Fintech Startups and Artificial Intelligence
Startups and established financial service firms are targeting these days to provide investors clearer guidance with information collected and captured from multiple sources. Artificial intelligence contributes to providing greater insights and thereby better customer experience by capturing vast amounts of data in real-time as well as helping users in understanding how different data points relate to each other. Financial services are leveraging data-driven technologies in order to overcome the breakdown in brand loyalty.
Cambridge, MA-based Kensho, which recently received $58 million in funding from Goldman Sachs, San Francisco-based Alphasense backed by Tribeca Venture Partners and Toronto-based Bigterminal are some of the fintech players leveraging artificial intelligence in fintech.
Relevant article: Big Data in the Space of Fintech
It’s a lucrative market. Equity deals for artificial intelligence startups including fintech, has increased nearly six times, to nearly 400 in 2015 up from 70 in 2011. More than 200 AI-focused startups raised nearly $1.5 billion (U.S.) in funding in first half of the year 2016.
“Data is the lifeblood of AI,” Falguni Desai of Future Asia Ventures wrote in Forbes recently. Desai quotes Adrian Lawrence, partner at Baker & McKenzie, in saying: “data and the various rules and processes which both enable and regulate access to and use of that data, stand at the heart of disruptive fintech businesses.”
The market is also “evolving from a descriptive analytics model (rear view mirror view) to a predictive analytics model (insight GPS view),” says Jim Marous, co-publisher of Financial Brand. “With predictive analytics, we are in a better position to ‘know the consumer’, ‘look out for the consumer’ and ‘reward the consumer,’” he writes, “learning from previous experiences and predicting future behaviour.”
BigTerminal CEO Adam Rabie says advances in machine learning are allowing fintech platforms like his to do more for their customers.
Powered by IBM Watson, Bigterminal’s solution curates, consolidates, and analyzes financial data from markets, social media, and other sources. The company’s target market includes researchers, analysts, and traders as well as big banks and insurance companies. Bigterminal’s app can be used to conduct research, generate hypotheses, and make decisions based on significantly more data than what financial analysts traditionally use.
Improvements in cognitive technology, such as relationship analysis and language comprehension, will expand the possibilities for data analytics in finance and banking. As fintechs bring this functionality into their services, they will continue driving disruption in the financial world.
The Big Risks of Big Data & Regulating Fintech
Due to the quantity of big data and its analytics, there has been a big question of data privacy and security.
Since the availability of data has increased, this data also involves sensitive information and as a consequence consumers fear data theft, privacy infringements, and information fraud.
Financial services firms are providing for solutions that are built in the applications that provide for guarding both personal and professional data and its accessibility. In addition, in order to manage data collected by firms, various governance programs are being developed and regulations are being brought into sight to provide for compliance and security.
Fintech regulation, however, is a work in progress as independent territories struggle with the challenges faced by a financial arena which extends across borders. Entire Countries may enact specific regulations to be adhered to by fintech organizations, but this doesn’t necessarily safeguard citizens as fintech evolves into a global industry.
Some jurisdictions such as Australia, Singapore, and the U.K. have implemented strategies such as a “regulatory sandbox” framework which allows for limited testing over a restricted time after which existing regulations must be followed, while others are creating fintech councils which help address necessary legislation and compliance.
Major challenge that is being faced by firms and their managers is to master the skill of extracting meaningful insights from raw noisy data that is collected from social media, satellite information, personal data, sales and marketing data, etc.
Two Sigma, which says it was exploiting big data before it was a buzzword, is one of the handful. The $37 billion hedge fund has outperformed rivals partly by using machine-learning algorithms to find trading signals in the data. Now traditional hedge funds, which have relied on information such as company filings and shopping surveys, are on the hunt.