Credit scoring is the process enabling lending businesses to determine how likely a lender will default its loan. This process is utilizing different data sources and less or more is designed around the business logic of the lending organization. In some markets like USA and UK, there are credit scoring bureaus assigning credit score derived from credit files and history of a particular person. In other markets, the credit scoring is up to the lending institutions, not matter banks or nonbank financial institutions like leasing companies, consumer finance companies, telecommunication companies, etc. Such businesses hold the credit risk and they need a solution like credit scoring. In this blog post, we explain why credit scoring as a service is the most viable option for lending businesses of different size and markets. Let us share with you the three main options in front of a company dealing with such kind of financial risk.
Have you ever heard about Moore`s law? In 1965, Gordon Moore, who would later become one of the founders of Intel, wrote a paper claiming that the number of electronic components, which could be placed into an integrated circuit, will double every year. This exponential became known as Moore`s law and turned out to be the foundation of the digital world.
Just think of it! Without credit and lending businesses, we would not be able to buy a house or appliances when we need them. Business would not be able to grow, expand and innovate at the desired pace. Credit is the fuel of any country` economy and this is not a secret. But what is fueling credit and lending businesses? How do they decide if you are eligible for a particular loan or not?
The worldwide known fast fashion retailer H&M is suffering from a problem with unsold stock worth $4.3 billion. The situation became clear when the Q1 report of the Swedish giant was released. It made analysts and commentators speculating with the company capability to stay competitive. Such statements are problematic, especially for a public company with significant free float.
We`re not sure if you already have heard about this and if not, keep in mind that those couple of minutes will be more than worthy.
The Spotify Fraud
Unknown scammer, allegedly from Bulgaria generated about $3m revenue out of creating and continuously playing a couple of playlists with tracks with an average length of 43 seconds. Spotify is paying artists about $0.004 per play and the fraudster has registered around 1200 premium Spotify accounts, continuously rolling the playlists.
It is believed that such ‘performance’ was achieved via bots automating the skip and play game regarding the Spotify policy to pay for a listened track only its 30 seconds or more. This way, the playlists called ‘Soulful Music’ and ‘Music From The Heart’ became played so many times they made it to number 84 globally and 22 in the US in the playlists charts.
It was 1997 when the Deep Blue computer beat the world chess champion, Garry Kasparov. It was the first machine over human victory while playing the mother of all strategic games. Well, even though the Deep Blue computer was created specifically to play chess versus human, it took just a few years more for standard desktop computers to dominate our brains on the chess board. Now there are smartphone chess apps have been able to defeat exceptionally good players.
This was the first time when the machine ‘outminded’ humans. By declaring that AI software scored a better result than humans in a large-scale reading and comprehension test, it seems we are witnessing the second breakthrough.
It is that time of the year when you take a look back and take an educated guess on what is going on to be in the very near future of data analytics. There is no doubt that in 2018 analytical industries will be not just hot but also rapidly changing. Here are the top trends that will prevail within the New Year.
Black Friday is marking the beginning of the X-Mass shopping hysteria since 1952 when it was created as a retailer campaign aimed to clear stock on promotional prices. More than half century later, Black Friday and the entire weekend after Thanksgiving, including Cyber Monday transformed into a shopping spree, helping retailers to generate more sales and revenues. Since data is one of the key assets for any retailer, its analysis is helping to boost the efficiency and overall campaign performance. As Black Friday revenues are estimated at billions of dollars, it is worthy to get aware how to take a bigger piece of this retail pie. In this blog post, we are going to share 3 different analytics applications helping retailers to achieve more on Black Friday.
Worldwide known data science community Kaggle did something nice and sweet as industry-wide survey sharing interesting and valuable information on data scientists from around the globe. More than 16 000 data scientists, analysts, experts, and statisticians joined the Kaggle survey which is full of interesting insights. Among the most important is the fact that 3 of every 4 participants rely on Python, followed by R and SQL and the logistic regression is the most commonly used data science method, followed by decision trees and random forests.
Most commonly applied in boosting the efficiency of a measurable processes, data analytics is hardly scary by any means. Well, just like the machines or any product of the human knowledge at all, data analytics can be a scary tool. The frightening element within data analytics is not the power of data but its usage. Prior to Halloween, we would like to share a few scary data analytics applications out of real life.