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All Blog Posts Tagged 'Modelling' (6)

Credit Risk Prediction Using Artificial Neural Network Algorithm

1 Introduction

Credit risk or credit default indicates the probability of non-repayment of bank financial services that have been given to the customers. Credit risk has always been an extensively studied area in bank lending decisions. Credit risk plays a crucial role for banks and financial institutions, especially for commercial banks and it is always difficult to interpret and manage. Due to the advancements in technology, banks have managed to reduce the costs, in order to…

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Added by Shruti Goyal on March 14, 2018 at 11:30am — 5 Comments

Avoiding Look Ahead Bias in Time Series Modelling

Any time series classification or regression forecasting involves the Y prediction at 't+n' given the X and Y information available till time T. Obviously no data scientist or statistician can deploy the system without back testing and validating the performance of model in history. Using the future actual information in training data which could be termed as "Look Ahead Bias" is probably the gravest mistake a data scientist can make. Even the sentence “we cannot make use future…

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Added by Rohit Walimbe on April 21, 2017 at 6:00am — No Comments

Building A Predictive Model

“Predictive analytics” is a commonly used term today. Wikipedia describes it as ‘encompassing a variety of statistical techniques from modeling, machine learning, and data mining that analyze current and historical…

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Added by Aureus Analytics on November 21, 2015 at 12:30am — No Comments

Applying Data Exploration & Discovery techniques to BCBS 239

Time is fast running out for G-SIBS (and indeed D-SIBS) to demonstrate compliance with the principles of BCBS 239.  Many surveys have been conducted by firms such as EY, McKinsey and Deloitte – none of which paint a particularly pretty picture in terms of readiness. Most suggest that the majority of banks will only be able to demonstrate compliance with between 25% and 60% of the listed principles by the January 2016 deadline. Why is this?

At a recent industry event discussing…

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Added by James Phare on September 15, 2015 at 5:22am — No Comments

Modelling a Data Warehouse

When designing a model for a data warehouse we should follow standard pattern, such as gathering requirements, building credentials and collecting a considerable quantity of information about the data or metadata. This helps to figure out the formation and scope of the data warehouse. This model of data warehouse is known as conceptual model. General elements for the model are fact and dimension tables. These tables will be related to each other which will help to identity relationships…

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Added by Avesh Dhakal on June 8, 2014 at 7:54am — 1 Comment

Dimensional Modelling

There isn’t any specific standard to model data warehouse. It can be built either using the “dimensional” model or the “normalised” model methodologies. Normalised model normalises the data into third normal form (3NF) whereas dimensional model collects the transactional data in the form of facts and dimensions. Normalised model is easy to use as we can add related topics without affecting the existing data. But one must have good knowledge of how data is associated before performing…

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Added by Avesh Dhakal on May 29, 2014 at 4:12am — No Comments

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