Banks face diverse challenges to manage risk with simple spreadsheets and gut feelings. Banks choose Big Data Analytics for Risk Management
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Cox Proportional Hazards Model came into effect in 1972, when David Cox developed the proportional hazards model which derives robust estimates of covariate effects using proportional hazards assumption.
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The Kaplan-Meier model, a non-parametric estimator, is one of the most widely used techniques in survival analysis, and it has found several applications in banking. From assessing loan defaults to predicting customer churn, the Kaplan-Meier estimator provides valuable insights that aid in effective decision-making. In the banking domain, understanding and predicting customer behavior, financial risks,
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Generative AI in banking has spread in wings in the last two years as never before. It has become a game-changer not only in banking, but also in every other industry. Generative AI in banking has far-reaching impacts. While traditional machine learning and artificial intelligence have turned out to be efficient across various aspects of
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Daron Acemoglu, Simon Johnson, and James A. Robinson, all of them from USA, have won the 2024 Nobel Prize in Economics.
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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
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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
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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
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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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