Our news and views relating to Data Analytics, Big Data, Machine Learning, and the world of Credit.
Every year we compile a list of our Top 10 blog posts, to keep those who are out of the loop easily informed of the latest developments and thinking in data analytics. But in the interest of evolving and practicing what we preach, we are letting data inform the structure of our “Top Blogs” post this time around.
We have a bit of a joke in the office around how data scientists in 2027 will have a good laugh at what we define as ‘Big Data’ in 2017. Pat pat, there there, I guess that was Big Data back then. Unlike the term Big Data, Machine Learning is here to stay. It is after all one of the foundations of Artificial Intelligence and this is rapidly becoming more and more part of our culture. The impact of Machine Learning is being felt on a daily basis, from using interactive devices like Amazon’s Echo to do our shopping, learning a language through DuoLingo, or interacting with chatbots to get your statement in under a second instead of waiting “for the next available agent”. So what has happened, why the recent explosion of Machine Learning applications?
The word optimisation is used quite loosely and can relate to many different areas. For example, there is search engine optimisation (getting your website pages to the top of online search results), process optimisation (making existing processes more efficient), code optimisation (making your code run more efficiently) and then there is mathematical optimisation. In this blog post, we'll be focusing on mathematical optimisation: what it is, how it can be applied in making more optimal business decisions at a customer level, and specifically how it's applied in credit risk. And you can even try using optimisation yourself - using an optimisation tool we've shared in this post - to see the various scenarios resulting from your decisions. Scroll to the bottom to try it for yourself!
Everyone is wanting to learn more about how machine learning can be used in their business. What’s interesting though, is that many companies may already be using machine learning to some extent without really realising it. The lines between predictive analytics and machine learning are actually quite blurred. Many companies will have built up some machine learning capabilities using predictive analytics in some area of their business. So if you use static predictive models in your business, then you are already using machine learning, albeit of the static variety.
In this blog post I’ll be covering what bots are, how bots are used, the growing popularity of bots and the three types of bots that I have come across.