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
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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 »