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The importance of Alternative Data in Credit Risk Management

The emergence of alternative data as a key enabler in expanding credit delivery and financial inclusion is unmistakable.

The saying that the only thing that is constant is change, is attributed to Heraclitus, the Greek Philosopher. This is so very relevant today in the way lenders use technology and scoring solutions to understand the credit worthiness of applicants. Credit Risk Management has come a long way from the days when banks used just one credit score cut off to…

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Added by Naagesh Padmanaban on March 25, 2019 at 11:15pm — No Comments

Fascinating Developments in the Theory of Randomness

I present here some innovative results from my most recent research on stochastic processes. chaos modeling, and dynamical systems, with applications to Fintech, cryptography, number theory, and random number generators. While covering advanced topics, this article is accessible to professionals with limited knowledge in statistical or mathematical theory. It introduces new material not covered in my recent book (available …

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Added by Vincent Granville on March 21, 2019 at 7:30am — No Comments

How to Automatically Determine the Number of Clusters in your Data - and more

Determining the number of clusters when performing unsupervised clustering is a tricky problem. Many data sets don't exhibit well separated clusters, and two human beings asked to visually tell the number of clusters by looking at a chart, are likely to provide two different answers. Sometimes clusters overlap with each other, and large clusters contain sub-clusters, making a decision not easy.

For instance, how many clusters do you see in the picture below? What is the optimum number…

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Added by Vincent Granville on March 13, 2019 at 6:00pm — No Comments

Deep Analytical Thinking and Data Science Wizardry

Many times, complex models are not enough (or too heavy), or not necessary, to get great, robust, sustainable insights out of data. Deep analytical thinking may prove more useful, and can be done by people not necessarily trained in data science, even by people with limited coding experience. Here we explore what we mean by deep analytical thinking, using a case study, and how it works: combining craftsmanship, business acumen, the use and creation of tricks and rules of thumb, to provide…

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Added by Vincent Granville on March 7, 2019 at 1:46pm — No Comments

The graph analytics landscape 2019

Read part 1 - The graph database landscape

The graph analytics landscape 2019

Graph analytics frameworks consist of a set of tools and methods developed to extract knowledge…

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Added by Elise Devaux on February 27, 2019 at 5:00am — No Comments

New Perspectives on Statistical Distributions and Deep Learning

In this data science article, emphasis is placed on science, not just on data. State-of-the art material is presented in simple English, from multiple perspectives: applications, theoretical research asking more questions than it answers, scientific computing, machine learning, and algorithms. I attempt here to lay the foundations of a new statistical technology, hoping that it will plant the seeds for further research on a topic with a broad range of potential…

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Added by Vincent Granville on February 23, 2019 at 11:00am — No Comments

A Plethora of Original, Not Well-Known Statistical Tests

Many of the following statistical tests are rarely discussed in textbooks or in college classes, much less in data camps. Yet they help answer a lot of different and interesting questions. I used most of them without even computing the underlying distribution under the null hypothesis, but instead, using simulations to check whether my assumptions were plausible or not. In short, my approach to statistical testing is is model-free, data-driven. Some are easy to implement even in Excel. Some…

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Added by Vincent Granville on February 13, 2019 at 7:00pm — No Comments

Machine Learning Glossary

For background to this post, please see Learn Machine Learning Coding Basics in a weekend. Here,we present the glossary that we use for the coding and the mindmap attached to these classes and upcoming book. About 80 terms are included in the glossary, covering Ensembles, Regression, Classification,…

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Added by Vincent Granville on February 12, 2019 at 12:31pm — No Comments

Alternatives to Logistic Regression

Logistic regression (LR) models estimate the probability of a binary response, based on one or more predictor variables. Unlike linear regression models, the dependent variables are categorical. LR has become very popular, perhaps because of the wide availability of the procedure in software. Although LR is a good choice for many situations, it doesn't work well for all situations. For example:

  • In propensity score…
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Added by Vincent Granville on February 7, 2019 at 3:23pm — No Comments

From Infinite Matrices to New Integration Formula

This is another interesting problem, off-the-beaten-path. It ends up with a formula to compute the integral of a function, based on its derivatives solely. 

For simplicity, I'll start with some notations used in the context of matrix theory, familiar to everyone: T(f) = g, where f and g are vectors, and T a square matrix. The notation T(f) represents the product between the matrix T, and the vector f. Now, imagine that the…

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Added by Vincent Granville on February 3, 2019 at 5:30pm — 1 Comment

Top 10 Technology Trends of 2019

First days after the celebration of the New Year is the time when looking back we can analyze our actions, promises and draw conclusions whether our predictions and expectations came true. As 2018 came to its end, it is perfect time to analyze it and to set trends for the next year. The amount of data generated every minute is enormous. Therefore new approaches, techniques, and solutions have been developed.…

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Added by Vincent Granville on January 29, 2019 at 11:43am — No Comments

Great Sunday Reading

Extract from the upcoming Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week. To subscribe, …

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Added by Vincent Granville on January 27, 2019 at 3:20pm — No Comments

Mining Customer Reviews to drive Business Growth

A passionate customer always provides feedback about his favorite product if it touches his emotional chord.

Product review contains wealth of information. Analyzing the review texts can unearth many hidden data points about the customer and the product. Such insights can help grow the business and gain revenue.

Lets look into a specific example. …

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Added by Kaniska Mandal on January 24, 2019 at 3:30pm — No Comments

Graph Analytics to Reinforce Anti-fraud Programs

Organizations across industries are adopting graph analytics to reinforce their anti-fraud programs. In this post, we examine three types of fraud graph analytics can help investigators combat: insurance fraud, credit card fraud, VAT fraud.

Detecting fraud is about connecting the dots



In many areas, fraud investigators have at their disposal large datasets in which clues are hidden. These clues are left behind by…

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Added by Elise Devaux on January 22, 2019 at 12:30am — No Comments

Great Sunday Reading

Extract from the upcoming Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week. To subscribe, …

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Added by Vincent Granville on January 20, 2019 at 12:15pm — No Comments

Understanding the foundations of Deep Learning through Linear Regression

This article was written by Ajit Joakar

In this longish post, I have tried to explain Deep Learning starting from familiar ideas like machine learning. This approach forms a part of my forthcoming book. I have used this approach in my teaching. It is based on ‘learning by exception,' i.e. understanding one concept and it’s limitations and then understanding how the subsequent concept…

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Added by Vincent Granville on January 16, 2019 at 9:48am — No Comments

5 reasons why graph visualization matters

Why is graph visualization so important? How can it help businesses sifting through large amounts of complex data? We explore the answer in this post through 5 advantages of graph visualization and different use cases.

What is graph visualization

Also called network, a graph is a collection of nodes (or vertices) and edges (or links). Each node represents a single data point (a person, a phone number, a transaction) and each edge represents how two nodes…

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Added by Elise Devaux on January 11, 2019 at 9:25am — No Comments

5 Predictions about Data Science, Machine Learning, and AI for 2019

Summary:  Here are our 5 predictions for data science, machine learning, and AI for 2019.  We also take a look back at last year’s predictions to see how we did.

 

It’s that time of year again when we do a look back in order to offer a look forward.  What trends will speed up, what things will actually happen,…

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Added by Vincent Granville on December 20, 2018 at 6:30pm — No Comments

New Books in AI, Machine Learning, and Data Science

We are in the process of writing and adding new material (compact eBooks) exclusively available to our members, and written in simple English, by world leading experts in AI, data science, and machine learning. In the upcoming months, the following will be added:

  • The Machine Learning Coding Book
  • Off-the-beaten-path Statistics and Machine Learning Techniques 
  • Encyclopedia of Statistical Science
  • Original Math, Stat and Probability Problems - with…
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Added by Vincent Granville on December 1, 2018 at 6:26pm — No Comments

Things that Aren’t Working in Deep Learning

Summary:  This may be the golden age of deep learning but a lot can be learned by looking at where deep neural nets aren’t working yet.  This can be a guide to calming the hype.  It can also be a roadmap to future opportunities once these barriers are behind us. The full article is accessible here, below is a…

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Added by Vincent Granville on November 21, 2018 at 10:00am — No Comments

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