Read about how Linked Data and semantic technology can enrich data and pave the way to advanced analytics.
Technology is changing the way consumers relate to their finances, and the way institutions function in our financial system.
So wrote Adrienne Harris, Special Assistant to President Barack Obama for Economic Policy, in The Future of Finance is Now article published on The White House website in June 2016.
The rise of fintech in recent years has upended the traditional banking models and opened up opportunities and niches such as peer-to-peer lending, digital wallets and mobile banking, personal finance management and planning, and crowdfunding, to name but a few.
At the same time, the massive amount of data has spurred innovations in data management, big data analytics and predictive modeling.
Still, the huge data flow in the fintech industry is vastly unstructured, and sifting through numbers and finding links between datasets is enormously resource- and labor-intensive. This is where Linked Data comes into play to put data into context and expose various links between these concepts.
With Semantic Technology, Linked Data can be managed via a semantic graph database, also known as an RDF triplestore, which enables the inferring of new relationships out of existing facts. And if this Linked Data is further interlinked with free-flowing text by generating metadata that also becomes part of the semantic knowledge graph, even more complex analytics and querying of the available information can be performed. Various entities such as people, organizations and places can be classified and disambiguated by linking the information in the text to other Linked Data sets, for example, the linked open data source DBpedia.
Classified and linked data in big data analytics helps fintech organizations to have a larger view of their customers, potential clients and their credit scores, and new niches and streams of revenue. Text mining with semantic technology helps to extract relevant and meaningful insights from social media posts, for example.
Linked data in analytics allows the fintech industry to segment customers and offer personalized financial advice and solutions to small and medium-sized enterprises (SMEs), millennials, or the underbanked and unbanked who have little or no access to traditional retail banking. Using Linked Data in big data analysis also helps with fraud detection and flagging suspicious customer behavior. Predictive analytics tools are a useful source of information on how a group of customers would react to new products, altered rates, or discount offers.
The fintech industry was fast to seize the opportunity to extend loans to small businesses, which are otherwise snubbed by traditional banking either because companies are too new or too risky and without any credit history. SMEs, on the other hand, have been often wary of the lengthy review processes the banks require when they need the money immediately. Many online lenders rely on big data and internal algorithms to calculate the amounts of credit extended and interest rates, or to analyze any potential new customers and offers.
Millennials are another huge group of customers that stays away from traditional banking and is open to a digital and remote ‘marketplace’.
As early as in 2013, The Millennial Disruption Index study by Viacom’s company Scratch showed that banking was at the highest risk of disruption. The survey showed that 71% of Millennials in the US would rather go to the dentist than listen to what banks are saying. In addition, 70% believed that very soon the way we pay for things would be completely different, and 73% would be more excited about a new offering in financial services from Google, Amazon, Apple, Paypal or Square than from their own nationwide bank.
For the fintech industry, the Millennials group is an opportunity to offer personalized finance solutions, savings and investment advice, on the basis of linking data from social media posts to understand how digital natives interact and how they view financing. Millennials, active users of all things digital, are willingly sharing information on social media and are open to innovative and customized fintech models and offers.
The rise of fintech and the simultaneous increase of the use of smartphones, even by unbanked or underbanked people, is another opportunity where linked data and big data help track consumer behavior and potential sensitivity to the cost of funding.
Jason Furman, who serves as President Obama’s Chief Economist and a Member of the Cabinet, wrote about fintech on the White House blog:
Some of these platforms may have the potential to reduce the cost of serving low- and moderate-income consumers, expanding access to safe and affordable products and decreasing the use of alternative financial services.
In this cost-sensitive group of target customers, Linked Data and big data analytics play their part in predicting how clients would react to any changes in interest rates or in the terms of their savings accounts.
With the rise of funding offers, the fintech industry also needs advanced analytics and predictive models to detect fraud.
Text mining with linked data that disambiguates between people, organizations and locations can flag potentially suspicious transactions for further monitoring, scrutiny or reporting to authorities.
Text mining, combined with data analytics, can also detect more easily changes in regulations, which have been frequent since the 2008 financial debacle.
While the fintech industry is on the rise, banks are not sitting on their hands and are trying to adapt to a fast-changing competitive landscape. Goldman Sachs, for example, launched earlier this year an online-only banking service for individual retail clients, in what analysts described as venturing into financing for ordinary people.
It’s not only investment and commercial banks that have seen the potential of fintech, though. In mid-June 2016, Mark Carney, Governor of the Bank of England, announced the launch of FinTech Accelerator, which would work in partnership with Fintech firms.
FinTech has the potential to deliver more resilient financial infrastructure, more effective trade and settlement, and new ways to encode, share and analyse data, Mr Carney had written in the speech he had to deliver the day MP Jo Cox was killed.
Analyzing data from social media can result in getting a clue on how equities would move on the stock exchange. HedgeChatter, for example, analyzes millions of social media posts in real time to provide clients with the current market sentiment and show which US stocks are positioned to move in which direction. And although, of course, a sound investor judgment usually involves considering many other factors, still, the existence of such a business points to the possibilities that data analytics holds in predictive modeling in fintech.
Structured and semantically linked data allows for gaining faster insights and reveals knowledge, which puts the organizations using linked data and analytics ahead of competitors. Fintech organizations with the ability to be proactive rather than reactive are set to stay up and floating in the already crowded fintech startup market.
In a December 2015 report ‘Cutting Through the FinTech Noise: Markers of Success, Imperatives For Banks’, McKinsey & Company said:
Perhaps the most exciting area of fintech innovation is the use of data.
Indeed, it is.
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