Fintech strategies require data aggregation
- Data aggregation is the underpinning of the modern financial experience.
- Platforms differentiate themselves in the breadth of their coverage and the mechanics of their data structure.
Modern financial apps are powered by user data. The more robust this data -- the more all-encompassing the data -- the more powerful the fintech service.
Modern digital banks can pull together spending data from all of their users' bank accounts in order to provide financial advice. Accounting software platforms, when they include accounts payable and accounts receivable information, can help small business owners forecast whether they can make payroll on a given month.
Access to financial data drives innovation. “Data is the new oil,” argues Linda Jeng, a senior fellow at Georgetown Law and former Federal Reserve official. “If you have access to data, then you have the ingredients to build better services.”
Fintech firms continue to produce and consume vast reams of financial data. Technology firms entering into financial services bring with them lots of demographic and behavioral information about their users. The future of financial services is as much about financial data as it is about innovative new products.
To connect to users' various accounts across institutions, fintechs generally turn to data aggregators. These technology and connectivity platforms provide fintech companies with access across tens of thousands of financial institutions, account types, and data sources -- a monumental task.
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There are a handful of major data aggregators that work with banks and fintechs to provide consumer-permissioned financial data. Each generally has its own strengths, differentiating themselves in how wide their coverage of banking institutions is or in the mechanics of their data structure.
Data aggregators continue to roll out new products and services, as they serve as the underpinning of the modern financial experience.
A new whitepaper by Fiserv outlines five categories of evaluation criteria for selecting a data aggregator. These include data reliability and cleanliness, coverage of institutional sources and data type handling, platform adaptability and support for innovation, security practices, and privacy, transparency, and reliability.