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Our news and views relating to Data Analytics, Big Data, Machine Learning, and the world of Credit.

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Our 2017 Top Blogs: Data Science

December 15, 2017 at 3:01 PM

Data science continues to be a hot topic in many large firms globally.  2017 saw data science subjects such as R vs. Python, deep learning, natural language, gamification, AI and machine learning being arguably the most topical.

We continue to be passionate about this rapidly evolving world and as it’s the end of the year we’ve decided to look back at our top data science blogs of the year.  We are running a series of these retrospective assemblies of blogs having previously looked back at our best Machine Learning blogs and thereafter our Credit Risk blogs.

If you missed them during the year, I hope you enjoy them now.

The Four Types of Data Analytics

Simplistically, analytics can be divided into four key categories. In this blog post, we focus on the four types of data analytics we encounter in data science: Descriptive, Diagnostic, Predictive and Prescriptive.

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The Seven Logical Fallacies to Avoid in Data Analytics

“Lies, damned lies and statistics” is the frequently quoted adage attributed to former British Prime Minister Benjamin Disraeli. The manipulation of data to fit a narrative is a very common occurrence from politics, economics to business and beyond.

In this blog post, we'll touch on the more common logical fallacies that can be encountered and should be avoided in data analysis.

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What is Mathematical Optimisation and How Does It Benefit Business?

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.

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Things You Wanted to Know About Mathematical Optimisation, but were Afraid to Ask

Today we explore some of the frequently asked questions around mathematical optimisation.  For the most part the questions are answered in the context of credit risk.  However mathematical optimisation and operations research in general have many applications.

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Thomas Maydon
Thomas Maydon
Thomas Maydon is the Head of Credit Solutions at Principa. With over 13 years of experience in the Southern African, West African and Middle Eastern retail credit markets, Tom has primarily been involved in consulting, analytics, credit bureau and predictive modelling services. He has experience in all aspects of the credit life cycle (in multiple industries) including intelligent prospecting, originations, strategy simulation, affordability analysis, behavioural modelling, pricing analysis, collections processes, and provisions (including Basel II) and profitability calculations.

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Incorporating Credit Lifecycle Predictive Outcomes In Your Collections And Recoveries Call Centre

In a collections environment, an agent needs to follow up with numerous customers on their outstanding credit and the more distinct information the agent has on each customer, the better the agent will understand who they are interacting with and what the opportunities, risks and expectation of the collections call with the client are.

[Slideshare] How To Make Your Business Data Work For You

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. 

5 Must-Join Facebook Pages For Data Science, Machine Learning And Artificial Intelligence In 2019

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.