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Feedzai report parses machine learning for banking

Feedzai, a data science firm specialised in the detection of fraud in omni-channel commerce, has published a new report – Demystifying Machine Learning for Banking – that sets out to do exactly what its title suggests by offering a thorough appraisal of machine learning, which it calls ‘The Key AI Technology.’

That society is currently witnessing a special moment in the world of AI almost goes without saying. Previously, AI was tucked away in the depths of academic research and the R&D bunkers of multibillion-dollar corporations. Now, AI has become more affordable and attainable, signaling a radically transformed landscape that is expected to change the way we live, work, and play. But what is it that makes this moment so unique? In a word: convergence. Certain distinct technological strands have been in the works for more than 15 years; and, now, finally, in this moment, they are coming together in a perfect storm.

In this newly published report, data science specialist Feedzai aims to provide readers with a comprehensive guide to machine learning. And as a company that uses real-time, machine-based learning for analysis of big data to identify fraudulent payment transactions and minimize risk in the financial industry, Feedzai is well equipped to offer this expert guide to machine learning.

This machine learning guide report begins with a brief look into the history of AI, mentioning the key differences between then and now that make this particular moment so exciting. Machine learning, or ‘ The Key AI Technology,’ is then defined as a subset of the broader field of artificial intelligence, which is made up of other related capabilities, for instance: deduction and reasoning systems, robotics and motion, knowledge representation, image and voice recognition, and numerous other niche areas. Of all these tech solutions, machine learning is particularly versatile.

The next section of the report outlines the way machine learning works, describing two main phases of the process, namely: training the system, and scoring real data. The data science loop offers an iterative approach to developing and improving machine learning systems. According to the report, this loop is highly powerful because it combines the analytical prowess of sophisticated algorithms with human insight and judgment. Data scientists can evaluate the performance of algorithms in real time, making adjustments that develop the most accurate model possible.

The final part of the report delves into the application of machine learning for risk management in financial institutions, examining key considerations that risk managers should bear in mind when considering implementing a fraud detection system rooted in machine learning technology. The report concludes by advocating for investment in agile machine learning platforms, stating: “This powerful approach works best when the system presents an open and scalable platform, rather than an inflexible solution. Systems that are agile and react quickly can easily be adapted by in-house staff, and are more likely to offer greater return on investment. Likewise, these systems stand much greater chance of successfully combatting fraudulent adversaries.

To read or download the complete Feedzai white paper Demystifying Machine Learning for Banking, click here..  [*Updated 13-04-2017]

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