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dc.contributor.authorKamath, Harish-
dc.contributor.authorJahan, Noor Firdoos-
dc.date.accessioned2021-04-08T10:11:49Z-
dc.date.available2021-04-08T10:11:49Z-
dc.date.issued2020-
dc.identifier.citationKamath, H., & Jahan, N. F. (2020). Using hidden Markov model to monitor possible loan defaults in banks. International Journal of Economics and Business Administration, 8(4), 1097-1107.en_GB
dc.identifier.urihttps://www.um.edu.mt/library/oar/handle/123456789/73305-
dc.description.abstractPurpose: Banking business faces a major challenge with defaults. This may not be practical, as there is no control over the borrower’s financial situation or their intents to repay. However, if the banks get to know the possible defaults ahead of some actionable time frame, with a certain degree of accuracy in such prediction, Banks may apply any a possible risk mitigation strategy to remediate possible defaults. Willingness to repay the debt and the capability to repay the debt are two primary reasons for the loan default. The subject of this paper is to closely monitor the Facebook activities and check if we can predict if the borrower may become a defaulter any soon, by applying the sentiment analysis on Facebook data and use Hidden Markov model to compute the probabilities of the possible default. Approach/Methodology/Design: The loan dataset was used for the borrower details and the Facebook data for all those borrowers were gathered. The data from Facebook posts, likes and shares on a borrower were subjected to sentiment analysis, considering income-related information of spend related information on neutral. Hidden Markov model was applied to the polarized data based on the sequence of the sentiment analysis. Findings: Hidden Markov Model gives the transition probability of state, default or regular, for the observed polarized sentiments from Facebook data for borrowers. Practical Implications: This mechanism can be integrated into the bank's credit risk management system and could help predict the possibility of a borrower becoming a defaulter. This is very much useful where the tenure of the loan is longer. This research paper fills the gap of active monitoring of the credit risk for long term loans, where the financial status of the borrower could change but the lender doesn’t get to know until the borrower stops the repayment.en_GB
dc.language.isoenen_GB
dc.publisherEleftherios Thalassinosen_GB
dc.rightsinfo:eu-repo/semantics/openAccessen_GB
dc.subjectHidden Markov modelsen_GB
dc.subjectSoftware frameworksen_GB
dc.subjectBanks and banking -- Accountingen_GB
dc.subjectLoansen_GB
dc.titleUsing hidden Markov model to monitor possible loan defaults in banksen_GB
dc.typearticleen_GB
dc.rights.holderThe copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.en_GB
dc.description.reviewedpeer-revieweden_GB
dc.identifier.doi10.35808/ijeba/653-
dc.publication.titleInternational Journal of Economics and Business Administrationen_GB
Appears in Collections:IJEBA, Volume 8, Issue 4

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