Deposit Analytics

Depositor behaviors are inherently volatile. Changes in economic conditions, pricing strategy, competitive market dynamics, laws & regulations, technology and consumer preferences can all impact an institution’s deposit base. Despite this, most institutions have a limited understanding of their depositors, hindering their ability to predict future behavior. The view deposit gatherers and product managers have on relationships and/or products often do not align with that of risk or profitability. This will undoubtedly lead to misaligned incentives. It can also lead to difficult to explain fluctuations in key risk measures, like economic value of equity. In all, it makes understanding risk and return much more difficult and could result in incorrect assertions about an institution’s projected net interest income.

Empirically-based behavioral modeling

A selection of risk-aware methodologies for estimating non-maturity deposit rate sensitivity (betas), balance decay and volatility (stable vs non-stable) behaviors, that: align with new IRBB guidance and US regulatory standards, produce conservative and defensible assumptions, and bring stability to risk measures by creating a stable balance with a buffer for volatility.

A consistent view of risk and profitability

Integrated with your ALM modeling process to create a standardization in data, product segmentation, assumptions and calculations that will foster a consistent dialogue across the institution on the value (risk vs reward) of your products, depositors and overall portfolio.

Early warning system

A continuous back-test of predicted behaviors against actual behaviors presented through dashboards that illustrate deviations from expectations resulting from possible misaligned incentives, changing market dynamics or changing customer behaviors.