Links for ‘Banking On Machine Learning’ talk

I gave a talk to Microsoft Student Partners about incubating machine learning into a large organisation, in particular into a big bank.  The slide deck is here.  I mentioned several events and articles during the talk.  Here are the links.

Rohan Kopparapu (4th year UCL) and Microsoft Student Partner  wrote a blog post about the UCL Data Science Student Challenges.

The video recordings of the two recent talks  about building a data-driven culture in an organisation are  Transforming a Museum to be data-driven using R – Alice Daish and
From nothing to something, a local government journey- John Kelly.

The podcast of the BBC Radio 4 interview with Anthony Jenkins, ex-CEO of Barclays, and others about how fintech and ML are disrupting banking is available here.

I suggested some London-based meetups
https://www.meetup.com/London-Business-Analytics-Group/ (LBAG, my one)
https://www.meetup.com/London-PUG/
https://www.meetup.com/data-visual-London/
https://www.meetup.com/rladies-london/

The Economist article on the change of culture at Microsoft and its focus on cloud and AI is here.

 Dr Andy Pardoe runs a comprehensive list of AI and ML resources at homeAI.info as well as a personal site.  He has been named a Top 30 AI influencer recently.

The abstract for the talk is below.

Banking On Machine Learning

Many of the audience will have recently attended a hackathon at UCL. Over a weekend participants applied Azure Machine Learning to financial datasets supplied by Bloomberg and derived some insightful results – a fantastic achievement. This was for many their first taste of a hackathon and data science.  However, for many organisations, getting to the stage where they are doing hard data science as part of a viable project is a long way into the journey of introducing and successfully taking advantage of ML.

This talk looks at the experience of starting ML at a few organisations then focuses on the challenges faced by big diversified banks.  We look at why banks are rushing to embrace ML and consider both the  opportunities and threats that ML poses.  We’ll consider the steps to introduce and incubate ML within the bank; through awareness, education, crowdsourcing, mobilisation (including hackathons) and finally implementation of ML as the new normal.

We’ll conclude that the successful introduction of ML into an organisation is about communication, culture, change, marketing, business analysis… and finally the hard data science of the sort that we saw in the hackathon.