Finance

Data Science Foundations for a New Stock Market In this article, I describe a new trading and gaming system, based on state-of-the-art mathematical engineering, robust architecture, and patent-pending technology. It offers an alternative to the stock market and traditional gaming. This system is also far more transparent than the stock

Applying Machine Learning Techniques in Quantitative Finance Two weeks ago, I was invited to present about Machine Learning and its applications in Quantitative Finance at a conference in London, UK. Without a break, I went through four research papers which I completed over the last two years with my co-author;

An Application of Data Science and Mathematics in Finance Imagine that one day, you see people are queuing up in front of Bank A; so you ask the staff at the counter, you are told that they are offering anyone (regardless of their credit history) a loan of $100,000 at

Lecture slides: Introduction to Adjoint Differentiation and Back-Propagation in Machine Learning and Finance To access the document, go to https://antoinesavine.files.wordpress.com/2018/11/intro2aadinmachinelearningandfinance.pdf This is a work in progress, feedback is highly appreciated. Content Introduction Adjoint Differentiation – AD Application: model fitting Calibrating a financial pricing model Training a deep learning model Application: market

Applying Noise Reduction to Stock Market Data I don’t believe the issue of stock market noise has received much attention from professionals in the investment industry.  I am uncertain if it is much of a topic even among traders.  It certainly wouldn’t be unusual for noise to be discussed among

Application of Machine Learning in Pricing and Revenue Management Prescriptive Analytics and B2B Pricing Science Pricing science is the application of analytical techniques and methods to solve the problem of setting prices. This discipline had its origins in the development of yield management in the airline industry in the 1980s, and has since

Finance basics for data scientists I picked up a little book called “Finance Basics” published by Harvard Business Review Press, for a short in-flight reading. This tiny book isn’t going to make someone a finance expert but I did find a few things useful for data scientists and business analysts

Machine learning in finance: Why, what & how Machine learning in finance may work magic, even though there is no magic behind it (well, maybe just a little bit). Still, the success of machine learning project depends more on building efficient infrastructure, collecting suitable datasets, and applying the right algorithms.

Top 7 Data Science Use Cases in Finance In recent years, the ability of data science and machine learning to cope with a number of principal financial tasks has become an especially important point at issue. Companies want to know more what improvements the technologies bring and how they can

Extending churn analysis to revenue forecasting using R In this article we will review application of clustering to customer order data in three parts.  First, we will define the approach to developing the cluster model including derived predictors and dummy variables; second we will extend beyond a typical “churn” model

The New Face of Credit Card Fraud You would think that I am going to talk about new levels of sophistication achieved by cybercriminals. No, here I discuss a different trend: Outright credit card fraud committed by big  businesses, in this case Amazon.  While this is about my experience, and

How will AI change the future of banking and financial services? Artificial Intelligence on the Horizon The Future of Banking (First in a Series) Introduction Humanity has been on the road for a very long time—from the beginning, when each individual had to collect sufficient food to survive every single

How are Data Scientists Important in Finance? How are Data Scientists Important in Retail Banks? I wanted to start this blog as a few Data Scientists I had spoken to have mentioned they are less interested in Finance. This series will focus on how Data Scientists can make an impact in

How are Data Scientists Important in Finance How are Data Scientists Important in Retail Banks? I wanted to start this blog as a few Data Scientists I had spoken to have mentioned they are less interested in Finance. This series will focus on how Data Scientists can make an impact in

Artificial Intelligence for Digital Payments Security Abstract – Digital wallets are becoming the new way to pay. Most interesting factor here is to know companies who are most successful in this field actually came out of payment industry with no prior knowledge or payment intelligence but rather came with Artificial Intelligence.

Big Data for FinTech & InsureTech Abstract – Last Sunday I was at big retail store in Harare and it was a very busy day due to the fact it was month end and people got paid. Grocery shopping was in full swing, I also bought some groceries for my self. When

The Success Story of The Biggest Online Payment System -PayPal Here is a guest blog from Raj Vardhman.  Raj  is an ardent supporter of responsible gambling and good casino practices, which is why he likes PayPal particularly much, as the e-wallet collaborates only with the most reliable casinos.In his free time

Churn Rate: Calculations and Strategies for Reducing Mobile App Attrition Ready for some sobering stats? The average mobile app loses 77% of its DAUs within the first 3 days after install. Within 30 days, that number jumps to 90%. Within 90 days, it’s over 95%. For your app to succeed, you

10. Credit Limits Model Posted by Yi-Chun Tsai on December 29, 2009 Link: 10. Credit Limits Model