scholarly journals The Fundamental Determinants of Credit Default Risk for European Large Complex Financial Institutions

Author(s):  
Inci Otker Robe ◽  
Jiri Podpiera

2010 ◽  
Vol 10 (153) ◽  
pp. 1 ◽  
Author(s):  
Jiri Podpiera ◽  
Inci Ötker ◽  
◽  


2019 ◽  
pp. 221-233
Author(s):  
Vritti Bang ◽  
Shreyansh Bhansali ◽  
Devansh Doshi ◽  
Asawari Vedak

India has been riddling since decades with the problem of insolvency and bankruptcy issues. Several public sector banks, financial institutions and operational creditors were facing severe credit default risk. Various laws and codes have been passed as a corrective measure, but have proved to be inefficient and failed to provide any kind of relief to the creditors. There was thus a need for reform in insolvency and bankruptcy laws. The Insolvency and Bankruptcy code 2016 (IBC) has been instrumental in creating a shift in the way the bankruptcy process of defaulting firms has been dealt with. The IBC 2016 promises to bring about transparency, method and infrastructure in the entire system of liquidation. Changing up core aspects of the insolvency process, it gives companies a well-deserved chance at revival. Despite the recent amendments to the code and regulation changes by the Insolvency and Bankruptcy Board of India, there are still few grey areas in the code. This paper aims to thus test the effectiveness of the IBC 2016 since its introduction in 2016 and whether it resolves lags in the previous system. Hence, the paper dwells into the various components of IBC to critically analyse its sustainability and scalability. The research paper is purely based on secondary research through different news articles and reports from reliable sources. Though it is too early to comment on the impact of the IBC 2016, the researchers have tried to study the code and conclude whether it will be successful in fixing the problems and will keep up to its promise in the long run.



2009 ◽  
Author(s):  
Giovanni Calice ◽  
Christos Ioannidis ◽  
Julian M. Williams


2011 ◽  
Author(s):  
Giovanni Calice ◽  
Christos Ioannidis ◽  
Julian M. Williams


2011 ◽  
Vol 42 (1-2) ◽  
pp. 85-107 ◽  
Author(s):  
Giovanni Calice ◽  
Christos Ioannidis ◽  
Julian Williams


2010 ◽  
Author(s):  
Giovanni Calice ◽  
Christos Ioannidis ◽  
Julian M. Williams


2010 ◽  
Author(s):  
Giovanni Calice ◽  
Christos Ioannides ◽  
Julian M. Williams


2020 ◽  
Vol 28 (1) ◽  
pp. 1-7
Author(s):  
Irving Simonin ◽  
Marc Brooks ◽  
Luis Enrique Nieto Barajas

This article presents an exciting application of machine learning for loan origination in microfinance. Microfinance targets people who cannot build a credit history and therefore cannot access loans from banks or other financial institutions. We use data from a Mexican microfinance company that operates in several regions throughout the country. The objective is to guide intermediate lenders to choose their clients and achieve a lowerr credit default risk. We use several statistical models such as principal component analysis, clustering analysis and a regression tree. We obtain, as a result, a series of recommendations based on the characteristics of the clients.



Author(s):  
Giovanni Calice ◽  
Christos Ioannidis ◽  
Julian M. Williams


Sign in / Sign up

Export Citation Format

Share Document