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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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 (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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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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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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Generative Artificial Intelligence or Generative AI is a set of algorithms capable of generating text, images, or other media in response to prompts. It performs beyond simple classification problem. It can create new text, images, video, audio, code, or synthetic data. Whenever any AI technology is generating something on its own, it can be referred
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The process of extracting, transforming and loading (ETL) is a fundamental aspect of modern data integration. ETL is used to consolidate data from multiple sources, transform it into a format that can be used for analysis and load it into a target system. However, the ETL process can be time-consuming, complex and error-prone. In recent
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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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The world is one big ball of unrefined data and big data is the name of the game. Raw data doesn’t do much, but with the help of data analysis, it becomes invaluable. It provides valuable insights into all facets of company operations and performance – from consumer behaviour to underwriting practices to the ROI
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