Nene Azu
About Nene Azu
Nene Azu is a Product Manager specializing in AI and machine learning within global lending and credit risk. With a background in Economics, Mathematics, and Applied Statistics from The University of British Columbia, Azu has extensive experience in applying machine learning to credit risk management, particularly in retail credit models.
Work at Borealis AI
Nene Azu currently serves as a Product Manager specializing in AI and machine learning within the Global Lending and Credit Risk division at Borealis AI. Since joining in 2020, Azu has focused on developing and implementing advanced machine learning models tailored for the financial sector. This role involves overseeing projects that enhance credit risk management strategies through innovative technology solutions.
Education and Expertise
Nene Azu holds a Bachelor of Science degree from The University of British Columbia, where the focus was on Economics, Mathematics, and Applied Statistics. This educational background provides a strong foundation for Azu's expertise in applying machine learning techniques to credit risk management, particularly in the development of retail credit models.
Background in Commercial Banking
Before joining Borealis AI, Nene Azu worked as an Analyst in Commercial Banking at Canadian Western Bank from 2017 to 2019. In this role, Azu contributed to the mid-market segment, gaining valuable insights into the banking industry and enhancing analytical skills that would later support machine learning initiatives in credit risk.
Experience at JUDI.AI
Nene Azu was previously a Product Manager at JUDI.AI from 2019 to 2020. During this nine-month tenure in Vancouver, British Columbia, Azu focused on leveraging machine learning to improve product offerings, gaining experience that would be instrumental in future roles within the financial technology sector.
Achievements in Machine Learning Applications
Azu has successfully expanded machine learning applications across multiple use cases in the retail lending ecosystem. Notably, Azu implemented RBC's first applied machine learning retail credit model, which resulted in a financial impact exceeding $40 million. This achievement underscores Azu's capability in driving significant advancements in credit risk management through technology.