Published on March 5, 2014
Top 5 Big Data Use-Cases in Banking & Financial Services #sap #services #BigData
1. Fraud Detection and Security Prevent fraud by leveraging analytics, machine learning, and Big Data technology to gain a holistic view of customers, identify patterns buried in data, cluster information, and distinguish fraudulent activity from normal activity. © 2014 SAP AG or an SAP affiliate company. All rights reserved. 2
2. Compliance and Regulatory Reporting Comply with a key provision of the Dodd Frank Act that requires big swap traders to document everything that goes into each swap trade by implementing a deal monitoring system based on a new generation of Big Data technology © 2014 SAP AG or an SAP affiliate company. All rights reserved. 3
3. Customer Segmentation Group customers into different segments to support sales, promotion, and marketing campaigns by collecting and analyzing all available data and using Big Data technology to mine for intelligence from underlying data. © 2014 SAP AG or an SAP affiliate company. All rights reserved. 4
4. Risk Management Support new regulations and increasing demand for better internal management support by implementing a central, integrated finance and risk management data platform that can quickly and flexibly address new requirements © 2014 SAP AG or an SAP affiliate company. All rights reserved. 5
5. Personalized Product Offering Target new product and service offerings to the right customers by implementing software that supports flexible and integrated processes for understanding customer buying habits, what channels customers listen to, and who the key influencers are. © 2014 SAP AG or an SAP affiliate company. All rights reserved. 6
Learn how SAP Services can help your banking or financial organization realize tangible business benefits by implementing a Big Data strategy. @SAPServices Visit: http://www.sap.com/solution/big-data.html © 2014 SAP AG or an SAP affiliate company. All rights reserved. #sap #services #BigData 7
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Prevent fraud by leveraging analytics, machine learning, and Big Data technology to gain a holistic view of customers, identify patterns buried in data,
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