Our news and views relating to Data Analytics, Big Data, Machine Learning, and the world of Credit.
In a previous blog, we looked at assessing your credit models and the challenge of building and deploying models representative of the COVID-19 crisis. At the crux of the challenge was the fact that:
New technology gives the promise of greater enablement. But some of the shrewdest entrepreneurs understand that opportunity comes from the unintended consequences of new technology. So, let us take digitalisation of the loan application process: the opening of digital channels has enabled lenders to service their customers 24/7 and through APIs integrate with a host of sophisticated services. However, the advent of the digital channel has meant more opportunity for fraud. The question to ask is:
Organisations and individuals, need to adapt and change to the new ways of working to ensure that we survive this pandemic, and protect our sustainability for the future.
In 2011, Microsoft founder, Bill Gates, reflected on the many strides researchers have made in terms of treating diseases such as HIV, malaria and polio. Gates warned us, “There is one area, though, where the world isn’t making much progress, and that’s pandemic preparedness. This should concern us all because if history has taught us anything, it’s that there will be another deadly global pandemic”.
This year, we wrote about machine learning (ML) and artificial intelligence (AI) mostly in the context of chatbots. That’s because we have a brand new relationship with NML as an Atura, AI Chatbot for Financial Services reseller. You can read more about our partnership, here. We’ve also written a very interesting blog regarding machine learning in medicine, which you shouldn’t miss, so we’ve included it in this roundup.
The call centre world, unsurprisingly, ranks as one of the highest adopters of data analytics platforms year on year. This is largely due to the invaluable insights we gain through the analysis of thousands of calls received each day by the typical call centre. With speed being of the essence in making the right decision at the right time for each caller many call centres are turning to machine learning to automate their data analysis and make crucial customer experience decisions within seconds.
At Principa, we are passionate about new ideas and product development. 20% of our revenue is ploughed back into innovation. One of our key areas of focus is machine learning where we have built machine learning solutions in both collections and the customer acquisition space. While our focus is primarily on credit risk and customer engagement, we are always interested in how machine learning has gained traction in other industries.
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’ve recently partnered with Atura to bring a world-class chatbot to the credit and collections industry – and we’re very excited! Your business is likely already considering a chatbot, and if not: you’ve likely got it on your agenda for this year. We’ve written recently about some powerful statistics on chatbots that you need to know, but in this article, we’ll detail some of the benefits of chatbots and why they are so important for your business in 2019.
If you haven’t yet heard, we’re in the chatbot game! We’ve recently announced our partnership with Atura and we’re very excited to bring the Atura chatbot into the credit and collections world.
Common barriers to success: Skills shortage: data scientists are in high demand and in low supply. Companies lack the skills to develop advanced data analytics or machine learning applications. Cost: recruiting and building up or training a team, as well as infrastructure costs are immense. Inefficiency and low ROI on: acquisition campaigns; re-activation and retention campaigns; outbound sales calls and debt collection. Resulting in: No or ineffective use of data. High cost to get insights from data. Low returns from campaigns. What’s the alternative? Machine Learning as a Service (MLaaS): removes infrastructure skills and requirements for machine learning, allowing you to begin benefiting from machine learning quickly with little investment. Subscription based pricing, allowing you to benefit using machine learning while minimising your set-up costs and seeing returns sooner. Answers as a Service: Use historic data and machine learning to allow answers to increase in accuracy with time. MLaaS with predictive models pre-developed to answers specific questions: Genius Call Connect: What is the best time and number to call customers? Genius Customer Growth: Which customers are most likely to respond to cross-sell? Genius Re-activation: Which dormant customers are worth re-activating? Genius Customer Retention: Which customers are most likely to churn? Genius Leads: Which contacts are likely to respond to my campaign? Genius Risk Classifier: Which debtors are most likely to pay or roll? Benefits of Genius: Quick and cost-effective ability to leverage machine learning: Minimal set-up time Minimal involvement from IT Subscription based service Looking to make your data work for your business? Read more on Genius to see how it can help your business succeed.
While LinkedIn has traditionally been thought of as the business or work focussed social platform, Facebook has been making headway into gaining market share in the space as well. With company pages and groups, Facebook is catering to every interest and aspiration that people might have – and combining that with their social interactions and news sources. Facebook aims to give users a one-stop-shop experience, and it’s very good at doing it.
It isn't always easy to keep up-to-date with the latest news in data science, machine learning or artificial intelligence. Twitter is a great source of information and helps you quickly scan through headlines to engage with content that interests you. This helps eliminate a lot of noise and helps you focus on what you want to read about, but you need to follow the right people for that content to appear in your feed.
Meetup (www.meetup.com) was founded in 2002 and is an online service used to organise groups that host in-person events for people with similar interests. A social networking tool, unlike any other, Meetup, has more than 35 million users in 180 countries. In South Africa, there are many meetups organised for various interests. Of course, our interests span data science, machine learning and AI, so we took the time to put together a (non-comprehensive) list of the groups who organise meetups that we (and hopefully you) find of interest. We hope you make it out to one of these groups meetups soon!
We've been sharing our favourite machine learning books, podcasts and courses in the recent weeks, but we felt there was a glaring gap in the lists of resources we've been sharing: videos! Why did we think this is a noticeable gap? Because of these impressive statistics:
We’ve compiled this list of 10 people you should take note of in the machine learning field to keep yourself updated and informed about the field. It's also useful if you're interested in learning about machine learning, as these are the people who not only influence the industry but are paving the way forward and shaping the future of machine learning. We've given a short overview and a video to familiarise yourself with each of them, but if you're invested in learning about machine learning, follow them on social and start reading their publications.
Machine learning and artificial intelligence are exciting fields, and we've been writing about these topics for a couple of years now. While a lot of what we talk about on our blog is advanced implementations of machine learning and can be overwhelming to beginners, the core concepts of machine learning are actually pretty easy to grasp. There are many resources and cheat sheets available online, but we believe the old fashioned way of learning is sometimes the best: with a good book. Few resources can match the in-depth, comprehensive detail of a good book.
Our data scientists are keen readers and avid podcast listeners. In this blog, we list a few of the podcasts that cover topics such as data science, machine learning and artificial intelligence, and that we’d recommend if you’re looking to start exploring the world of podcasts.
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.
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've written many blog posts on the topic of Machine Learning and how it's improving everything from fraud prevention, direct marketing in retail and the customer experience in call centres to getting us to make more impulse purchases online and making holiday and business travel more enjoyable. It's with good reason we've given Machine Learning so much focus on our blog: it is a driving force in what the founder of the World Economic Forum - Klaus Schwab - is calling the Fourth Industrial Revolution. In this blog post we answer some frequently asked questions about Machine Learning, starting with what it is and how it relates to Artificial Intelligence.
At Principa we’ve become quite passionate about Artificial Intelligence and Machine Learning. Recently quite a bit has been published in the press about how automated machines should be allowed to get. Most famously perhaps there have been the warnings from the likes of South African born Elon Musk and theoretical physicist Professor Stephen Hawking.
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?
Here's a blog post covering some of the most frequently asked questions we get on Machine Learning and Artificial Intelligence, or Cognitive Computing. We start off with "What is Machine Learning?" and finish off by addressing some of the fears and misconceptions of Artificial Intelligence. So, what is machine learning? A simple search on Google for the answer will yield many definitions for it that leave most non-analytical people confused and entering more "What is..." statements into Google. So, I asked our Head of Marketing to try his hand at defining Machine Learning in the most simplistic way he can: explain Machine Learning to someone you've just met at a social gathering. Here's his definition - a "Machine Learning for Beginners' " definition if you will.
Seems like the term Machine Learning is popping up in mainstream media as the next big thing. The fact is, however, that Machine Learning went mainstream a long time ago. You don’t think so? Check your mobile phone. Chances are you’ve been using and benefiting from Machine Learning algorithms all this time without even knowing it. Read our opinion piece: Machine Learning Is Here To Stay In this blog post, I go through some of the many apps on your mobile phone that use Machine Learning algorithms to make recommendations, get you to your destination quickly and safely, improve your photos, tell you what song you’re listening to and more. You’ll see, Machine Learning is not so far away. It’s already in the palm of your hand. If you want to know what machine learning is, read our blog post on What is Machine Learning? And Other FAQs We Get...
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.
McDonalds mastered the upsell with one simple question at the time of purchase: “You want fries with that?”. A simple and relevant question at the right time that has likely generated millions of extra dollars in revenue through the years for the company. Ever since then, companies have tried to emulate their success by identifying complementary products in their offering and training sales staff to ask customers the right question at the right time.
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.
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).
Machine learning is revolutionising how companies are capitalising on Big Data to develop their marketing strategies. While the term encompasses a broad spectrum of technologies and approaches, in a marketing context it can be used to improve targeting, response rates and overall marketing ROI. To put it simply, machine learning involves the automated analysis of large volumes of data – such as consumer spending habits and purchasing behaviour, as well as demographic information – and using a mathematical algorithm and a computer to identify patterns and trends. The algorithm then tests predictions based on historical campaign data and learns from the predictions it gets right. With time, these algorithms become highly accurate as more data from campaign results is added.
Thanks to mobile technology, wearable devices, social media and the general pervasiveness of the internet, an abundance of new customer information is now available to marketers. This data, if leveraged optimally, can create opportunities for companies to better align their products and services to the fluctuating needs of a demanding market space.
Artificial intelligence (AI) and machine learning technology present an interesting meeting point between our fear of the unknown and our fear of being known (that is, fear of our private information being exposed and known by others). The erosion of privacy and rise of intelligent machines is actually a common theme in science fiction. But while reality still has a lot of catching up to do before we can call Skynet’s customer support or play cards with Agent Smith, many people have expressed genuine concern over the implications of modern technology – especially regarding their privacy.
Machine learning is helping brands narrow the divide between their products and consumers in ways that would appear almost magical only ten years ago. From Amazon's personal product recommendations based on past purchases and browsing habits, to Netflix's uncanny ability to suggest just the right movie title according to your taste in film, data-driven insights are helping companies speak to individual customer preferences, who are demanding more personalisation in their products and engagements. This has moved data analytics from novelty status to an integral part of the marketing strategy, as brands discover new opportunities to communicate their unique selling points.
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.
It’s been almost 15 years since we saw the future of crime prevention in “Minority Report” – but today, we are beginning to see those then fictitious yet fantastical methods of predicting and preventing crime being implemented in various parts of the world. I’ll briefly mention three examples below of how analytics is already being used to prevent crime today before going into more detail on a fourth example: using analytics to prevent a criminal from re-offending.
When you think of machine learning in action, it’s easy to imagine analytics-based marketing research or a matrix-style AI takeover. But there are actually far more grounded and practical applications that benefit hundreds of millions of people in their day-to-day leisure and business activities. Take the travel industry, for example. We’ve now reached a point in human history where over 100,000 flights are taking off every single day. With airports seeing more foot traffic than ever before – Dubai International alone saw over 78 million passengers in 2015 – they need to be run and managed like a carefully oiled machine. Alleviating bottlenecks is central to ensuring as smooth an operation as possible in the travelling experience.
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.