Driving Client Profitability Insights with AI Powered Calculators

At KlariVis, where our platform transforms core banking data into actionable enterprise insights, I led the development and delivery of a client-facing profitability calculator. This tool empowered mid-sized banks to evaluate the true economic value of relationships by factoring in required effort, revenue streams, costs, and risk-adjusted returns.

The project required strong ownership from ideation to production. I directed a cross-functional team—including Product, BI, DevOps, and QA—to integrate machine learning models that dynamically calculated profitability at the account, customer, product, and household levels. Using Python for model logic and SQL for relational schemas, we built secure data pipelines that pulled from core systems while enforcing access controls for compliance.

A key challenge was ensuring the calculator reflected real-world effort: we incorporated workload proxies (e.g., transaction volume, servicing touchpoints) into the models to avoid over- or under-valuing high-maintenance relationships. I applied MLOps best practices—versioning models, implementing automated testing, and setting up observability dashboards—to monitor accuracy and performance drift post-deployment.

Mentoring played a central role: I guided engineers on bias detection in profitability scoring and collaborated with stakeholders to refine UI integrations for intuitive dashboards. The result was a feature that helped clients identify unprofitable segments, optimize pricing, and prioritize high-value relationships—driving measurable improvements in net interest margins and resource allocation for our bank users.

This initiative underscored how generative AI and ML, when governed properly, turn complex data into strategic tools that directly impact bottom-line performance in banking.