Our news and views relating to Data Analytics, Big Data, Machine Learning, and the world of Credit.
The benefits have been recounted many times, but now that Machine Learning has the business world’s attention, how does one get started? Moving into the machine learning space can be somewhat daunting, but we hope this blog post provides some guidance that you will find helpful.
We are quite proud of the ability to develop performant, stable and trustworthy predictive models here at Principa. For nearly 20 years, we have been developing predictive models that have helped so many of our clients to make better decisions, more often than not outperforming what our best competitors can achieve. The models that we have historically developed can be categorised as part of the additive group of models – that is, a handful of predictive characteristics are selected and classed in a way that best separates the ‘goods’ from the ‘bads' (i.e. the traditional binary classification application). Depending on the new unseen data, the resulting weightings are then added together to get a final score. For example, consider a 3-feature model that uses only Home Ownership, Years at Employer and Age. Let's say you are a homeowner and for this you get 10 points, you have been with your employer for 5+ years (15 points), and you are 23 years of age (8 points), then your final score is 33, and the strategy will use this score and decide where you should go in the business decision tree.
Machine learning models can be used very successfully in many different contexts to predict outcomes for different use cases accurately. These predictions can be used within the business to make better decisions or to operate more efficiently (or both) and can give you an edge over your competitors. Predictive models all follow the same recipe – i.e. train a model on historical data and then apply this model to unseen data to get predictions. If your model generalises well, you have a prediction that you can trust and use to decide "do this, not that" with some degree of accuracy.
During the last year, we’ve experienced the escalation of social issues around artificial intelligence (AI), with Elon Musk leading the charge. Musk continues to advocate the idea that humanity is getting closer to a Skynet-like future – to many people’s concern. One of the very real and valid concerns is the idea that many existing jobs will be automated, thanks to AI.
A Deep Learning IndabaX is a locally-organised, one-day Indaba that helps spread knowledge and builds capacity in machine learning. It's a way to experiment with how we can strengthen our machine learning community, and allow more people to contribute to the conversation.
Data mining is another term that is often confused with machine learning (ML). Here’s an easy explanation of the two terms, as well as the relationship between the two.
With all the possibilities it presents, machine learning is on many a company’s to-do list for 2018. You could determine the optimal time and number to reach prospects and debtors on, improve cross-sell and upsell rates by offering the perfect next product at the perfect time or you can know when to give which customer a discount or special offer to prevent churn. But the costs of implementation of a machine learning tool in just one business area is high.
Machine learning has the power to transform the world, and it’s not just being used to power chatbots or to recommend your Netflix content. There are many amazing machine learning applications that improves our everyday live, but also makes the world a magical place.
The concept of artificial intelligence (AI) has been around for a while, but with the more recent rise of both machine learning (ML) and deep learning, it’s still a buzzword. These three are often used interchangeably, and thought of as the same thing. However, there is a significant difference between the three, which is valuable to know, especially when using the terms in conversation with people you want to impress!
Machine learning is a hot topic. Although we are interested in the inner workings of machine learning and how to improve our models, we are as interested in how to apply machine learning to improve business’ revenue and ROI. The positive impact on ROI through the application of machine learning techniques is well known to us. Based on our experiences, here are the most influential business applications of machine learning. These are the areas where we would recommend you start introducing ML in your consumer-focused business.
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.
Machine Learning is by no means a new thing. Back in 1959, Arthur Samuel’s self-training checkers algorithm had already reached “amateur status” – no mean feat for that period in time. This article is intended to shed some light on the two different types of Machine Learning that one can encounter, which may be useful if you are thinking of entering into this space and are unsure as to which avenue is appropriate for your business.
Predictive Analytics can yield amazing results. The lift that can be achieved by basing future decisions from observed patterns in historical events can far outweigh anything that can be achieved by relying on gut-feel or being guided by anecdotal events. There are numerous examples that demonstrate the possible lift that can be achieved across all possible industries, but a test we did recently in the retail sector showed that applying stable predictive models gave us a five-fold increase in the take-up of the product when compared against a random sample. Let’s face it, there would not be so much focus on Predictive Analytics and in particular Machine Learning if it was not yielding impressive results.
Hands up who has not heard of R? If you are in the data analytics space and have an internet connection then you would have heard of the open source programming language for predictive analytics and statistical computing that has taken the analytics world by storm.
Your customers go through numerous milestones in their journey through your business: the initial interest, the first purchase and opening of an account, (hopefully) paying their accounts on time, maybe signing up for your loyalty programme (and being comfortable to tell you more about themselves).
Few take on a larger portion of the responsibility to steer their organisations to success than Risk Managers. And with fast-moving consumers, a globalised marketplace, unabated industry disruptions and shifts seemingly all occurring in unison, modern Risk Managers face a new set of challenges to that of their predecessors. But now, banks and other financial institutions are using historical customer transactional data to detect unusual activity on buyers' debit and credit cards to freeze transactions until purchases can be verified by the card owners.
Machine learning is a subfield of data science that involves the use of algorithms and computing systems capable of learning on their own from new data as it becomes available, identifying patterns and automatically adapting to predict or anticipate future outcomes with an increasing degree of accuracy. For marketers, what this means is the ability to predict customer behaviour and make relevant and personalised offers on the fly to acquire, retain or grow your most profitable customers.