There is something extremely intriguing about the way startups are disrupting the fintech space. The likely anticipation is that a new unicorn might emerge from anywhere and anytime. Both the investor community as well as the startups are bullish on this possibility. More interestingly, the disruptive manner is which new ideas and startups are taking down the high-walls of the conventional banking enterprises has all the elements of a transformation story in it. Many argue that fintech is nothing short of a revolution. Proponents argue that fintech will eventually reduce traditional banks to narrow banking functions only, while fintech companies will take over the tech and data driven borrowing and lending decision making and interface, including the transaction infrastructure. How it will change banking? It will transform it. Conventional banks will operate on lesser margins and will have to redefine the rules of customer engagement and service quality as fintech companies will exercise their influence. Though that day still seems far today, what is undeniable is the extent to which fintech companies are augmenting machine learning into their processes to create effective and efficient systems. And in today’s article, we’ll have a look at some of the leading machine learning startups to assess the ways the technology is defining fintech.
AI backed UX for Human like Interactions
Kasisto is one startup that attracted attention last year when it presented its offerings to Goldman Sachs. Kasisto matches state-of-the-art UX with data-driven artificial intelligence to give customers a human-like interaction experience. Such an effort, if completely optimized, can rewrite the rule books of personalized financial services. It raised $2.25 in seed funding in August last year.
Faster and Accurate Processing of Decisions
Financial processing, underwriting and decision-making could be augmented by AI technologies that allow computers to process data and make decisions, such as credit related, quicker and efficient. Testing for fraud, risk, and important events is difficult to do at a truly individual level, and as a by-product it’s defined the business model of many financial services companies, but augmenting human data decision-making is an area with high potential. Many startups have entered this domain including the likes of Affirm, Zest Finance, Kreditech, FinGenuis and Kensho, the startup backed by Goldman Sachs and Google Ventures.
Binatix is a Palo Alto based deep-learning based trading firm that has employed machine learning algorithms to gather analytical edge in trading. Another startup, Williamsburg based Inovance is trying to bring machine learning based trading analytics to the rescue of common investors. Compared to traditional matching and testing algorithms, machine learning can optimize predictions and trading rules to an altogether new levels.
Fraud Detection and Transactional Safety
This segment is witnessing heightened activity with startups finding new ways to address the much prevalent fraudulent transaction issues. Startups, by leveraging in-depth data analysis on user transactions, are building AI-backed models that predict frauds better. Billguard is one such startup operating in this field. It uses crowdsourced big data analytics to harness the collective knowledge of millions of consumers reporting billing complaints online and to their merchants and banks. The free service scans user’s e-statements daily, alerting them to bad charges and helping them get their money back. Another startup in this domain is Brighterion that employs behavioural machine learning methods to detect frauds. Feedzai, the SAS based fraud protection startup, raised $17.5 million Series B funding earlier this year.
Portfolio Construction/ Automated Investment
A number of startups have also made headway in the fixed income world. For example, BondIT utilises machine learning for portfolio construction for investment managers. A bigger player in the market is Wealthfront, which has already raised funding to the tunes of $65 million. Upside (acquired by Envestnet earlier this year) was another startup operating in the segment, focusing strongly on its design-led interface platform for investment accounting.
Automated Lending Platform
This is an already crowded market. Business loans institutional marketplace players such as Lending Club and OnDeck have already gone public, signifying trust for the business model. Other players in the market include similar businesses such as Kabbage, LendUp and Avant. In addition, newer business models are also emerging. Pay Your Tuition (PYT) works on a similar model but focuses only on connecting students and banks, helping aspirants secure educational loans at optimized interest rates.
New York based Dataminr is a leading real-time information discovery company. Employing machine learning algorithms, it transforms real-time data from Twitter and other public sources into actionable signals, identifying the most relevant information in real time for clients in the financial sector. Most recently, earlier this year, the startup raised $130 million in Series D funding. Another startup, Social Alpha, provides actionable insights for traders, analysts and investment managers. Another startup, Social Alpha, is using a similar approach while focusing on the portfolio stock investment segment.
San Francisco based AlphaSense is building the search engine for financial search. Employing natural language processing (NLP) and machine learning algorithms, its search engine mines wealth of information answering conversational style questions.
Startups are employing machine learning to build next-gen identity authentication tools for the financial sector. Behaviosec is a Swedish startup that focuses on behavioral biometrics for identity access and verification. It already has gathered some institutional clientele for itself. Another Nordic company at the forefront of this technology is Norway-based Encap that employs context-based multi-factor authentication and app shielding mechanism. Last heard, the startup raised $2 million in mid-2013.
Quite frankly, to judge that fintech startups will bulldoze the traditional banking is an oversight for now. Lending Club, through its marketplace, has issued loans up to $11.2 billion, which is a mere 1% of total US credit-card debt. These startups are yet to undergo the test of times and downward turns of a full-industry-cycle to determine their utility. But notwithstanding that, their arrival is definitely helping the industry to optimize itself towards higher efficiency. Fintech operators derive better margins than conventional banks; this is something which will force conventional banking to rethink its strategies. Otherwise, the extra margin at hand will allow fintech to offer unbeatable deals to customers. Also, data-driven decision making is expected to be less biased and wiser than conventional methods, thereby easing the comparative risk exposure of fintech business models. Then, unlike a bank, fintech companies do not have long-term and diversified agenda for its assets, which will translate into greater focused and transparent set of operations. Having said all that, we should also expect the big banks to adopt the tech and allay the fears of any threats.