This video is part of the Deep Learning in Finance Summit, London, 2018 Event. If you would like to access all of the videos please click here.

Interpreting Machine Learning Models

Big data and machine learning are becoming central parts of the business for many organisations in the public and private sectors. This is driven by the continuous growth and availability of data that empowers the organisations to make better data driven decisions. To support the business and the users of the machine learning models, it is key to explain why the algorithm made a certain decision. This way machine and people can work together to tackle important issues like Fraud. In addition, with the imminent coming of the new regulation on data protection, today model interpretability is becoming more important than ever. Many machine learning algorithms like gradient boosted trees and deep learning have been traditionally called ‘black-box’ algorithms, because it seems unclear the process they undertake to make a certain decision. In particular, deep learning involves feeding information through non-linear neural networks that classify data based on the outputs from previous layers, making it very difficult to understand the reasons for the decisions made. In this talk, I will discuss different implementations that allow us to understand why an algorithm makes a certain decision at an observation level. I will show how we can use these tools in public datasets and then describe how we use them in insurance.

Soledad Galli, Lead Data Scientist at LV=

Soledad is a Lead Data Scientist at LV=, with 2+ years of experience in data science and analytics in the financial sector, and 10+ years of experience in scientific research in academia. She is passionate about extracting meaningful information from data and supporting institutions make solid and reliable data driven decisions. At LV=, Soledad and the data science team are leading the implementation of machine learning across the multiple company business areas. Having transitioned from academia to data science, Soledad is passionate about enabling and facilitating data scientists and academics transition into the field, and helping data scientists increase their breath of knowledge. During the last year, Soledad shared insight in blogs and talks in the data science community. She also created 2 online courses on machine learning now live in Udemy, which have enrolled 350+ students from several parts of the world in just under 3 months.