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Two-stage rule extraction method based on tree ensemble model for interpretable loan evaluation
Information Sciences, 2021Abstract The tree ensemble model has been widely employed as a loan evaluation method in credit risk assessment due to its high accuracy and robustness. However, the tree ensemble model is complex and incomprehensible, which restricts its adoption for decision-making in loan evaluation.
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Deep Learning for Information Extraction in Finance Documents — Corporate Loan Operations
2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2020In Corporate lending back office operations, the key challengeof the document processing stage is the volume and variety of Loan Application Process (LAP) documents. This paper describes a deeplearning system called Loan Operations Data Extraction System(LODES) that extracts the key data points known as loan elementsfrom the Loan Application Process ...
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2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017Fraudulent activities in financial institutes can break the economic system of the country. These activities can be identified using clustering and classification algorithms. Effectiveness of these algorithms depend on quality of the input data. Moreover, financial data comes from various sources and forms such as financial statements, stakeholders ...
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Monitoring and Loan Pricing: Do Microfinance Institutions Extract Rents from Entrepreneurs?
The Quarterly Journal of Finance, 2022Microfinance institutions (MFIs) have been criticized for charging high interest rates on loans. Building on multiple-principal agency theory, we argue that when an MFI acquires proprietary information about its clients through monitoring, it gains an information advantage over other lenders enabling it to extract rents by charging higher interest ...
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Predicting whether a borrower will default on a loan is of significant concern to platforms and investors in online peer-to-peer (P2P) lending. Because the data types online platforms use are complex and involve unstructured information such as text, which is difficult to quantify and analyze, loan default prediction faces new challenges in P2P.
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