How using data helps banks minimize the risks of loan default
Automation of your scoring mechanism using predictive models enables a shift from reactive to proactive credit risk management
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Data Science
Code quality matters
In the modern age of big data, data science solutions are entering ever new fields. The market is literally over-saturated with start-ups, which daily generate new and modern ideas on how to extract even more from data. So, it’s surprising that ML hasn’t taken over the business of all major corporations yet, and it’s even harder to understand how...
Data-driven incident handling
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 processes always cost dearly. On top of that, slow processes…
Spark AI Summit – what is new in the world of big data?
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? ...
Managing credit risk with advanced data analytics
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...