Cost-effective, granular data without privacy encroachment.
Up To Now: Small financial institutions need to grow but are stifled by the lack of data within their locale at their scale. With no data about their prospective customers, they are limited in their ability to produce a business strategy focused on growth. Syndicated solutions focus on larger markets while also sometimes lacking the specific details needed for unique situations.
The Solution: Smaller footprint organizations like credit unions and insurance mutuals are increasingly turning to population simulators built from synthetic data to fuel their strategic and tactical planning. Population simulators are growing in use and importance as a solution for credit unions and other financial institutions because (i) they are highly cost-effective, (ii) they are built on top of rich and trusted primary data sources (iii) the synthetic data can be deeply analyzed in a privacy compliant way and (iv) the comprehensive nature of the data allows for analysis and innovation at both the strategic, tactical, member and market levels. Some of the proven small organization use cases are for new market profiling, member and market segmentation, cross-category understanding to create partner ecosystems, geospatial and mobility analysis, and online offline distribution strategy. Other organizations including not-for-profit and government agencies use synthetic data population simulators for applications such as financial stress testing, new product innovation, market mix modelling, financial forecasting and advertising creative testing.
What Is a Population Simulator?
While there are numerous sources and definitions of population simulators and synthetic data, the population simulator that you choose must be created using an academically published method, and be created from trusted public sources . Once merged with clients’ first-party data, organizations are discovering a privacy-by-design data asset with unparalleled potential.
Seeing Is Believing.
Learn More: One of the common criticisms of synthetic data sources and population simulators is the opaqueness of the methodology used to create them. You need to know the construction before you can trust the data.
Contact Arima to learn more in as much detail as you need to feel comfortable and to get your questions answered.