Use of Machine Learning for Personalisation in Retail Banking

Synopsis

There is a new wave of innovation in the banking sector to move towards personalization of banking services. This is accelerated by Fintech companies offering competitive solutions in lending, insurance, collections, personal finance and payment options. Internet companies have been masters of personalisation and localisation of services e.g. internet search results, movie recommendations, restaurant recommendations. Personalisation is set to make retail banking more consumer friendly and change the way consumer interact with banks. In this session, we will discuss some interesting use cases, a recap of machine learning and analytics methodologies powering personalisation and why it is relevant in the mobile context.

About the Speaker

Raghav looks after data science and analytics for Grab’s lending business. He specialises in credit risk analytics from traditional and alternative data sources, machine learning and advanced analytics for a large spectrum of use cases including fraud, marketing, text analytics and IoT. Before joining Grab a little more than a year back, to set up the data science function for its lending business, Raghav was working at Experian Singapore where he was instrumental in setting up the machine learning and Innovation Lab for APAC businesses. He has 13 years of experience in analytics and machine learning across a diverse set of use cases while specialising in retail banking and risk management.

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