Finance has always been a lucrative industry to work in. From the time of the first banks and insurance companies, it always paid off to manage other people’s money. Offering an annual average wage of $ 100,000, finance allowed millions to study a respected profession and find an attractive job, depositing cash, transferring money, authorizing a loan, underwriting insurance, investing in securities, equity or commodities or just managing all of the above.
But with the age of big data and AI a significant portion of these jobs can become obsolete, or at least transformed. As predictive analytics, computing power and granular personalized data are converging robots can achieve more and more tasks, challenging their human counterparts. After all, finance is all about taking hundreds or thousands of factors into account and then predicting the chance a customer will pay a loan back or the return a stock will create in exchange for investment – and what can be a better task for a robot than that?
That is why Opimas, a consulting firm, predicts that 230,000 jobs in the finance sector could disappear by 2025, and Oxford academics claim that 54% of total lost jobs will be in American finance industry.
Market Research Analyst
Market and company researchers and analysts can be found in most of the consulting branches in banks, or in firms such as Mackenzie, Gartner and Forrester. Their work is rather manual, going over detailed research papers, interviewing key executives, and writing dozens of pages per report. However, their service is considered to be expensive, ranging from tens of thousands of dollars for a simple task to an average of a few million for an annual subscription.
Company analysis may be the last kind of job you’d imagine would be automated. Yet, it can be done by harnessing NLP and AI algorithms that crawl over dozens of structured or unstructured data sources. Digital analysis startup Zirra had built an automatic process that delivers insightful outputs on private companies, including a list of possible competitors, level of competition, web traffic, a map of meaningful events during a company’s life, and an automated list of risk and success criteria. Its proprietary technology allows it to create high-level analysis insights within seconds.
Zirra collects and aggregates data from a myriad of public and private information sources, including structured directories, semi-structured databases, and completely unstructured text. It then utilizes Natural Language Processing (NLP) techniques to process large volumes of unstructured text, to detect companies and their semantic context, to model their interrelationships, and to derive dynamic indicators.
Zirra recently released a first of a kind AI-powered analysis bot called Emmet. By using simple English, Emmet answers company analysis with outputs within seconds. Take for examples this output when asked about the risk and success criteria for ride-hailing app Lyft:
Stock, Commodity, and Equity Analysts
Since robots are great at aggregating large amounts of information from various data sources, as well as at employing predictive analytics techniques from an even simpler data set, it is only natural that they can compete with a flesh-and-blood stock and equity analyst. What highly paid analysts to take hours to prepare, robots can do within minutes, even when talking about sophisticated predictions.
Serving hedge fund and bank traders, Kensho looks at real-world events and analyzes their effect on financial markets, and particularly on asset prices. For instance, Kensho can answer questions such as “what will happen to cement stocks when a Category 3 hurricane hits Florida?” Or “what happens to the currencies of oil-producing countries when oil trades below $45?” Following the iPhone 8 and iPhone X launch earlier this month, a Kensho study for CNBC found a pattern behind Apple’s shares after each iPhone announcement: the stock is flat on the day of the event, and then up 1.2% one week after the announcement, and up 2.6% three months later.
Kensho’s systems crawl through 90,000 financial and governmental reports and sources, searching for correlations between geopolitical and natural world event and financial assets. Backed by Goldman Sachs, JPMorgan Chase, Bank of America, Merrill Lynch, Morgan Stanley, Citigroup and Wells Fargo, the company serves trading desks at S&P and some of its investors.
The insurance industry is also facing a challenge from robots that are evaluating risks and compute actuarities. Insurance brokers are now being replaced by AI-based apps that sell insurance using chatbots. Startups such as Lemonade in the US, ZhongAn in China, and hibob and Gryphon in the UK have raised hundreds of millions of dollars each.
ZhongAn, backed by Ant Financial, Tencent and Ping An Insurance, partnered with the largest e-commerce, mobile, travel and insurance company of China to offer personalized insurance for their customers. ZhongAn sells insurance for web-based actions, such as shipping return policy to Alibaba’s shoppers, flight delay insurance to Ctrip’s customers, and cracked screen insurance to Xiaomi’s customers.
Lemonade has sold dozens of thousands renters insurance so far, claiming to be the fastest growing insurance company among New York millennials. Lemonade built a chatbot called AI Jim to provide insurance policies handle claims, thus replacing brokers. The chatbot can check the claim, validate it with the policy, approve the claim (or deny it), and detect fraud. Among others, it uses the location of residence and a video of the customer via its app as criteria in the process of authorization.
Loan officers, Credit Analysts, and Risk Managers
Let’s face it: risk is already calculated by robots. Whether it is the potential risk of not returning a loan or the risk entailed in making a fraudulent e-commerce transaction, robots are doing the jobs at the expense of banks and credit card companies.
Startups such as Riskified, Forter and Signifyd use machine learning to review credit card orders in e-commerce, evaluating the risk behind the orders and guaranteeing them so that any fraudulent orders that do pass through are reimbursed by them. Other startups, such as Fundbox, C2FO, Behalf, and BlueVine, are offering invoice financing for businesses, allowing them to expand and pay their payroll while waiting for third-party payments. In addition, Funding Circle, Lufax, Zopa, EZbob, and Blender operate peer to peer lending platforms, allowing individuals and businesses lend money from their peers.
Most of these platforms belong to small startups that have not shown any profitability yet, one has to admit. Further, they serve mainly small or medium businesses and haven’t yet penetrated into the 500 Fortune club to make a more significant change in the industry.
Personal Financial Adviser
The age of robo-advisers has begun. Financial advisers and planners are already being replaced by programs and chatbots that automatically manage money and wealth for individuals, mostly millennials and those with average wealth and investment needs. Robo-advisers bring an empirical methodology to financial advisory, and also offer cheaper fees than human advisors. Companies such as SigFig, Betterment, Hedgeable, WiseBanyan, Wealthfront, Futureadvisor, and Personal Capital manage together almost trillion dollars, according to Business Insider.
SigFig, for instance, uses algorithms to track several metrics in the portfolio and provides investment recommendations based on this technology. It also takes into account the customer’s risk tolerance and automatically diversifies the investment accordingly. An average investor could save up to $5,000 in fees each year, according to the company, or 4-5% of the average portfolio. The company has partnered with UBS and Wells Fargo to offer their customers the service.
Another startup, Personetics, is calling itself a “cognitive banking technology company” and uses predictive analytics and AI to provide timely insights to bank customers – or “nudges” as the company calls them. Customers will receive alerts about duplicate debits on their credit cards, that their cards have been used at new merchants, or that an important birthday is about to come so that automatic savings will temporarily stop. Other apps, such as Digit, Acrons, Chip or Qapital provide a similar service.