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}
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.