Enriched Data Leads to a Better Breed of Personal Financial Management Tools

Context is king in financial services today. The rich data delivered around transactions, particularly from mobile devices, is fueling the growth of a new breed of personal financial management.

On Tuesday, data aggregation and analytics platform Yodlee, an Envestnet company, made a further advancement in delivering contextual information to customers while protecting their data with its transaction data enrichment, a machine-learning engine which provides data based on its findings from millions of transactions. (c) Can Stock Photo

The engine focuses on a problem many in fintech are trying to rectify, which is the ability to bring context to financial data for both consumers and the financial institutions that serve them. In addition to Yodlee, companies such as Moven and MX offer algorithms designed to categorize their clients’ financial data, as well as tools such as Hydra, MX’s multi-source aggregation platform.

“Consumers and financial institutions want to see something that’s intuitive,” said Yodlee VP of Marketing and Senior Director of Product John Bird. “Contextualization of that data is very important, because I think what you’re going to see in the next couple of years, especially in fintech, is a layering in of predictability and predictability analytics, which requires useful data.”

As consumers are coming to expect easier and clearer management of their finances as things like mobile wallets become more and more popular, the ability of both consumers and financial institutions, whether they are traditional banks or services like the U.K.’s Mondo, is key, making it critical for platforms such as Yodlee and MX to provide access to the best possible data.

To this end, MX partnered with virtual banking solutions provider Q2 Holdings earlier this month in order to provide more valued insight into consumer finances, while Yodlee’s new data engine focuses on that same goal by adding things like “a simple description, merchant name, category, and transaction type” to better contextualize that data.

The contextualized data can now create personalized patterns and help reduce the amount of time banks spend inquiring into fraudulent accounts or chargers.

According to Bird, this leads to cost reduction for financial institutions, as well as allowing them to offer consumers “the on-the-fly money management that they want nowadays.”

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