ML

  How ML is Changing Financial Services

In brief:  ML is progressively integrating into our everyday and changing how we live and make decisions. As ML analyses historical data and behaviours to predict patterns and make decisions, it has proved hugely successful in retail for its ability to tailor products and services to customers - retail banking and ML are also a perfect combination. Thanks to ML functions such as fraud detection and credit scoring are now automated. Banks also leverage ML and predictive analytics to offer their customers much more personalised user experiences, recommend new products, and animate chatbots that help with routine transactions such as account checking and paying bills. ML is also disrupting the insurance sector. As more connected devices provide deeper insights into customer behaviours, insurers are able to set premiums and make payout decisions based on data. Insurtech firms are shaking things up by harnessing new technologies to develop enhanced solutions for customers. 

Why this is important: The potential for change is huge and, according to management consultancy McKinsey, “the [insurance] industry is on the verge of a seismic, tech-driven shift.” Few industries have as much historical and structured data as the financial services industry, making it the perfect playing field for ML technologies.

  Google Scientists Test Self-Evolving AI

In brief:  Computer scientists working for a high-tech division of Google are testing how ML algorithms can be created from scratch, and then evolve naturally, based on simple math. Experts behind Google's AutoML suite of AI tools have now showcased fresh research which suggests the existing software could potentially be updated to "automatically discover" completely unknown algorithms while also reducing human bias during the data input process. According to ScienceMag, the software, known as AutoML-Zero, resembles the process of evolution, with code improving every generation with little human interaction.ML tools are "trained" to find patterns in vast amounts of data while automating such processes and constantly being refined based on past experience. But researchers say this comes with drawbacks that AutoML-Zero aims to fix. Namely, the introduction of bias. The analysis is titled ‘Evolving Machine Learning Algorithms From Scratch’ and is credited to a team working for Google Brain division.

Why this is important: There is a sense amongst many members of the community that the most impressive feats of AI will only be achieved with the invention of new algorithms that are fundamentally different from those that we have so far devised. This paper presents a method by which we can automatically construct and test completely novel ML algorithms