In general, expected credit loss as the name suggests is the expected loss from a loan exposure. On the other hand, unexpected credit loss is the loss that exceeds the expectations. Credit loss, a fundamental risk in financial institutions, is the economic loss resulting from a borrower’s inability to meet their financial obligations. Whenever a […]
Expected Credit Loss vs Unexpected Credit Loss in Banking Read More »
Bootstrapping in statistics is a powerful technique that allows us to estimate the properties of a statistic (like its standard error or confidence interval) by repeatedly sampling from the original dataset. It is a resampling technique used to estimate the distribution of a sample statistic by repeatedly resampling with replacement from the original dataset. This
Bootstrapping in Statistics : A Powerful Tool for Inference Read More »
The Credit Conversion Factor (CCF) is a concept used in banking regulations to estimate the potential risk of off-balance sheet items. It’s a way to assess how likely an off-balance sheet commitment, like a loan guarantee, might turn into a real loan on the bank’s books. This plays a crucial role in ensuring banks maintain
Understanding the Credit Conversion Factor: A Pillar of Bank Capital Adequacy Read More »
Survival analysis is a branch of statistics originally developed for analyzing time-to-event data. It has substantial application in various fields, including medicine, engineering, and economics. In recent years, survival analysis in banking has become immensely popular in this sector due to its ability to provide insights into customer behavior, risk management, and the overall financial
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A scorecard model is a statistical tool used to assess risk, often in loan applications. It analyzes borrower data like income and credit history, assigning points to different factors. These points are totaled to create a score that predicts the likelihood of loan repayment. Scorecards help lenders make objective decisions and streamline the approval process.
Scorecard Model in Banking: Enhancing Risk Management and Decision Making Read More »
The financial world thrives on predictions. Understanding and incorporating macroeconomic scenarios and probability weights are pivotal when it comes to calculating Expected Credit Loss (ECL). This blog explores the relationship between macroeconomic factors and credit risk assessment. Let’s shed some light on the process of assigning probability weights to various economic scenarios. By delving into
Macroeconomic Scenarios and Probability Weights in Expected Credit Loss Calculation Read More »
Stress testing in banks is a technique used to evaluate the resilience of financial systems under adverse conditions. In today’s ever-evolving financial landscape, banks rely heavily on predictive models to navigate risk, optimize operations, and inform strategic decisions. However, the efficacy of predictive models in banking hinges on their ability to withstand adverse conditions and
Stress Testing in Predictive Model Building in Banks Read More »
Credit default risk is a critical consideration for lenders and investors in the financial world. It refers to the likelihood that a borrower will fail to meet their debt obligations. This ultimately leads to default. Several factors contribute to credit default risk, including economic conditions, borrower characteristics, and the performance of loan portfolios. Evaluation of
How Restructures, Write-Offs and Delinquencies Impact Credit Default Risk Read More »
Expected Credit Loss refers to the estimated amount of loss a bank can expect to incur on a financial asset over its lifetime. In the landscape of financial risk management, understanding and implementing effective credit loss models is crucial for financial institutions worldwide. The two prominent frameworks, Basel III and IFRS 9, play pivotal roles
Expected Credit Loss: Basel III vs IFRS 9 – A Comparative Analysis Read More »