Making Big Data Work in Retail Banking

Making Big Data Work in Retail Banking

          
Title image

Making Big Data Work in Retail Banking

  • Add To Interests
  • SAVE CONTENT
  • PRINT
  • PDF

  • Technology_Advantage_Apr2016_Page_Promo
    Improving Current Practices with Point Analytics

    One of the simplest—and most powerful—applications of data analytics is the development of point solutions for individual needs and issues while steering clear of other areas. Big data can be used to improve the assessment of customer risk in a particular context, for instance. Data analytics can also be employed to more effectively measure marketing potential.

    One large European bank, for example, used a combination of point solutions to upgrade its credit underwriting and pricing and to enhance the effectiveness of cross-selling and up-selling campaigns. The bank had been running campaigns to increase the share of high-end (gold and platinum) credit cards in its portfolio. It had been using both risk assessment and marketing analytics based on aggregated data to preapprove current standard-card customers and target potential new clients. Its transformation rate was an unimpressive 3% to 5%.

    We helped develop a series of advanced-analytics models that can process far more detailed customer information—including data collected at the transaction level and compiled from multiple sources—related to credit risk, behavior, card use, and purchase patterns for other products and services. Using this data and the new models, the bank generated an entirely new series of risk and targeting scores. After a few adjustments were made on the basis of test campaign results, the new scores were applied to the bank’s full portfolio of card-marketing programs. Uptake surged fivefold to an average of more than 20%, and the bank generated tens of millions of euros in new revenues—without incurring the excessive costs often associated with new-client acquisition in saturated European banking markets.