Personal Financial Aggregation and Social Media Mining: A New Framework for Actionable Financial Business Intelligence (AFBI)

Consumers have been banking and trading online for several years now. More ambitious and tech savvy consumers have also been constructing an overview of their financial life by using Personal Finance software like Quicken and online tools such as Yodlee and Mint.com. Since late 1999, Personal Financial Aggregators PFAs have started offering internet based services to automate this process of account aggregation. This web account aggregation allows individuals to log onto one Web site and view all of their online accounts in one place. Online accounts that can be aggregated include financial sites bank, credit card, brokerage, insurance, etc. as well as lifestyle-based sites travel awards, email, chat rooms, etc.. The idea behind Personal Financial Aggregation is to offer consumers their own personal portal from which they can see all their finances at a glance, balance and rebalance accounts, make investments, pay bills, etc. In addition to this Web data aggregation, consumers are relying on social media sites such as facebook, tweeter and other internet forums to get financial advice from each other and also to critique various financial products and services. As a result, many Financial Institutions FIs are using social media analysis and mining to shape their businesses. FIs include consumer banks, brokerages, insurance, wealth management firms, etc. This paper presents a framework for financial institutions that combines social media mining, web mining, online advice engines, and web aggregation. This framework can be utilized by FIs to analyze online buzz about their products/services and combine those insights with web aggregation and online advice to create different revenue streams and to offer personalized bundled products and services. The authors conducted interviews with various executives at the Global Financial institutions and insurance companies to test and validate this framework. A comprehensive review of top service providers and vendors that can enable and drive this framework is also discussed in this paper, followed by managerial implications, benefits and challenges.

[1]  Ion Smeureanu,et al.  Applying Supervised Opinion Mining Techniques on Online User Reviews , 2012 .

[2]  Thilini Ariyachandra,et al.  Mobile Business Intelligence , 2013, Int. J. Bus. Intell. Res..

[3]  D. BergerPaul,et al.  The Impact of Social Media Usage on Consumer Buying Behavior , 2012 .

[4]  Steve Smith,et al.  Conceptualising and Evaluating Experiences with Brands on Facebook , 2013 .

[5]  Victoria L. Crittenden,et al.  The use of social media: an exploratory study of usage among digital natives , 2012 .

[6]  Qingyu Zhang,et al.  Web Mining: a Survey of Current Research, Techniques, and Software , 2008, Int. J. Inf. Technol. Decis. Mak..

[7]  M. Thelwall,et al.  Data mining emotion in social network communication: Gender differences in MySpace , 2010 .

[8]  Michael F. Gorman,et al.  Searching for Herbert Simon: Extending the Reach and Impact of Business Intelligence Research Through Analytics , 2013, Int. J. Bus. Intell. Res..

[9]  Hiroshi Sasaki,et al.  Time Lags Related to Past and Current IT Innovations in Japan: An Analysis of ERP, SCM, CRM, and Big Data Trends , 2014 .

[10]  Mahesh S. Raisinghani,et al.  Intelligent Agents for Competitive Advantage: Requirements and Issues , 2004 .

[11]  Jaideep Srivastava,et al.  Web Business Intelligence: Mining the Web for Actionable Knowledge , 2003, INFORMS J. Comput..

[12]  Stuart E. Madnick,et al.  Seizing the Opportunity: Exploiting Web Aggregation , 2001, MIS Q. Executive.

[13]  W. Scott Spangler,et al.  Leveraging sentiment analysis for topic detection , 2010, Web Intell. Agent Syst..