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Credit Risk Modeling Using Transfer Learning and Domain Adaptation
Frontiers in Artificial Intelligence, 2022 In the domain of credit risk assessment lenders may have limited or no data on the historical lending outcomes of credit applicants. Typically this disproportionately affects Micro, Small, and Medium Enterprises (MSMEs), for which credit may be ...
Hendra Suryanto +5 more
doaj +5 more sources
IMPLICATIONS OF CREDIT RISK TRANSFER ON BANK PERFORMANCES [PDF]
Economy and Sociology, 2015 The impact of the financial crisis has demonstrated the fragility of the banking sector and the need to implement new technologies that would allow not only insurance against the most important credit risk - credit risk, but development of lending ...
Victoria COCIUG +1 more
doaj +2 more sources
Credit Risk Transfer and Bank Competition [PDF]
SSRN Electronic Journal, 2009 Abstract We present a banking model with imperfect competition in which borrowers’ access to credit is improved when banks are able to transfer credit risks. However, the market for credit risk transfer (CRT) works smoothly only if the quality of loans is public information.
Hendrik Hakenes, Isabel Schnabel
core +7 more sources
Investigation on the credit risk transfer effects on the banking stability and performance
Cogent Economics & Finance, 2022 Considered among of the main causes of the 2007 financial crisis, the credit risk transfer activities deserve nowadays particular attention. This study discusses the continuous effectiveness of the credit risk transfer activities by investigating their ...
R. Younes
doaj +3 more sources
Credit Risk Transfer and Contagion [PDF]
SSRN Electronic Journal, 2005 Some have argued that recent increases in credit risk transfer are desirable because they improve the diversification of risk. Others have suggested that they may be undesirable if they increase the risk of financial crises. Using a model with banking and insurance sectors, we show that credit risk transfer can be beneficial when banks face uniform ...
Allen, Franklin, Carletti, Elena
openaire +5 more sources
FedVI: Financial Cross-Domain Federated Learning with Scarce Overlapping Samples via Visual Representation of Heterogeneous Tabular Data and Meta-Optimization [PDF]
EntropyFederated learning offers a promising approach for cross-institutional financial risk control modeling but encounters two key challenges in practice: feature space heterogeneity and low sample overlap rate.
Kaiqing Yuan, Jiang Wu
doaj +2 more sources
Credit Risk Transfer and Systemic Risk
New Economic WindowsAbstractThis chapter investigates the relationship between the banking and insurance industry by focusing on systemic risk. The concept of credit risk transfer stems from banks’ inclination to offload credit risks. Insurance companies, particularly those specializing in risk transfer services, emerge as natural recipients for these risks.
exaly +2 more sources
A Study of the Influence and Influence of Factors Affecting the Stability of the Banking System in Selected Countries of the Mena Region [PDF]
مجله توسعه و سرمایه, 2021 Objective: Banking, by its very nature, involves a wide range of risks. Banking supervisors should identify their risks and evaluate and manage them. Therefore, the factors affecting banking stability should be identified and applied in proportion to the
Babak Kouhi leilan +3 more
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Research on Contagion and the Influencing Factors of Personal Credit Risk based on a Complex Network
Discrete Dynamics in Nature and Society, 2022 With the digital transformation of commercial banks and the online transfer of credit transactions, the relationship between credit subjects tends to be complex, and personal credit risk management is facing challenges.
Xin Sui +3 more
doaj +1 more source
Cross-Domain Credit Default Prediction via Interpretable Ensemble Transfer
International Journal of Crowd Science, 2023 The evaluation and prediction of credit risk have always been a research hotspot to ensure the healthy and orderly development of the credit market. Most researchers use deep learning to predict credit risk.
Zhida Shang +5 more
doaj +1 more source