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George Colwell – Leveraging Real Time Data Insights | SAP Banking day #sapbankingday

George Colwell – Leveraging Real Time Data Insights

Comments from this session:

  • Types of data for Banks:
  • legacy
  • premium content
  • social media
  • deep web
  • PFM – whether presented to clients or not, client data must be aggregated and understood at that level
  • Premium data – commercial services
  • object is one single view of data representing best view of client
  • data insight:
    • sense & respond
    • predict and act
  • the better the data the better at prediction
  • embedding the predictive capability at the business process level real time
  • scoring example in a payment fraud startup
    • location
    • time of day
    • etc
  • decreases chance of false positives
  • [ed] this is also an opportunity for marketing and pro-active service levels
  • other data elements
    • social media for brand sentiment
    • relate to purchase propensity
    • mortgage payment to another bank
    • mortgage payment stops – why?  (different bank, sold house, lost job etc)
  • personalised care
    • Amazon example again – they are watching for customer service problems
    • bank example:  customer tweets about bank problem – don’t just ask why – relate the tweet to actual client transactions and frame service response in that contaxt
  • George introduced Dan Adamson from http://www.outsideiq.com/.
    • doing interesting things by aggregating customer data around the web, and developing structured views and analysis on that client
    • using HANA predictive cycles go from 12 hours to 30 seconds to 3 minutes
    • the plug here is that with enormous data sets HANA runs the cycles much faster