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Data Mining and Predictive Analytics for Financial Institutions: Banks Hold Trust Advantage When Tracking Consumers in a Privacy Minefield
Javelin Strategy & Research, June 2011, Pages: 40
Three of the nation's best-known brand names—Apple, Google, and Facebook—have taken a beating in recent weeks as consumers and politicians have focused on how privacy protections are eroding as corporations seek to use consumer information and location-based data to provide online and mobile services.
But the recent controversy should not frighten away financial institutions that smartly and transparently tap into a rich repository of personal finance management (PFM) data— and combine it with location-based data, and online and mobile search and behavior data.
A Javelin consumer survey demonstrates that FIs enjoy an enviable position of trust over third- party affiliates, merchants, and billers and can use PFM data to build loyalty, deepen relationships, and open new revenue streams. But that strength is under siege from powerful non-bank rivals such as Apple, Google, Facebook and Amazon.
In this report, Javelin outlines how to solidify that trust advantage, weighs three models that have emerged for mining PFM data, outlines why FIs should combine PFM data with location-based data, and details how imperfect data is hampering PFM data mining.
Primary Questions
- What is the return on investment (ROI) from mining personal finance data?
- What lessons can financial institutions (FIs) learn from the recent controversy and congressional hearings into the collection and use of location-based data on iPhones and other mobile devices?
- Are FIs well positioned to extract value from PFM and location -based data?
- Do consumers trust FIs more than third-party affiliates, merchants, and billers to track and analyze their financial transactions in order to provide better service?
- How should FIs seek consumer approval to analyze PFM and location-based data?
- What are the pros and cons of the “banker dashboard model,” the “black-box model,” and the “search engine model” for extracting value from PFM data?
- How could PFM data be improved to enhance the value of transaction data?
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