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Predictions of Loan Defaulter - A Data Science Perspective
2020 5th International Conference on Computing, Communication and Security (ICCCS), 2020With the progress of technology and implementation of Data Science in banking, changes the face of banking industry. Most of the banking, financial sectors and social lending platforms are actively investing on lending. But financial institutions might face huge capital loss if they approved the loan without having any prior assessment of default risk.
P. Maheswari, Ch. V. Narayana
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, 2011 African financial deepening is beset by a high rate of loan defaults, which encourages banks to hold liquid assets instead of lending. We put forward a novel theoretical model that captures the salient features of African credit markets which shows that equilibrium with high loan defaults and low lending can arise when contract enforcement institutions
Svetlana Andrianova +2 more
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Loan Default Analysis with Multiplex Graph Learning
Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2020Aiming to effectively distinguish loan default in the Mobile Credit Payment Service, industrial efforts mainly attempt to employ conventional classifier with complicated feature engineer for prediction. However, these solutions fail to exploit multiplex relations existed in the financial scenarios and ignore the key intrinsic properties of the loan ...
Binbin Hu +7 more
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Bank Loan Renegotiation and Credit Default Swaps
SSRN Electronic Journal, 2017Abstract Using Roberts (2015) loan-level data from 2000 to 2011, we find that the inception of CDS trading on reference firms’ debt is associated with a decreased number and lower probability of amendments, restatements, and rollovers to existing lenders of bank loans.
Brian Clark +3 more
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A multi-objective approach for the prediction of loan defaults
Expert Systems with Applications, 2007Credit institutions are seldom faced with problems dealing with single objectives. Often, decisions involving optimizing two or more competing goals simultaneously need to be made, and conventional optimization routines/models are incapable of handling the problems.
Oluwarotimi Odeh +4 more
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Predicting Student Loan Defaults
The Journal of Higher Education, 1997(1997). Predicting Student Loan Defaults. The Journal of Higher Education: Vol. 68, No. 3, pp. 322-354.
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Effects of Unobserved Defaults on Correlation between Probability of Default and Loss Given Default on Mortgage Loans [PDF]
SSRN Electronic Journal, 2009 This paper demonstrates how the observed correlation between probability of default and loss given default depends on the fact that defaults in which collateral provides 100% recovery are not observed. Creditors see only the defaults of mortgagors who suffer from a fall in collateral value to less than the remaining loan principal.
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SME Loan Defaults in Bangladesh [PDF]
, 2014 This policy brief discovers, for example, that SMEs are vital for growth and jobs in Bangladesh, accounting for 40 per cent of all employment. In comparison with large enterprises and microenterprises, SMEs have traditionally been underserved in terms of access to credit.
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Loan Default Predictive Analytics
2022 IEEE World Conference on Applied Intelligence and Computing (AIC), 2022Ebenezer Owusu +3 more
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