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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 17 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.

Latest Posts

Solving the Credit Unaware Challenge with Psychometrics

At Principa, we engage with clients and organisations across the entire credit lifecycle and track the focus of the South African credit industry. For nearly ten years the focus has consistently been in the collection space, but recently (since early 2021) this has changed and a large number of our clients are focused on acquisitions and originations.

Predicting Customer Behaviour (PART 2)

In Part One of this two-part blog, we started providing a short overview of just some of the propensity models that Principa has developed. In this Part Two, we continue to look at different types of propensity models available across the customer engagement lifecycle that are used to predict behaviour and solve business problems. 

PART 2: How to Cure the Post Pandemic “Collections” Symptoms

In PART 1 of this two-part series, we explored how the current socio-economic climate resulting from the lingering financial hangover caused by the pandemic is negatively impacting the consumer's ability to settle a debt.