Steven Finlay is a data scientist with more than 20 years experience of developing practical "value add" machine learning solutions in large scale data environments. He holds a PhD in predictive modelling and is an honorary research fellow at Lancaster University in the UK.
Dr Finlay has previously been employed by one of the UK's top 10 banks to manage their suite of credit risk models, has developed machine learning approaches for the UK government and worked for a number of consultancy groups. He is currently Head of Analytics for Computershare Loan Services (CLS) in the UK.
Steven has published a number of practically focused books about machine learning, artificial intelligence and financial services. His most recent books include:
Finlay, S. (2018). Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Data Driven Technologies. (Third Edition). Relativistic.
Finlay, S. (2017). Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Data Driven Technologies. (Second Edition). Relativistic.
Finlay, S. (2015). Predictive Analytics in 56 Minutes. Relativistic.
Finlay, S. (2014). Predictive Analytics, Data Mining and Big Data. Myths, Misconceptions and Methods. Palgrave Macmillan.
Finlay, S. (2012). Credit Scoring, Response Modeling and Insurance Rating. A Practical Guide to Forecasting Consumer Behavior. Palgrave Macmillan.
Finlay, S. (2010). The Management of Consumer Credit. Theory and Practice. Palgrave Macmillan.
Finlay, S. (2009). Consumer Credit Fundamentals. Palgrave Macmillan.
Steven is very relaxed when it comes to discussing his work and always likes to hear from readers. If you have any questions or comments about his books, or would like to chew that fat a little about Machine Learning or AI, then he would love to hear from you at: firstname.lastname@example.org.
Artificial Intelligence (AI) and Machine Learning are now mainstream business tools. This book delivers a simple and concise introduction for managers and business people. The focus is on practical application and how to work with technical specialists (data scientists) to maximize the benefits of these technologies.