Many financial institutions struggle with constring accurate forecasting models incorperating fee and expense information against sales projections, in the context of dynamic global events. Delta Data’s newly released IntelliCaster is a powerful soluton that brings your firm’s macro-economic data to life against real world models.
Delta Data’s new institutional profit and expense forecasting tool, IntelliCaster, tracks multiple economic models that enable asset manager distributors to forecast profits and expenses based on a variety of critical influencing factors, including sales forecasts and dynamically calculated fee data. IntelliCaster provides financial models that will aid in more realistic forecasting. No one has a crystal ball, however, with the major economic variables covered, any asset manager will be afforded a tighter picture of what profits against expenses should look like.
Delta Data’s IntelliCaster provides asset managers with a platform to input multiple economists generated macro-economic based models. The models are then used as the basis for scenarios that include adjustments from sales by region, dealer or product, and by finance that adjusts by share class, fee waivers and other proprietary inputs from that specific asset manager. Once the data has been input into the system the IntelliCaster yields forward looking composite analysis of projected distribution fees, expenses, and profit. Fee agreements input into the system provides a monthly expense calculation that is then applied against the future AUM models.
Analytic dashboards and reports based on the performance by scenario against actual results provide additional insight. Multiple models can be continually analyzed in an automated fashion with the impact of external factors highlighted, to provide a more realist profit forecasting ability covering a span of months. The solution delivers multiple profit and expense models per product, channel, and platform. The delta between the models indicates a more realistic forecast of performance against predictions.