The Experts below are selected from a list of 1917 Experts worldwide ranked by ideXlab platform
Prasanna L Tantri - One of the best experts on this subject based on the ideXlab platform.
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costs of job rotation evidence from mandatory Loan Officer rotation
Management Science, 2020Co-Authors: Subhendu Bhowal, Krishnamurthy Subramanian, Prasanna L TantriAbstract:Job rotation inside an organization creates two conflicting effects. It disciplines agents by creating the fear that their successors may discover and report their hidden information. Thus, the age...
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soft information and the cost of job rotation evidence from Loan Officer rotation
2013Co-Authors: Subhendu Bhowal, Krishnamurthy Subramanian, Prasanna L TantriAbstract:Job rotation, where a principal routinely rotates agents among tasks, is argued to be a powerful antidote for agency problems inside an organization. However, when soft information dominates transactions inside a firm, verifying the information set that led to a particular decision becomes difficult. This difficulty imposes costs on job rotation since an incoming agent cannot verify the information set that the outgoing agent utilized when arriving at a key decision. This lack of verifiability distorts incentives for effort when a decision straddles two agents since neither agent receives the entire marginal cost/benefit of her effort. In this study, we highlight this cost of rotation policies. We use unique data on over 50,000 Loans sanctioned by 51 Loan Officers of a large public sector bank in India, which follows a fixed-tenure-based policy of Loan Officer rotation. We find default probabilities to be about 8% higher for Loans that are likely to straddle an incoming and an outgoing Loan Officer when compared to other Loans. This difference does not stem from any differences in hard information captured in borrowers' credit histories or from Loan Officer rotation destroying lending relationships. Finally, we find evidence of credit rationing as well as the new Loan Officer rejects 7% more Loans for borrowers who have borrowed during the straddling period.
Cristian Bravo - One of the best experts on this subject based on the ideXlab platform.
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the value of text for small business default prediction a deep learning approach
European Journal of Operational Research, 2021Co-Authors: Matthew Stevenson, Christophe Mues, Cristian BravoAbstract:Abstract Compared to consumer lending, Micro, Small and Medium Enterprise (mSME) credit risk modelling is particularly challenging, as, often, the same sources of information are not available. Therefore, it is standard policy for a Loan Officer to provide a textual Loan assessment to mitigate limited data availability. In turn, this statement is analysed by a credit expert alongside any available standard credit data. In our paper, we exploit recent advances from the field of Deep Learning and Natural Language Processing (NLP), including the BERT (Bidirectional Encoder Representations from Transformers) model, to extract information from 60000 textual assessments provided by a lender. We consider the performance in terms of the AUC (Area Under the receiver operating characteristic Curve) and Brier Score metrics and find that the text alone is surprisingly effective for predicting default. However, when combined with traditional data, it yields no additional predictive capability, with performance dependent on the text’s length. Our proposed deep learning model does, however, appear to be robust to the quality of the text and therefore suitable for partly automating the mSME lending process. We also demonstrate how the content of Loan assessments influences performance, leading us to a series of recommendations on a new strategy for collecting future mSME Loan assessments.
Gregory F. Udell - One of the best experts on this subject based on the ideXlab platform.
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small business credit availability and relationship lending the importance of bank organisational structure
The Economic Journal, 2002Co-Authors: Allen N Berger, Gregory F. UdellAbstract:This paper models the inner workings of relationship lending, the implications for bank organisational structure, and the effects of shocks to the economic environment on the availability of relationship credit to small businesses. Relationship lending depends on the accumulation over time by the Loan Officer of 'soft' information. Because the Loan Officer is the repository of this soft information, agency problems are created throughout the organisation that may best be resolved by structuring the bank as a small, closely-held organisation with few managerial layers. The shocks analysed include technological innovations, regulatory regime shifts, banking industry consolidation, and monetary policy shocks. The issue of credit availability to small firms has garnered world-wide concern recently. Models of equilibrium credit rationing that point to moral hazard and adverse selection problems (eg, Stiglitz and Weiss, 1981) suggest that small firms may be particularly vulnerable because they are often so informationally opaque. That is, the informational wedge between insiders and outsiders tends to be more acute for small companies, which makes the provision of external finance particularly challenging. Small firms with opportunities to invest in positive net present value projects may be blocked from doing so because potential providers of external finance cannot readily verify that the firm has access to a quality project (adverse selection problem) or ensure that the funds will not be diverted to fund an alternative project (moral hazard problem).
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Small business credit availability and relationship lending: the importance of bank organizational structure
Finance and Economics Discussion Series, 2001Co-Authors: Allen N Berger, Gregory F. UdellAbstract:This paper models the inner workings of relationship lending, the implications for bank organizational structure, and the effects of shocks to the economic environment on the availability of relationship credit to small businesses. Relationship lending depends on the accumulation over time by the Loan Officer of "soft" information. Because the Loan Officer is the repository of this soft information, agency problems are created throughout the organization that are best resolved by structuring the bank as a small, closely-held organization with few managerial layers. The shocks analyzed include technological innovations, regulatory regime shifts, banking industry consolidation, and monetary policy shocks.
Subhendu Bhowal - One of the best experts on this subject based on the ideXlab platform.
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costs of job rotation evidence from mandatory Loan Officer rotation
Management Science, 2020Co-Authors: Subhendu Bhowal, Krishnamurthy Subramanian, Prasanna L TantriAbstract:Job rotation inside an organization creates two conflicting effects. It disciplines agents by creating the fear that their successors may discover and report their hidden information. Thus, the age...
-
soft information and the cost of job rotation evidence from Loan Officer rotation
2013Co-Authors: Subhendu Bhowal, Krishnamurthy Subramanian, Prasanna L TantriAbstract:Job rotation, where a principal routinely rotates agents among tasks, is argued to be a powerful antidote for agency problems inside an organization. However, when soft information dominates transactions inside a firm, verifying the information set that led to a particular decision becomes difficult. This difficulty imposes costs on job rotation since an incoming agent cannot verify the information set that the outgoing agent utilized when arriving at a key decision. This lack of verifiability distorts incentives for effort when a decision straddles two agents since neither agent receives the entire marginal cost/benefit of her effort. In this study, we highlight this cost of rotation policies. We use unique data on over 50,000 Loans sanctioned by 51 Loan Officers of a large public sector bank in India, which follows a fixed-tenure-based policy of Loan Officer rotation. We find default probabilities to be about 8% higher for Loans that are likely to straddle an incoming and an outgoing Loan Officer when compared to other Loans. This difference does not stem from any differences in hard information captured in borrowers' credit histories or from Loan Officer rotation destroying lending relationships. Finally, we find evidence of credit rationing as well as the new Loan Officer rejects 7% more Loans for borrowers who have borrowed during the straddling period.
Matthew Stevenson - One of the best experts on this subject based on the ideXlab platform.
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the value of text for small business default prediction a deep learning approach
European Journal of Operational Research, 2021Co-Authors: Matthew Stevenson, Christophe Mues, Cristian BravoAbstract:Abstract Compared to consumer lending, Micro, Small and Medium Enterprise (mSME) credit risk modelling is particularly challenging, as, often, the same sources of information are not available. Therefore, it is standard policy for a Loan Officer to provide a textual Loan assessment to mitigate limited data availability. In turn, this statement is analysed by a credit expert alongside any available standard credit data. In our paper, we exploit recent advances from the field of Deep Learning and Natural Language Processing (NLP), including the BERT (Bidirectional Encoder Representations from Transformers) model, to extract information from 60000 textual assessments provided by a lender. We consider the performance in terms of the AUC (Area Under the receiver operating characteristic Curve) and Brier Score metrics and find that the text alone is surprisingly effective for predicting default. However, when combined with traditional data, it yields no additional predictive capability, with performance dependent on the text’s length. Our proposed deep learning model does, however, appear to be robust to the quality of the text and therefore suitable for partly automating the mSME lending process. We also demonstrate how the content of Loan assessments influences performance, leading us to a series of recommendations on a new strategy for collecting future mSME Loan assessments.