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Preetika Madan
Preetika Madan

Preetika Madan

Bio

Preetika Madan is an accomplished Executive Director of Advanced Analytics with over a decade of experience in Fortune 500 companies. A double patent holder in fraud and compliance solutions, she specializes in machine learning, predictive analytics, and data-driven strategy to solve complex business challenges. She has successfully transitioned across geographies and industries, building and scaling high-performing analytics teams while driving multi-billion-dollar strategic projects across consumer retail, commercial banking, and corporate cards. Preetika is passionate about leveraging data science for business transformation, bridging the gap between technical solutions and executive decision-making. Recognized for her strong leadership, stakeholder engagement, and data storytelling, she has played a key role in shaping analytics strategies that drive innovation and efficiency.
Beyond her corporate role, she actively mentors aspiring analytics professionals, judges industry competitions, and speaks at conferences, sharing insights on advanced analytics. When she’s not working, she loves spending time with her toddlers and is dedicated to perfecting Kathak, an Indian classical dance form – a passion that keeps her connected to rhythm, tradition, and artistic expression


Driving Growth Through Predictive Analytics: Optimising Product Cross-Sell and Upsell in Commercial Banking

This presentation explores the application of propensity-to-buy models at XYZ bank to enhance cross-sell and up-sell opportunities in commercial banking. By leveraging advanced machine learning algorithms, these models analyse transactional data, customer profiles, and behavioural patterns to predict the likelihood of customers adopting additional financial products. Attendees will gain practical insights into model design, feature engineering, model selection, and implementation, along with case studies highlighting how the bank utilised this innovation for improving customer engagement, increased revenue, and campaign efficiency.  In today’s competitive banking environment, where personalization and data-driven decisions are key to customer retention and revenue growth, such models are increasingly relevant.

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