Banking on the Future


Banking on the Future: Leveraging AI and Machine Learning in Banking

Companies now operate in a world where it is possible and prudent to map and understand vast amounts of customer data.

The processing of data and its subsequent transformation into value-adding analytics is particularly relevant for banks which typically have access to very rich sources of customer data. Consequently, investment in advanced analytics (including Machine Learning) is rapidly becoming the norm rather than the exception.

In this e-Book, we have classified our Machine Learning use cases under three main headings.

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    To make the best possible decisions about your business, it is imperative to base them on a rigorous understanding of the current picture. Machine Learning techniques can be used to reveal hidden insights to help inform and improve strategic decision making.


    Predictive analytics is rapidly becoming the most powerful way to use available data to enable optimal decisions now, based on future predictions. 

    Effective risk management relies on the suitability of underlying risk models. Machine Learning and analytics solutions can not only optimise and streamline the modelling process – thereby saving time and money – but can also help reduce model error. 

    In our e-Book we look at each of these cases in more detail.

    "Pythia is Lux’s proprietary machine learning platform that we use internally to develop optimal Machine Learning algorithms for our clients."