Nick Sime
Director of Fraud & Credit Risk Modelling
Nick, with 30+ years of experience in the industry, is one of the UK’s leading risk strategy experts.
Nick’s expertise is in predictive modelling and has presided over many scorecard developments, primarily in the areas of credit and fraud risk, and has also delivered multiple credit strategy reviews.
More recently, Nick efforts have focused on exploiting the power Archetype, our AI-based modelling tool, delivering consistent and impressive benefits over traditional models.
With over 20 years of service at Jaywing, Nick has worked with many clients including Secure Trust Bank, Virgin Money, and Chetwood.
Prior to this, he worked at first direct and HSBC.
Combined vs bespoke models in credit risk: Does segmentation still add value?
Do segmented models improve credit risk performance? New analysis shows why combined ML models consistently outperform segmented approaches.
Sample size and model choice: When GBMs outperform DNNs in credit risk
When do GBMs outperform DNNs in credit risk modelling? New research shows how sample size and number of defaults influence machine learning model performance.
Machine learning model stability: Do Gradient Boosting Machines (GBMs) and Deep Neural Networks (DNNs) really degrade faster?
Machine learning models often outperform early, but what happens after go-live? We look at long-term performance of GBMs and DNNs using multi-year credit data.
15 minutes with… a placement analyst bringing an engineering mindset to credit risk
A conversation with a placement analyst on data, modelling and the skills shaping the next generation of credit risk analysts.
15 minutes with… a placement analyst building experience across credit risk modelling
A conversation with placement student on data, modelling and the skills shaping her early career in credit risk at Jaywing.
14 lessons in predictive modelling to strengthen credit risk assessments
Read 14 lessons in predictive modelling to boost credit risk assessments, improve stability, and gain a competitive edge with advanced machine learning insights
AI vs traditional risk modelling: A comparative analysis
Explore how AI is transforming risk management. Compare traditional vs AI-based models and learn key implementation strategies for financial institutions.
Risk Model Development – The Next Generation
With the use of AI/ML on the rise in the world of risk management, Nick Sime, Jaywing's Director of Fraud and Credit Risk Modelling, explores the key differences between the development of traditional models and these new data-driven powerhouses.
What's next in fraud?
As the end of 2023 approaches, experts Nick Sime and Ben O’Brien share their predictive lens, offering insights into the future landscape of fraud and its detection.