Banking Analytics - Tanuka Mandal

Fraud Detection in Banking Industry using Data Analytics

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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

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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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Credit Risk Assessment and Big Data Analytics

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Credit risk assessment or analysis is the analysis of historical data for understanding of a borrower’s creditworthiness or assessment of the risk involved in the granting of a loan. The outcome of the analysis helps banks and financial institutions to evaluate their risks as well as their customers. The aim of credit risk assessment is

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Understanding the ‘Loss Given Default’ Model

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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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Estimation of Probability of Default

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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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Importance of Post-Model Adjustments in Banking Sector

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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

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Logistic Regression in Credit Risk Analytics

Leave a Comment / Banking Analytics, Machine Learning / Tanuka Mandal

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

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Data Analytics in Finance and Banking Sector

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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 – The Way Banks Do Business

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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

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