THE SMART MONEY: Decoupling financial services for cross-departmental big data integration

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Fintech companies

Methods used by fintech companies are likely the way of the future for financial services. These startup, data-savvy organizations specialize in highly specific areas of this vertical. Moreover, they leverage data (most notably public data sources) to reach decisions for consumer-based action far quicker than the incumbents can. Kwiatkowski referenced a use case in which a small to midsize organization dedicated 3 months to forming a business plan for a loan that was rejected by a traditional financial institution. The organization then availed itself of PayPal’s working capital service and “within 15 minutes” had the money in its account. “PayPal didn’t effectively turn off all the risk management alerts and hand out some money,” said Kwiatkowski, noting that it was doing everything from start to finish with all of the appropriate asset risk management activities in place. The expedience with which fintech companies are able to produce these advantages directly correlates to their ability to swiftly integrate different data sources, especially public ones supporting the open data movement.


The data integration demands for trading opportunities are some of the most strenuous in finance, simply because of the sheer number and array of unstructured or semi-structured data sources involved. Organizations such as Reuters and others specialize in compiling business news that may affect trades. “If company A merges with company B, it’s a merge event; if company A fires CEO C at this time, that becomes an event,” said Jans Aasman, CEO of Franz. Other sources of alternative data, such as weather, are also incorporated. “Big banks and trading houses buy that data because it provides a mix of events they can put in their machine learning,” Aasman explained. Financial institutions put those events in factors and then start doing machine learning to see how to correlate the rise and fall of stock prices with the news that is available.

Integrating a variety of data sources also aids in monitoring trading activities, which is pivotal for legal and regulatory compliance. Marty Loughlin, senior vice president, head of global sales, Cambridge Semantics, described a trade surveillance use case in which an organization is “blending together trade data with other kinds of information—things like email, instant messaging, badge swipes of who’s coming in and out of the building, and when.” The versatility of this application of big data integration is one of its strengths. “It’s not confined to any particular kind of trade,” Loughlin observed. “It’s just for where you have traders actively trading, and you want to make sure you’re surveying them to make sure they’re not doing insider trading, front running, or anything nefarious.”

Customer 360

The most cogent example of capitalizing on the prowess of decoupling the enterprise via data integrations utilizes holistic customer views. By nature, Customer 360 views involve data across business units for a well-formed composite of clients. As with the trading use case, this application of data integration assists with both risk—most notably the mandate to “know your customer,” or KYC—and revenue generation. An example of the latter is analyzing credit card data, including commercial credit card lines and consumer credit card lines, to look across those datasets and see common customers, Loughlin commented. “That’s an ideal use case.” Customer 360 networks are also useful for assessing credit risk, determining loan recipients and amounts, and identifying additional means of “cross-sell, upsell, next-best offer, who are my most valuable customers, what are my most profitable products, and all of those questions that are hard to answer in big banks today,” Loughlin revealed.

Customer 360 initiatives are exacting for banks because of the distributed nature of larger financial institutions and the data sources they’re mandated to incorporate for KYC. Successful implementations not only redress these difficulties while offering a single place to query for and avail organizations of the insights Loughlin described, but also vastly accelerate the time required to reach these decisions. In this respect, organizations solving timely big data integration issues are also increasing their ability to keep pace with fintech companies—further propelling financial institutions into the prompt delivery of decoupled services. If someone wanted to get a mortgage in the old days, organizations would have to go through seven or eight different databases to figure out if they know the person in another context, the person’s level of wealth and investments, and so on, said Aasman. Now the goal with a Customer 360 network is to see everything, including the net worth of family members, the person’s occupation, or maybe a second home in another location.

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