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控制不规则的银行卡冲销:可疑行为的影响以及如何打击它.pdf

上传人: 张** 编号:168837 2024-07-06 30页 654.46KB

1、Reining in irregular bankcard charge-offs:The impact of suspect behavior and how to combat it1Josh TurnbullVP,Card and Payments Business 2024 TransUnion LLC All Rights Reserved2024 US Financial Services SummitKey findings and agenda2Charge-off rates are at elevated levels;however,this is somewhat te

2、mpered when examined relative to account volumesWhile most consumers charge off with non-prime credit scores,this isincreasinglyan issue impacting all issuersEarly default losses topped half a billion dollars in Q4 2023,representing at least 4%of industry dollars lost to charge-off2Source:TransUnion

3、 credit database2024 US Financial Services Summit 2024 TransUnion LLC All Rights ReservedInvestigating charge-offs:An issuer perspective3 2024 TransUnion LLC All Rights Reserved 2024 TransUnion LLC All Rights ReservedRising bankcard charge-offs,reaching their highest level in a decade,are eroding le

4、nder profitabilityNew Charge-off Trend over Time$0.0 B$2.0 B$4.0 B$6.0 B$8.0 B$10.0 B$12.0 B$14.0 B0.0 M0.5 M1.0 M1.5 M2.0 M2.5 M3.0 M3.5 M4.0 M4.5 M5.0 M2017-Q42018-Q42019-Q42020-Q42021-Q42022-Q42023-Q4New Charge-off BalanceNew Charge-off VolumeNew Charge-off VolumeNew Charge-off Balance4Source:Tra

5、nsUnion US consumer credit database 2024 TransUnion LLC All Rights ReservedRelative charge-off volume is currently at pre-pandemic levels for non-prime risk tiers with an increasing trendNew Charge-off Volume as a%of All Active Cards 6 Months Prior for Non-prime Risk Tiers5VantageScore 4.0 risk rang

6、esSubprime=300600,Near prime=601660,Prime=661720,Prime plus=721780,Super prime=781+Source:TransUnion US consumer credit database0.0%0.2%0.4%0.6%0.8%1.0%0%1%2%3%4%5%6%7%8%9%2017-Q42018-Q42019-Q42020-Q42021-Q42022-Q42023-Q4%Near prime%SubprimeSubprimeNear Prime 2024 TransUnion LLC All Rights ReservedR

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该报告主要研究了银行信用卡违约行为,特别是早期违约(Early Defaults)、不活跃账户(Inactive Cards)和低利用率账户(Low Utilizers)的违约行为。研究发现,这些非传统的违约模式导致了信用卡违约数量的显著增加。与2019年相比,优质信用评分消费者的早期违约行为增加了48%,不活跃账户和低利用率账户的违约行为也有所增加。这些变化可能由典型的合同违约行为以外的异常行为驱动。研究指出,通过特定的评分和属性,可以有效地识别新兴账户中的早期违约风险,以及现有账户中的不活跃和低利用率风险。例如,早期违约评分(EPD)和快速违约模型评分(RDM)与TruVision信用属性结合使用,可以显著区分违约行为。在信用卡账户管理方面,这些模型有助于识别从休眠状态恢复到活跃状态的消费者,以及那些在12个月内可能违约的消费者。总体而言,这些模型为信用卡发卡机构在账户 origination 和 management 方面提供了重要的风险管理策略。
"早期违约风险上升,如何应对?" "模型预测异常违约,真的准确吗?" "信用卡坏账损失创新高, issuers 怎么办?"
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