This is Harnham’s weekly news digest, the place to come for a quick breakdown of the week’s top news stories from the world of Data & Analytics.PYMNTS.com: Fighting fraudulent transactions, by the numbersHow are banks using AI and other tools to curb transaction fraud?In 2021, PYMNTS interviewed banking executives to determine how acquiring banks use artificial intelligence (AI) and effective merchant monitoring to combat credit, debit, and prepaid card fraud. In this piece, it shares the results of the interviews:Most acquiring banks say fraudulent transactions increased between 2020 and 202193 percent of those surveyed said they saw a year-to-year increase in fraud. 88 per cent said reducing fraud is critical to their ability to increase or maintain merchant processing revenue.Most banks that use AI use it for fraud detectionAlmost all (98 per cent) of acquirers using AI said it has found fraud detection. 60 per cent have said AI is the best tool for them to detect fraud, while another 15 per cent said it’s an important weapon.Most banks outsource this workFraud detection is too important for some banks to spend years developing their own complex system. So, 92 per cent of banks that use AI systems for fraud prevention and detection said they outsource the systems.To read more about this, click here. Analytics Insight: Top Python machine learning libraries to explore in 2022What Machine Learning libraries should you be focusing on this year? Python is the most popular programming language for data science projects, while machine learning is globally trending. According to Analytics Insight, Python machine learning libraries have become the language for implementing machine learning algorithms. So, to fully understand Data Science and Machine Learning, Python is essential. Here are the top Python machine learning libraries to help you begin your Python journey, and what they’re most useful for:TensorFlow: an open-source numerical computing library for machine learning based on neural networks.PyTorch: used for natural language processing, computer vision, and other similar kinds of tasks.Keras: machine learning toolset that aids companies such as Square, Yelp and Uber.Orage3: includes tools for machine learning, data mining, and data visualisation. Numpy: includes robust computing capabilities within the large, high performance programming communitySciPy: a core tool for accomplishing mathematical, scientific and engineering computations.SciKit-Learn: an indispensable part of the technology stacks of Booking.com, Spotify, OkCupid, and others.Pandas: has powerful data frames and flexible data handling.Matplotlib: replaces the need to use the proprietary MATLAB statistical language. Theano: allows for simultaneous computing, fast execution speed and optimised stability. To read more about this, click here. Analytics India Mag: Why should data engineers learn Scala?Is Scala beneficial to a Data Engineer? Scala combines object-oriented and functional programming in one concise, high-level language, and its static types help avoid bugs in complex applications. Scala does have some key advantages such as its use of data-parallel operations, simple structure suitable for big data processors, and its high-volume capabilities. On the other hand, the article points out why Scala might not be beneficial to a Data Engineer:Difficult to learn Not widely adoptedOnly 10 per cent of jobs require Scala knowledge While Scala does not occupy the same level of importance as other popular languages, it’s certainly a useful language to learn if it matches a data engineer’s career goals. To read more about this, click here. Forbes: Data analytics marathon – why your organisation must focus on the finishIn this Forbes piece, the author compares analytics to a marathon – both take commitment preparedness, and endurance to be successful. A companies’ analytics will go through several cycles as business priorities shift and evolve. They are explained here as milestones of the Data & Analytics marathon:Data collectionData preparationData visualisation Data analysis Insight communicationTake action The author, Brent Dykes, notes that many drop off at the last mile in the race, the action phase where analytics teams perform analysis, share their insights and then implement changes to optimise the business. Most companies have no problem with the start of the data analytics marathon, but many of them aren’t completing the entire race. In order to finish the data analytics race in a strong position, companies and analytics teams must align the data with the business strategy and follow these three steps.Automate early-stage tasksNarrow the scopeFoster a stronger data cultureTo read more about this, click here. We’ve loved seeing all the news from Data & Analytics in the past week, it’s a market full of exciting and dynamic opportunities. To learn more about our work in this space, get in touch with us at info@harnham.com.
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