In the realm of finance and credit risk management, the concept of Loss Given Default (LGD) plays a crucial role. It involves identifying, assessing, and mitigating potential losses that could arise from various factors, such as market fluctuations, credit defaults, and operational failures. Among these risks, credit risk stands out as a significant concern for […]
Understanding the ‘Loss Given Default’ Model Read More »
Probability of default (PD) offers a glimpse into the borrower’s future: how likely are they to miss payments and ultimately default on their debt? Understanding PD is crucial for banks and investors to individuals making personal loans. It’s the backbone of informed decision-making, helping assess risk, price loans fairly, and allocate capital wisely. When economic
Estimation of Probability of Default Read More »
Overlays, or post-model adjustments (PMAs) is a term that is used to describe a spectrum of adjustments that are made outside the primary models. Banks often use post-model adjustments, where risks and uncertainties are not properly reflected in existing models. The objective of a post-implementation review is to assess shortcomings where models or data have
Importance of Post-Model Adjustments in Banking Sector Read More »
Many credit scoring techniques have been used by banks to build credit scorecards. Among them, logistic regression model is the most commonly used in the banking industry. In the banking industry, logistic regression, linear regression, linear programming and classification tree have been used to develop credit scorecard systems. Logistic regression is the most commonly used
Logistic Regression in Credit Risk Analytics Read More »
The finance and banking sector is one of the most data-rich industries in the world, generating vast amounts of data on a daily basis. The traditional banking institutions have been collecting customer data for decades, but with the emergence of fintech companies, the competition has become more intense, and banks are increasingly turning to data
Data Analytics in Finance and Banking Sector Read More »
Banking analytics is the use of machine learning techniques and artificial intelligence in customer data to make decisions in banking domain. Banking analytics is a management tool that gives insight on current performance and highlights areas where there is scope of improvement. This will help banks make informed decisions, prevent errors, and improve efficiency. Benefits
Banking Analytics – The Way Banks Do Business Read More »