零售信贷

Raymond A. Anderson
{"title":"零售信贷","authors":"Raymond A. Anderson","doi":"10.1093/oso/9780192844194.003.0003","DOIUrl":null,"url":null,"abstract":"This chapter covers retail credit, which has different data and modelling needs than wholesale. (1) Scorecard terminology—presented is a points-based model (other forms are acknowledged). The goal is to identify rare events, e.g. loan defaults, liquidations, bankruptcies or other undesirable outcomes. (2) Retail models—types across the credit cycle {solicitation, origination, collection, recovery, fraud}, what is being measured {risk, response, retention, revenue}, whose data is used {bespoke, generic, pooled, borrowed} and how it is done {empirical, hybrid, expert judgment}. (3) Data sources—focus is on credit bureaux and credit registries, their spread across various countries, ownership types of credit bureaux and some behind their establishment and spread. (4) Risk indicators—presentation of scores to end-users or downstream processes, as distinct from risk grades. (5) FICO scores—provided by major credit bureaux, with details of different versions and types, plus an imperfect formula for converting their scores into probabilities.","PeriodicalId":286194,"journal":{"name":"Credit Intelligence & Modelling","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Retail Credit\",\"authors\":\"Raymond A. Anderson\",\"doi\":\"10.1093/oso/9780192844194.003.0003\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This chapter covers retail credit, which has different data and modelling needs than wholesale. (1) Scorecard terminology—presented is a points-based model (other forms are acknowledged). The goal is to identify rare events, e.g. loan defaults, liquidations, bankruptcies or other undesirable outcomes. (2) Retail models—types across the credit cycle {solicitation, origination, collection, recovery, fraud}, what is being measured {risk, response, retention, revenue}, whose data is used {bespoke, generic, pooled, borrowed} and how it is done {empirical, hybrid, expert judgment}. (3) Data sources—focus is on credit bureaux and credit registries, their spread across various countries, ownership types of credit bureaux and some behind their establishment and spread. (4) Risk indicators—presentation of scores to end-users or downstream processes, as distinct from risk grades. (5) FICO scores—provided by major credit bureaux, with details of different versions and types, plus an imperfect formula for converting their scores into probabilities.\",\"PeriodicalId\":286194,\"journal\":{\"name\":\"Credit Intelligence & Modelling\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Credit Intelligence & Modelling\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/oso/9780192844194.003.0003\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Credit Intelligence & Modelling","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/oso/9780192844194.003.0003","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

本章涵盖零售信贷,它与批发信贷有不同的数据和建模需求。(1)记分卡术语-呈现的是一个基于点数的模型(其他形式也被承认)。目标是识别罕见事件,例如贷款违约、清算、破产或其他不良后果。(2)零售模型——整个信贷周期的类型{招揽、发起、催收、回收、欺诈},被衡量的内容{风险、响应、保留、收入},谁的数据被使用{定制、通用、汇集、借用}以及如何使用{经验、混合、专家判断}。(3)数据来源——重点是征信机构和征信登记机构,它们在各国的分布情况,征信机构的所有权类型以及它们建立和分布背后的一些因素。(4)风险指标-向最终用户或下游过程提供分数,与风险等级不同。(5) FICO评分——由主要的信用局提供,有不同版本和类型的详细信息,加上一个不完善的公式来将他们的分数转换成概率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Retail Credit
This chapter covers retail credit, which has different data and modelling needs than wholesale. (1) Scorecard terminology—presented is a points-based model (other forms are acknowledged). The goal is to identify rare events, e.g. loan defaults, liquidations, bankruptcies or other undesirable outcomes. (2) Retail models—types across the credit cycle {solicitation, origination, collection, recovery, fraud}, what is being measured {risk, response, retention, revenue}, whose data is used {bespoke, generic, pooled, borrowed} and how it is done {empirical, hybrid, expert judgment}. (3) Data sources—focus is on credit bureaux and credit registries, their spread across various countries, ownership types of credit bureaux and some behind their establishment and spread. (4) Risk indicators—presentation of scores to end-users or downstream processes, as distinct from risk grades. (5) FICO scores—provided by major credit bureaux, with details of different versions and types, plus an imperfect formula for converting their scores into probabilities.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信