Big Data Analytics - Tanuka Mandal

Scorecard Model in Banking: Enhancing Risk Management and Decision Making

A scorecard model is a statistical tool used to assess risk, often in loan applications. It analyzes borrower data like income and credit history, assigning points to different factors. These points are totaled to create a score that predicts the likelihood of loan repayment. Scorecards help lenders make objective decisions and streamline the approval process. […]

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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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Data Wrangling : Understanding, Why its Important

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Data wrangling has become the primary process to remain competitive for organizations. Data is the backbone of the digital age, and with growing volume leading to data explosion, the need for effective data handling becomes paramount. Among the essential processes in the realm of data science is Data Wrangling. This article delves into the intricacies

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Estimation of Probability of Default

Leave a Comment / Banking Analytics, Big Data Analytics / Tanuka Mandal

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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Zero ETL: A Revolution in Data Integration

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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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Geoanalytics and Its Usage

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Geoanalytics or geoanalysis, is a field of study that applies geographical concepts and machine learning methods to the analysis of information. Geoanalytics is an emerging science where big data technology extracts patterns, meaning and insights from complex geospatial datasets. It is a layer of geographical data added to business datasets. Hence it is also called

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Prescriptive Analytics – toward best solutions

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Prescriptive Analytics may be defined as the branch of analytics which provides guidance on how to make optimal use of data and gain maximum output from it. It is related to both descriptive analytics and predictive analytics, but it helps users to determine the best solution among various possibilities. Descriptive analytics offers BI insights into

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Apache Spark – The Analytics Engine for Big Data

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Apache Spark a.k.a. Spark is an open-source data processing engine which is mainly used to handle and analyze large datasets. Apache Spark was originated to improve the efficiency of Apache Hadoop. Ideally an engine to process data, Spark can also be leveraged as a database where we can use it as a table and query

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Outlier Analysis with Data Mining

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Data Mining is a process of discovering patterns from a large data set by implementing machine learning and statistics. It is also call it Knowledge Discovery in Data (KDD). One of the most vital feature in data mining is outlier analysis or detection. In statistics or data science, an outlier is a point which is

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