金融机构贷款预测软件

Narayana Darapaneni, Akshay Kumar, Archanna Dixet, M. Suriyanarayanan, Shabd Srivastava, A. Paduri
{"title":"金融机构贷款预测软件","authors":"Narayana Darapaneni, Akshay Kumar, Archanna Dixet, M. Suriyanarayanan, Shabd Srivastava, A. Paduri","doi":"10.1109/irtm54583.2022.9791797","DOIUrl":null,"url":null,"abstract":"Financial institutions are focused on expanding their revenue streams, by selling various financial solutions, to their customers, a big chunk of this revenue comes from the credit line of business. The profitability of a financial institution is dependent on how well the credit business is yielding revenue, hence there is a huge focus on optimizing this process and an ardent desire to reduce the risk of loan defaulters. Adoption of AI/ML technologies are transforming credit process by significantly reducing the risk by predicting loan defaults. Data Science has paved the way for enabling predictive analytics. Several data science techniques such as Logistic regression, SVM, Neural Networks, Random Forest are discussed in this paper on how they enable increasing the accuracy of predicting loan defaulters. This paper deals with how a credit score is predicted to help financial institutions set the terms of loan disbursements to their customers. The focus of this paper is to present a loan prediction solution - Seven Seas to financial institutions. Several aspects of loan origination have been dealt with in this paper. A high-level process of loan application and an alternative credit scoring model using Machine Learning has been described. This paper also entails the overall market scope for such a solution and identifies several financial institutions that can embark on their transformation initiatives with such a disruptive technology. The extent of the existing market and its scope to embrace this technology is phenomenal not just in India but also globally.","PeriodicalId":426354,"journal":{"name":"2022 Interdisciplinary Research in Technology and Management (IRTM)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Loan Prediction Software for Financial Institutions\",\"authors\":\"Narayana Darapaneni, Akshay Kumar, Archanna Dixet, M. Suriyanarayanan, Shabd Srivastava, A. Paduri\",\"doi\":\"10.1109/irtm54583.2022.9791797\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Financial institutions are focused on expanding their revenue streams, by selling various financial solutions, to their customers, a big chunk of this revenue comes from the credit line of business. The profitability of a financial institution is dependent on how well the credit business is yielding revenue, hence there is a huge focus on optimizing this process and an ardent desire to reduce the risk of loan defaulters. Adoption of AI/ML technologies are transforming credit process by significantly reducing the risk by predicting loan defaults. Data Science has paved the way for enabling predictive analytics. Several data science techniques such as Logistic regression, SVM, Neural Networks, Random Forest are discussed in this paper on how they enable increasing the accuracy of predicting loan defaulters. This paper deals with how a credit score is predicted to help financial institutions set the terms of loan disbursements to their customers. The focus of this paper is to present a loan prediction solution - Seven Seas to financial institutions. Several aspects of loan origination have been dealt with in this paper. A high-level process of loan application and an alternative credit scoring model using Machine Learning has been described. This paper also entails the overall market scope for such a solution and identifies several financial institutions that can embark on their transformation initiatives with such a disruptive technology. The extent of the existing market and its scope to embrace this technology is phenomenal not just in India but also globally.\",\"PeriodicalId\":426354,\"journal\":{\"name\":\"2022 Interdisciplinary Research in Technology and Management (IRTM)\",\"volume\":\"67 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-02-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 Interdisciplinary Research in Technology and Management (IRTM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/irtm54583.2022.9791797\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 Interdisciplinary Research in Technology and Management (IRTM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/irtm54583.2022.9791797","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

金融机构致力于通过向客户销售各种金融解决方案来扩大其收入来源,其中很大一部分收入来自信贷业务。金融机构的盈利能力取决于信贷业务产生收入的程度,因此,人们非常关注优化这一过程,并热切希望降低贷款违约的风险。人工智能/机器学习技术的采用通过预测贷款违约来显著降低风险,从而改变了信贷流程。数据科学为实现预测分析铺平了道路。本文讨论了几种数据科学技术,如逻辑回归,支持向量机,神经网络,随机森林,如何提高预测贷款违约者的准确性。本文讨论了如何预测信用评分来帮助金融机构为其客户设定贷款支付条款。本文的重点是向金融机构介绍一个贷款预测解决方案- Seven Seas。本文讨论了贷款发放的几个方面。描述了一个高层次的贷款申请过程和使用机器学习的替代信用评分模型。本文还涉及到这种解决方案的整体市场范围,并确定了几家可以利用这种颠覆性技术开展转型计划的金融机构。不仅在印度,而且在全球范围内,现有市场的规模和接受这项技术的范围都是惊人的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Loan Prediction Software for Financial Institutions
Financial institutions are focused on expanding their revenue streams, by selling various financial solutions, to their customers, a big chunk of this revenue comes from the credit line of business. The profitability of a financial institution is dependent on how well the credit business is yielding revenue, hence there is a huge focus on optimizing this process and an ardent desire to reduce the risk of loan defaulters. Adoption of AI/ML technologies are transforming credit process by significantly reducing the risk by predicting loan defaults. Data Science has paved the way for enabling predictive analytics. Several data science techniques such as Logistic regression, SVM, Neural Networks, Random Forest are discussed in this paper on how they enable increasing the accuracy of predicting loan defaulters. This paper deals with how a credit score is predicted to help financial institutions set the terms of loan disbursements to their customers. The focus of this paper is to present a loan prediction solution - Seven Seas to financial institutions. Several aspects of loan origination have been dealt with in this paper. A high-level process of loan application and an alternative credit scoring model using Machine Learning has been described. This paper also entails the overall market scope for such a solution and identifies several financial institutions that can embark on their transformation initiatives with such a disruptive technology. The extent of the existing market and its scope to embrace this technology is phenomenal not just in India but also globally.
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