Leading Predictive Deposit Forecasting and Household Analytics for Sustainable Growth

In the competitive landscape of deposit growth, I took ownership of an internal AI project at KlariVis to enhance deposit forecasting and householding capabilities. Banks needed forward-looking insights to prevent runoff, deepen relationships, and build resilient funding strategies—especially amid rate volatility.

I led the team in integrating predictive ML models into the platform, forecasting deposit balances and migration risks at the account and household levels. We aggregated core data with behavioral signals, using Python pipelines and SQL schemas to create household views that grouped related accounts for a holistic customer perspective. This enabled more accurate predictions of churn likelihood, renewal probabilities, and growth potential.

To ensure reliability, I enforced MLOps standards: model versioning, continuous integration/testing, and real-time monitoring for drift. We incorporated generative AI elements for automated scenario explanations (e.g., "This household is at 65% risk of outflow due to maturing CDs and competitive rates"), making outputs actionable for branch teams.

Cross-functional leadership was essential—I aligned Product on feature prioritization, mentored peers on data governance, and worked with DevOps to maintain sub-second query performance. The outcome? Clients reported stronger deposit retention strategies, with early indicators of reduced outflow through proactive outreach and targeted incentives.

This project highlighted the power of applied AI in payments and analytics: by combining forecasting with household intelligence, we helped banks move from reactive volume chasing to proactive, relationship-driven growth—delivering sustainable competitive advantage.