Federal Reserve Bank of Boston Research Department Working Papers最新文献_第5页

Larceny in the Product Market: A Hidden Tax? 产品市场中的盗窃:一种隐藏的税?

Federal Reserve Bank of Boston Research Department Working Papers Pub Date : 2020-10-01 DOI: 10.29412/res.wp.2020.14

Osborne Jackson, Thu Tran

引用次数: 0

No Longer Qualified? Changes in the Supply and Demand for Skills within Occupations 不再合格?职业技能供求的变化

Federal Reserve Bank of Boston Research Department Working Papers Pub Date : 2020-05-28 DOI: 10.29412/res.wp.2020.03

Mary A. Burke, A. Modestino, S. Sadighi, Rachel B. Sederberg, Bledi Taska

引用次数: 11

Wealth Distribution and Retirement Preparation among Early Savers 早期储蓄者的财富分配与退休准备

Federal Reserve Bank of Boston Research Department Working Papers Pub Date : 2020-02-01 DOI: 10.17016/feds.2020.043

Lindsay Jacobs, Elizabeth Llanes, Kevin B. Moore, Jeffrey Thompson, A. Volz

引用次数: 8

Consumption, Credit, and the Missing Young 消费、信贷和失踪的年轻人

Federal Reserve Bank of Boston Research Department Working Papers Pub Date : 2019-11-22 DOI: 10.29412/res.wp.2019.10

Daniel H. Cooper, O. Gorbachev, María J. Luengo‐Prado

引用次数: 4

How Magic a Bullet Is Machine Learning for Credit Analysis? An Exploration with FinTech Lending Data 信用分析中的机器学习有多神奇?金融科技借贷数据的探索

Federal Reserve Bank of Boston Research Department Working Papers Pub Date : 2019-10-14 DOI: 10.29412/res.wp.2019.16

J. C. Wang, Charles B. Perkins

{"title":"How Magic a Bullet Is Machine Learning for Credit Analysis? An Exploration with FinTech Lending Data","authors":"J. C. Wang, Charles B. Perkins","doi":"10.29412/res.wp.2019.16","DOIUrl":"https://doi.org/10.29412/res.wp.2019.16","url":null,"abstract":"FinTech online lending to consumers has grown rapidly in the post-crisis era. As argued by its advocates, one key advantage of FinTech lending is that lenders can predict loan outcomes more accurately by employing complex analytical tools, such as machine learning (ML) methods. This study applies ML methods, in particular random forests and stochastic gradient boosting, to loan-level data from the largest FinTech lender of personal loans to assess the extent to which those methods can produce more accurate out-of-sample predictions of default on future loans relative to standard regression models. To explain loan outcomes, this analysis accounts for the economic conditions faced by a borrower after origination, which are typically absent from other ML studies of default. For the given data, the ML methods indeed improve prediction accuracy, but more so over the near horizon than beyond a year. This study then shows that having more data up to, but not beyond, a certain quantity enhances the predictive accuracy of the ML methods relative to that of parametric models. The likely explanation is that there has been data or model drift over time, so that methods that fit more complex models with more data can in fact suffer greater out-of-sample misses. Prediction accuracy rises, but only marginally, with additional standard credit variables beyond the core set, suggesting that unconventional data need to be sufficiently informative as a whole to help consumers with little or no credit history. This study further explores whether the greater functional flexibility of ML methods yields unequal benefit to consumers with different attributes or who reside in locales with varying economic conditions. It finds that the ML methods produce more favorable ratings for different groups of consumers, although those already deemed less risky seem to benefit more on balance.","PeriodicalId":219195,"journal":{"name":"Federal Reserve Bank of Boston Research Department Working Papers","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129175711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6