多个分级段; 为每个细分生成逻辑回归模型; 以及为所述多个段中的预付费，违约和违约中的至少一个生成整体预测模型。 基于共享特征将贷款分为多个部分可以包括基于贷款类型，起始以来的房价指数（HPI）变化和贷款年龄对贷款进行分组。 为每个细分生成逻辑回归模型可能包括为每个分段的预付款，违约和违法犯罪的概率产生回归模型。

公开(公告)号：US08473391B2

公开(公告)日：2013-06-25

申请号：US12731888

申请日：2010-03-25

申请人： William C. Erbey ,  Ronald M. Faris ,  Ashish Pandey ,  Amanjeet Saluja ,  Deepak Dhayanithy ,  Saurav Chawla ,  Seth Carter

发明人： William C. Erbey ,  Ronald M. Faris ,  Ashish Pandey ,  Amanjeet Saluja ,  Deepak Dhayanithy ,  Saurav Chawla ,  Seth Carter

IPC分类号： G06Q40/00 ,  G06Q40/02

CPC分类号： G06Q40/025 ,  G06Q10/04 ,  G06Q10/06398 ,  G06Q20/102 ,  G06Q40/00 ,  G06Q40/02 ,  G08B25/08

摘要： Methods and systems are provided for an integrated approach to collections cycle optimization including optimizing personnel, communications, and collection resolutions, resulting in reducing the variability of the collections cycle, reducing instances of foreclosure in collections, while increasing borrower satisfaction with the collections process. Candidate loan officers are selected based on behavioral attributes, personnel history, and human resources information, among other factors. Scripts that improve the effectiveness of communication between loan collection staff and borrowers are generated. An optimized collection program to best suit the needs of a particular borrower is determined.

摘要翻译： 提供了方法和系统，用于收集循环优化的综合方法，包括优化人员，通信和收集决议，从而减少收集周期的变异性，减少收藏中止赎权的实例，同时增加借款人对收集过程的满意度。 根据行为属性，人员历史，人力资源信息等选择候选贷款人员。 产生了提高贷款人员和借款人之间沟通有效性的脚本。 确定最适合特定借款人需求的优化收集程序。

公开(公告)号：US20110060671A1

公开(公告)日：2011-03-10

申请号：US12731888

申请日：2010-03-25

申请人： William C. ERBEY ,  Ronald M. Faris ,  Ashish Pandey ,  Amanjeet Saluja ,  Deepak Dhayanithy ,  Saurav Chawla ,  Seth Carter

发明人： William C. ERBEY ,  Ronald M. Faris ,  Ashish Pandey ,  Amanjeet Saluja ,  Deepak Dhayanithy ,  Saurav Chawla ,  Seth Carter

IPC分类号： G06Q40/00 ,  G06Q10/00

CPC分类号： G06Q40/025 ,  G06Q10/04 ,  G06Q10/06398 ,  G06Q20/102 ,  G06Q40/00 ,  G06Q40/02 ,  G08B25/08

摘要： Methods and systems are provided for an integrated approach to collections cycle optimization including optimizing personnel, communications, and collection resolutions, resulting in reducing the variability of the collections cycle, reducing instances of foreclosure in collections, while increasing borrower satisfaction with the collections process. Candidate loan officers are selected based on behavioral attributes, personnel history, and human resources information, among other factors. Scripts that improve the effectiveness of communication between loan collection staff and borrowers are generated. An optimized collection program to best suit the needs of a particular borrower is determined.

摘要翻译： 提供了方法和系统，用于收集循环优化的综合方法，包括优化人员，通信和收集决议，从而减少收集周期的变异性，减少收藏中止赎权的实例，同时增加借款人对收集过程的满意度。 根据行为属性，人员历史，人力资源信息等选择候选贷款人员。 产生了提高贷款人员和借款人之间沟通有效性的脚本。 确定最适合特定借款人需求的优化收集程序。