The potential of artificial intelligence and machine learning (ML) to deliver value to financial institutions has created something of a gold rush in adopting this methodology for applications. More specifically, organizations are turning to ML models as an alternative to traditional models to gain faster, more accurate and insightful predictions and classifications in their risk management and financial management business decisions. Because they are more complex and less transparent than traditional models, ML models pose a unique set of challenges to model risk management and model validation. Join our webinar to learn about the challenges and benefits of incorporating machine learning models into your risk management program.
- Discuss practical applications of machine learning models at financial services institutions
- Define the unique set of challenges associated with validating machine learning models
- Develop customized methods for validating ML models within your institution based on industry best practices
Suresh Baral, Managing Director
Suresh is a Managing Director in Protiviti’s Data Management and Advanced Analytics solution. He has 20+ years of experience in leading model development and model validation; economic capital modeling, stress testing, loss forecasting, managing credit risk of consumer loan portfolios, and improving business performance through use of machine learning, predictive / advanced analytics. Prior to joining Protiviti, Suresh was a Director at PwC in their model validation and model risk management practice. He was also the head of portfolio credit risk modeling and analysis team at Bank of America Home Loans; head of risk department at 1st Financial Bank USA; and the head of modeling and quantitative analysis at HSBC subprime credit cards (formerly Household International).
Parham Ghorbanian, Senior Manager
Parham Ghorbanian is a Senior Manager in the Model Risk practice within Data Management and Advanced Analytics solution at Protiviti focused on model development, model validation, CCAR Stress testing and model audit support services. Parham holds a PhD in stochastic and dynamical system engineering from Villanova University in Villanova, Pennsylvania. He got his Masters in engineering in the field of System Dynamics and Controls also from Villanova University.
Romeet Chhabra, Manager
Romeet Chhabra is a Manager in Protiviti’s Risk and Compliance practice focused on model development, model validation and Market Risk. Romeet holds a Master’s degree in Financial Engineering from University of Illinois and a Post Graduate Diploma in Management from Management Development Institute. Prior to Protiviti, Romeet was a Quantitative Analyst with Intercontinental Exchange. Romeet has over eight years of experience in varied functions, including strategic initiatives, model design and validation, quantitative analysis and, software design and development.
Original webinar date
Wednesday, July 31, 2019