Having limited visibility to the transactions made on individual cards, issuers are seeking smart ways to better understand their clientele, and the name of the game is personalization. Whether individualized membership rewards, context sensitive purchase recommendations, recognizing that a specific card holder is an influencer of a group, or by alerting on fraud based on spending patterns, issuers can significantly improve customer experience. To do so, issuers seek smart tools that will use all available sources of information, and expose non-obvious facts.
ELEMENT™ harnesses the power of big data, AI and contextual analytics to understand individual transaction, and the entities behind them. It carries this internal information with high credential lists as well as open sources, to understand the drivers of purchasing.
Sample use cases that ELEMENT™ supports includes:
- Clustering card holders based on known parameters to understand the value of membership programs
- Correlating billions of transactions based on time, location and content, to identify influencers that will create greater impact if incentivized
- Detecting holders of competitor issuers, by matching purchasing behaviour and holder profile across segments
- Improving customer journey by offering personalized offerings, in real-time, based on purchase patterns triggered by purchase authorization
ELEMENT™ applications are aimed at increasing the efficiency of echanges, nullifying labour intensive tasks, shortening onboarding, lowering cost of operation, and improving traders experience.