Banking Analytics

Macroeconomic Scenarios and Probability Weights in Expected Credit Loss Calculation

Macroeconomic Scenarios and Probability Weights in Expected Credit Loss Calculation

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 […]

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Stress Testing in Banks in Predictive Model Building

Stress Testing in Predictive Model Building in Banks

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

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credit default risk

How Restructures, Write-Offs and Delinquencies Impact Credit Default Risk

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

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Expected Credit Loss

Expected Credit Loss: Basel III vs IFRS 9 – A Comparative Analysis

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

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Fraud detection in banking

Fraud Detection in Banking Industry using Data Analytics

Fraud detection in banking industry is becoming crucial day by day. In this digital age, where every transaction leaves a digital trail, the banking industry faces a constant battleground: fraud. From credit card skimming to sophisticated cyberattacks, fraudsters are becoming increasingly cunning, leaving banks scrambling for solutions. But amidst this digital arms race, a powerful

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Significance of EAD in Calculating ECL in the Banking Industry

In the dynamic world of banking, risk management plays a crucial role in ensuring financial stability. One crucial aspect of this is the calculation of Expected Credit Loss (ECL). This process involves various components, and among them, Exposure at Default (EAD) holds a key position. In this article, we will delve into the importance of

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Loss Given Default

Understanding the ‘Loss Given Default’ Model

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

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probability of default

Estimation of Probability of Default

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

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