{"title":"利用数字工具监督美国抵押贷款市场","authors":"Li Chang, Richard Koss","doi":"10.2139/ssrn.3362477","DOIUrl":null,"url":null,"abstract":"The US mortgage market is of paramount economic and financial importance. While the causes of the Global Financial Crisis (GFC) remain a subject of vigorous debate, lax lending standards and opacity surrounding innovations in securitization are often cited as central issues. A decade following the Global Financial Crisis, we have demonstrated that digital tools have been developed in the mortgage space that have the potential to allow investors to form a clear view of the investment risks and opportunities, and policymakers to design regulations with a complete view of the behavior of all participants: borrowers, underwriters, servicers and investors. While big data tools have been around for an extended period, it is only recently that advanced techniques have come to the market that allow for more cost-effective analysis. The latest enhancement is the application of AI to this data to unify the information across disparate data sets. We have seen demonstrations of the power of these techniques in analyzing business models for financial institutions, and for informing policymakers about the implications of their decisions across broad categories of actors in this market. Looking ahead, the analysis performed here can be extended by matching loans across time as well as between different data sets, and through applications to different markets and countries.","PeriodicalId":406666,"journal":{"name":"Applied Computing eJournal","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Utilizing Digital Tools for the Surveillance of the US Mortgage Market\",\"authors\":\"Li Chang, Richard Koss\",\"doi\":\"10.2139/ssrn.3362477\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The US mortgage market is of paramount economic and financial importance. While the causes of the Global Financial Crisis (GFC) remain a subject of vigorous debate, lax lending standards and opacity surrounding innovations in securitization are often cited as central issues. A decade following the Global Financial Crisis, we have demonstrated that digital tools have been developed in the mortgage space that have the potential to allow investors to form a clear view of the investment risks and opportunities, and policymakers to design regulations with a complete view of the behavior of all participants: borrowers, underwriters, servicers and investors. While big data tools have been around for an extended period, it is only recently that advanced techniques have come to the market that allow for more cost-effective analysis. The latest enhancement is the application of AI to this data to unify the information across disparate data sets. We have seen demonstrations of the power of these techniques in analyzing business models for financial institutions, and for informing policymakers about the implications of their decisions across broad categories of actors in this market. Looking ahead, the analysis performed here can be extended by matching loans across time as well as between different data sets, and through applications to different markets and countries.\",\"PeriodicalId\":406666,\"journal\":{\"name\":\"Applied Computing eJournal\",\"volume\":\"45 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-03-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Computing eJournal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.3362477\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Computing eJournal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3362477","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Utilizing Digital Tools for the Surveillance of the US Mortgage Market
The US mortgage market is of paramount economic and financial importance. While the causes of the Global Financial Crisis (GFC) remain a subject of vigorous debate, lax lending standards and opacity surrounding innovations in securitization are often cited as central issues. A decade following the Global Financial Crisis, we have demonstrated that digital tools have been developed in the mortgage space that have the potential to allow investors to form a clear view of the investment risks and opportunities, and policymakers to design regulations with a complete view of the behavior of all participants: borrowers, underwriters, servicers and investors. While big data tools have been around for an extended period, it is only recently that advanced techniques have come to the market that allow for more cost-effective analysis. The latest enhancement is the application of AI to this data to unify the information across disparate data sets. We have seen demonstrations of the power of these techniques in analyzing business models for financial institutions, and for informing policymakers about the implications of their decisions across broad categories of actors in this market. Looking ahead, the analysis performed here can be extended by matching loans across time as well as between different data sets, and through applications to different markets and countries.