Build predictive models that anticipate the unique needs of business customers by industry, size, and other demographic data. Create products and services that are highly tailored based on predictive patterns built through historical data.

Create more predictive and efficient forecast models for B2B tailored products and services. Boost conversion rates by building offerings that are well suited for unique client needs.

Deepen customer relationships by building more tailored product offerings and by identifying clients’ needs based on profiles and historical information.

Create models that predict at-risk clients by using historical criteria like delinquency, credit downgrades for clients and by leveraging information on basic client demographic data, deposit histories, withdrawals, loan details, etc.

Identify factors that influence delinquency and credit downgrades by analyzing historical transaction data and associated demographic data. Detect risk quickly and build criteria for high-risk sources.

Reduce defaults due to credit risk or changes in credit profiles. Identify at-risk clients early and intervene to minimize defaults.

Forecast cash demand for all your ATM locations by leveraging historical demand data, location information, calendar, and high-impact events like paydays. Prevent ATM shutdowns due to insufficient funds while reducing capital costs associated with excessive funding.

Achieve greater levels of operational efficiency by predicting levels of currency needed at individual locations through historical analysis and by building daily demand models on a month-to-month basis.

Lower ATM outages by preventing cashout situations and lower operating costs by lowering on-hand capital necessary and by adjusting to demand and seasonality.

Understanding what products you need, when you need them and what markets you should prioritize is a critical part of running a modern bank.

Leverage historical sales data along with customer demographics to build predictive models of future product demand to optimize your product portfolio based on ideal customer profiles.

Forecast interest rate changes, product demand, loan types and understand cash-flow requirements at both the branch as well as organizational level.

Leverage basic employee attributes as well as historical data derived from aptitude testing, attendance, performance evaluations, training history, etc. to build KPI-driven predictive models of employee performance.

Identify characteristics of high-performing as well as at-risk employees based on historical and demographic data. Create models by employee type that can then be aligned with actual performance on an ongoing basis.

Improve employee performance by focusing on key performance indicators that boost performance and morale. Lower attrition by minimizing at-risk patterns that lead to employee exits.