Naeem Siddiqi
Senior Advisor, Risk and Quantitative Research

Naeem Siddiqi is the author of Credit Risk Scorecards : Developing and Implementing Intelligent Credit Scoring, (Wiley and Sons, New York, 2005), Intelligent Credit Scoring: Building and Implementing Better Credit Risk Scorecards (Wiley and Sons, 2017), as well as various papers on credit risk topics. 

Naeem meets with senior executives and decision makers from between 40-50 lenders globally each year, and provides strategic advice to them on areas such as the development and validation of credit scoring models, climate change risk, infrastructure planning for analytics, and retail credit risk strategy. He has also trained hundreds of bankers in over 25 countries on the art and science of credit scorecard development, and helps credit risk analysts develop better scorecards. 

Naeem has an Honours Bachelor of Engineering from Imperial College of Science, Technology and Medicine at the University of London, and an MBA from the Schulich School of Business at York University in Toronto. 

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Recent Publications: 

Intelligent Credit Scoring: Building and Implementing Better Credit Risk Scorecards (Wiley and SAS Business Series) 2nd Edition
Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring

Economic Turbulence Ahead : A Guide for credit risk managers
Impact of Climate Change on Credit Scoring
Will Automation Reduce Modeling Skills
Black Box Models : A Governance Approach

Calculating Credit Risk : the COVID-19 Factor
What COVID-19 can teach companies about Climate Risk

Naeem Siddiqi on ML for credit scoring
Sea Change – Driving awareness to confront climate risk

Speaker engagements

2020Economic Downturn: How Credit Risk Managers Can Prepare for Better OutcomesOn-Demand WebinarSAS
2020Credit Risk Modeling : How to Prepare for the New NormalOn-Demand WebinarSAS
2019How to use altrenative Data for credit assessmentCairo, EgyptSAS
2019AI/ML: Lessons from Global ExperienceBangkokSAS Customer Connection
2019Getting the most from Alternative DataBeijingSAS Customer Connection
2019Keynote: Making the most of AI and Alternative DataHo Chi Minh City (Vietnam)SAS
2019AI/ML and the use of alternative data for lendingSao PaoloFebraban (Brazilian Banking association)
2019Artificial Intelligence and Credit RiskMontrealPRMIA
2019Machine Learning and Alternative Data: Myths vs RealityKenyaCredit Information Sharing Conference
2018Machine Learning and AI in Credit Scoring: Global PerspectivesCanadaCredit Scoring and Risk Strategy Association conference
2018Machine Learning and Alternative Data: Lessons from Global ExperienceMalaysiaCRO Forum Malaysia