Personal Finance Application

Make Personal Finance fun again by automated segmentations, benchmarking gamification and automated machine learning budgeting tool.



ELA AS is newly founded startup with a clear mission to make use of data to benefit humanity. #DataForGood is our core value and a hashtag of our activities in social media. We have started with four projects, but we hope to continue with some more:  

1. Personal Finance Digital Assistant which is an add-on solution to your digital bank account that gives you better picture of personal finances based on bench-marking against predefined data set of your segmentation (ex: income range, family members, region you live etc.) and fully automatized data input for all segments: cost, income and balance. This will include a machine learning (ML) algorithm that will suggest you the best way to save and invest money, how to overcome a financial difficulty and how to perform in budgeting your economy the best way.  

We will soon be in Kickstarter and I hope that you will support our project!

THE ECONOMETRICIAN’S TAKE ON EURO 2016

We present a model for predicting the outcome of the 2016 European Football championship in France from June 10 to July 10. It is similar to our model for the 2014 World Cup. Using historical performance data for each team—most importantly the Elo rating system originally devised to rank chess players—we estimate a set of probabilities that a particular team will reach a particular round, up to and including the championship. We also provide a modal “most likely” case for how the tournament will unfold (although “most likely” does not mean “likely”). The model says that France has a 23% probability of winning the trophy, followed by Germany at 20%, Spain at 14%, and England at 11%. Although Germany has the highest Elo rating, France is slightly favored because of its home advantage. After each day of play, we will re-run the model using updated historical performance data in order to generate new probabilities and a new modal forecast. How much faith should we have in these predictions? On the plus side, our approach carefully considers the stochastic nature of the tournament using statistical methods; also, the predictions are not far from bookmakers’ odds. On the minus side, the environment is “stochastic” indeed, i.e., football is quite an unpredictable game! That charming unpredictability was on full display two years ago, when our model failed to anticipate the elimination of heavyweights Spain and Italy in the group stage and gave Brazil a 48% probability of winning the trophy. More encouragingly, it identified three of the four semifinalists before the start of the tournament, and the fully updated version predicted the winner of every match in the knockout stage except for the 7-1 semifinal between Germany and Brazil. Below we will introduce our statistical model for predicting the outcome of the 2016 European Football Championship in France from June 10 to July 10.