How to use data to shift your business from reactive to proactive It is beyond any doubt that financial organizations have to focus on improving their processes for handling incidents. Whether we are talking about fraud, risk management or regular operations, every slowdown in incident-handling…
Before we dive into releases, I want to highlight one fact. Python is the most used language in the Databricks platform. If you have been working in big data for some time, you have probably had this discussion at least once. Scala/Java or Python? Which language should I choose for my big data processing? …
The traditional approach to quantifying credit risk in retail banking often relies on the most obvious variables such as annual/monthly income, employment length, employment title, etc. While these are still valid indicators for calculating…
As even the slowest thinkers of our current era have already recognized, social networks and related phenomena, defined in the broadest possible terms, are a great source of insight into human behaviour and preferences as well as a vital resource for data analysis and predictive…
Graduating students hope that they will create a new recommendation system or search ranking algorithm. In reality, they have to deal with missing data, scalability and integration issues. On top of that, they have to learn the company’s processes or build new ones. It is…
The effectiveness of data science projects is one of the hottest topics in business intelligence nowadays. On the one hand, we are discussing the huge potential of artificial intelligence in executing sophisticated tasks for businesses. On the other hand, we see the harsh reality of…
In the last five years of providing data science as a service to Europe’s largest banks, we have faced plenty of cases where banks were unprepared to work with their data. Unfortunately, this problem arises not only at the stage…
Over the years, we have often heard from our clients that black box machine learning solutions are unacceptable. In the previous article, we went over three key questions to ask before…
With the boost of computing power in past years, it has happened that almost anyone can run a sophisticated machine learning model, e.g. neural networks. And with modern frameworks such as Tensorflow, it is quite accessible and also popular to do everything with such a…
Every company doing business in retail has been dealing with customer segmentation, and so have banking organizations trying to describe their client base effectively. Customer segmentation in banking is key to the success of marketing campaigns, product cross-selling and risk scoring. That’s why it’s critical…
