Risk Modernisation – Leveraging advanced analytics and new age platforms, Europe

AI and data science have transformed credit risk analytics by enhancing accuracy, efficiency, and real-time decision-making capabilities. Modern technology provides a more holistic and dynamic approach to risk management than traditional methods, enabling financial institutions to manage risks better, efficiently comply with regulations, and reduce operational costs.

Join our insightful webinar to unlock the true potential of data analytics backed by artificial intelligence (AI).

Learn from Newgen and EY’s thought leaders and AI experts about the benefits of integrating cutting-edge technology with core systems to revolutionize credit risk management.

Agenda of the Webinar:

• Introduction
• Discussing ways to revolutionize credit risk management
• Demo session
• Q&A and conclusion

What to expect from the webinar?

• The Changing World of Credit Risk Analytics:
  • Overview of traditional vs. modern approaches in credit risk management
  • The importance of advanced analytics in the current financial landscape
  • Key drivers for the adoption of new-age platforms in credit risk analytics
• Key Application Areas of Credit Risk Management
  • Meeting regulatory requirements like Basel III/IV, IFRS 9, and stress testing
  • Enabling real-time risk analytics and decision-making
  • Continuous risk monitoring
• Advanced Analytics Techniques in Credit Risk
  • Deep dive into predictive analytics and machine learning models to discuss how they enhance credit scoring and risk assessment
  • Case studies on the successful application of AI/ML in credit decisions, reducing default rates, and managing portfolio risks
  • The role of big data in credit risk to improve data collection, processing, and analysis
• Exploring New Age Data Analytics Platforms
  • An overview of the Newgen Low-code Data Science platform and the capabilities that make data-driven risk analytics possible
  • Low-code method of data analytics brings all stakeholders, business heads credit policy owners, underwriters, and data scientists together to make data-driven credit decisions
  • Integrated model development, selection, deployment, monitoring, and governance to ensure credit policy compliance
  • Risk model repository and best practices for faster adoption