By Sarah Yung
Blue-jacketed market makers bustle across the New York Stock Exchange floor from opening to closing, standing ready to buy and sell stocks listed on the exchange. NYSE’s human traders are the face of Wall Street, but they may soon become obsolete. Fintech - the integration of technology into financial services - is a quickly growing field today that threatens to flood the financial industry.
A Machine World - Convergence of AI and Fintech
Analysts predict a torrent of Artificial Intelligence (“AI”) will soon sweep through the industry, driving companies to drop their high-earning traders in favor of machines. Financial giants have slowly been integrating AI-driven systems, which can foresee market trends and make trades better than humans. Machine learning algorithms simply excel at analyzing data, regardless of size and density. Algorithms can detect patterns that are difficult for humans to spot, and can process information fast enough to make short-term trades. For example, the algorithm can use price movement in the S&P 500 index to predict moves in individual stocks and then make trades. A flood of AI-based technology will displace many traders earning up to millions of dollars.
In a few decades, analysts predict that 90,000 out of 300,000 jobs in asset management will be lost to AI. However, society as a whole may benefit from this change. Wall Street attracts some of the most brilliant minds in society. About ⅓ of graduates from the top 10 business schools go into finance. As active managers divert money from human equity analysts to engineers, quants will be incentivized to seek work in other fields. Bright graduates who went to Wall Street can occupy openings in other fields like healthcare and energy, and as well as joining nonprofits. This could lead to advances in these fields that could tangibly benefit many people.
Digital Wealth Management
Although we haven’t yet reached that point, innovative new companies bring us closer and closer. A number of completely AI-based hedge funds have emerged in the last few years, among them Sentient and Numerai. Although many companies are integrating artificial intelligence into their operations, they are reluctant to hand over full control to machines. Only a few pioneering hedge funds like Sentient and Numerai have fully automated processes.
Machine learning models open up new methods to make predictions and draw conclusions. Satellite image recognition can give insight into real-time data points like parking lot traffic, using this data to derive business insights like frequency of shopping at specific stores. Advanced natural language processing techniques can study the mood of a news article of financial review and quickly analyze a company’s financial reports. This condenses large sets of text data into key points of interest that are easy for researchers and analysts to leverage.
There is a growing interest in quantitative trading - using large data sets to identify patterns that can be applied to trading. Although most companies aren’t fully automated, many are integrating new technology into their structure. Alpaca, based in San Mateo, California, combines deep learning and high-speed data storage to identify patterns in market price changes. They recently partnered with news giant Bloomberg to provide software that predicts short-term forecasts in real-time for major markets.
Finance is an ideal breeding ground for automated processes - it has a vast amount of publicly available data. The increase in computational power over the last decade or so makes these fields a good match for each other. Companies and investors from both financial and AI sectors are cautiously optimistic about the future of machines in finance. However, only time will tell whether AI is truly the best route to go in the financial sector. The ultimate future of AI will depend on its ability to turn a profit.
Prediction v. Judgement
Although technology is constantly improving, artificial intelligence still needs a human touch to keep it on track. Modern software struggles with predicting crises because every crisis is unique. It needs a wealth of historical data to make comparisons to and then make a prediction. Fund managers play an important role in the integration of AI, using their instinct to guide machines. Still, AI will make waves in the financial sector with its ability to refine and improve human predictions.
Profiling clients based on their risk score is a crucial ability for financial institutions. AI is an excellent tool for banks and insurance companies because it can automate categorization of clients based on their risk profile. Advisors can associate financial products with each risk profile. From there, they can then optimize product recommendations for clients.
Similarly, technology can be applied to develop valuation models for investment and banking in general. Such models can calculate the valuation of an asset using surrounding data points and historical examples. This model is traditionally used in real estate, where it can be trained on previous sales transactions, but it can be utilized in financial firms as well, using economic indicators and growth predictions, among others, to predict the value of the company and its assets.
Although we are fast entering a world that functions on computers, humans will still play a big role in the era of AI. Fund managers, in particular, are critical to the implementation of machines into a firm’s day-to-day operations. Because they rely on historical data, machines are not trained to anticipate or respond to events that haven’t happened before. Every crisis is unique, requiring a human touch to guide technology through stormy seas. A manager’s intuition about economic trends are the foundation of long-term strategies. Machines can find patterns and make predictions, but the role of human intuition in guiding and refining their predictions is equally critical to the process.
AI Risk Management
Many of life’s necessities - like landing a job and renting an apartment - hinge on having good credit. Banks and credit lenders are using artificial intelligence solutions to more accurately assess borrowers in the credit evaluation and approval process. ZestFinance is the maker of the Zest Automated Machine Learning (ZAML) platform, which helps companies assess borrowers that have a paucity of credit information or history. Scienaptic Systems is another company that runs an underwriting platform for banks and credit institutions. In just three weeks with a major credit card company, Scienaptic achieved $151 million in loss savings.
Accurate and timely forecasts are crucial to many businesses in the finance world. Financial markets are using machine learning to create more nimble models. These predictions can be used to leverage existing data, helping financial experts pinpoint trends and identify risks while conserving manpower. Financial institutions like J.P. Morgan, Bank of America, and Morgan Stanley are integrating machine intelligence and data analytics into their operations. In March 2018, S&P Global announced a deal to acquire Kensho for about $550 million. Kensho’s software uses a combination of cloud computing and natural language processing to answer complex financial questions. Ayasdi is another company deploying software to understand and manage risk.
Banks are also joining the technology craze. Like in retail, many banks are looking to use AI in chatbot software, increasing customer satisfaction and efficiency without the expense of hiring extra customer service workers. A study of 33,000 banking customers found 54% want tools to help monitor budget and make real-time spending adjustments. Using AI to learn from customers can help create a better banking experience for all.
Trim is a smart app that helps users save money by analyzing spending. The app can cancel subscriptions, find alternative options for services like insurance, and even negotiate bills. Trim has saved $6.3 million for over 50,000 people. Sun Life created a virtual assistant, Ella, which sent users reminders to allow them to stay on top of their insurance plans. Using computers to interact with customers is not new - chatbots are a new approach to automated customer service because they can cope with a huge variety of unstructured responses, and are continually refining how they interact with consumers.
Financial institutions like Bank of America are also instituting smart technology in the hope that this software will maintain and increase customer loyalty. Bank of America uses a bot called Erica as a digital financial assistant. The bot enables users to search their historical data for a specific transaction and computes the total amounts of credit and debt - two tasks that were time-consuming for users. JPMorgan and Chase are also increasing their connectivity through launching a mobile banking app, which makes them accessible from anywhere at anytime of day.
Customer Satisfaction and Engagement
Artificial intelligence can also be used to document customer information in a timely and efficient manner, drastically improving the user experience. Many are familiar with the processes in the insurance industry. Clients subscribe insurance, for which they pay. However, the process for activating their coverage in the case of an incident is often lengthy and complicated. Transactional bots can make this process much less painful for users. A transactional bot would walk the customer through the process, taking in photos and videos of the damage, and other information required for processing the claim. The bot could also run the application through fraud detection and provide potential values for payout.
Having a bot in charge of the entire cycle can reduce costs and operational tasks for the company and cut errors overall. Features like image recognition, fraud detection, and payout prediction upgrade the entire user journey, improving the experience for both users and the insurance company. Lemonade, a New York-based insurance startup, is leading the charge on this front. Their motto - “Forget everything you about insurance” - signals how they are going to disrupt the industry through the use of AI. Since their creation in 2015, they have raised over $180 million. The Chinese financial services group Ping An is incorporating similar software that can offer a while-you-wait quote to settle the claim.
Retaining clients is key ability in all industries and businesses. AI can support managers in this aspect by analyzing clients for signs that they are considering cancelling their policy. By providing a prioritized list based on client behavior, AI. The manager can leverage this list to provide better service and improved products to higher priority clients.
Cybersecurity and Fraud Detection
One of the most powerful applications of artificial intelligence comes in the ability for fraud detection and prevention. Huge quantities of digital transactions take place via online accounts and applications, and it is impossible for humans to monitor all of these transfers. There is an urgent need to ramp up cybersecurity and fraud detection efforts. Darktrace creates cybersecurity solutions for financial institutions. The company’s machine learning platform analyzes network data to detect suspicious activity before it can damage a financial form.
Computers may also be able to leverage human behavior to detect potential instances of fraud. Although micro-expressions are not infallible, they can be incorporated into fraud detection algorithms. Technology can also spot other patterns of potentially fraudulent behavior early on. For example, GoCompare, in partnership with analytics company Featurespace, can detect suspicious behavior like repeated changes to name, employment, or postcode, and block the transaction or raise an alert.
Citi Ventures, a private equity firm, is venturing deep into the fields of artificial intelligence, big data analysis, and machine learning. They have made multiple investments into companies deploying machine learning in new and innovative ways. One company Citi Ventures has invested in, Feedzai, is able to scan large amounts of data and recognize threats as they emerge, sending real-time alerts to customers. Citi Ventures continues to have an active presence in fintech, investing in companies focused on topics ranging from cybersecurity to real estate.
MasterCard also aims to increase convenience while reducing the risks of fraud and cybercrime. However, they must be mindful to avoid flagging genuine transactions as fraudulent. MasterCard came up with their Design Intelligence platform to reduce false declines and make fraud detection more accurate. They acquired the AI company Brighterion as part of their mission to make all online payments fraud-free. As time passes, the self-teaching algorithms should make better decisions regarding fraud detection.
AI-Powered Blockchain Smart Contracts
One of the most powerful applications of AI comes in its combination with Blockchain, a new system for storing and tracking digital information - utilizing an encrypted, distributed ledger format. In Blockchain, data is encrypted and distributed across multiple computers, making highly robust databases that can only be accessed by those with permission.
Applying machine learning to consumer actions, like filling out contracts and submitting incident reports, often brings up the question of user privacy and security. Relevant financial data is often also sensitive data. Increasing use of Blockchain combined with AI-algorithms enables our software to better predict and detect fraudulent financial transactions and build trust between contracting parties.
Human Intelligence Prevails
In a globally connected world, there is an urgent need for automated analysis that far exceeds human abilities. The rapid evolution of computing tech, providing advanced analytical capabilities at lower and lower costs, makes it more and more attractive. Ultimately, automation allows employees to focus their energy on revenue-generating activities and customer concerns. However, while technology is propagating rapidly into many fields, humans are still in the driver’s seat.
Trust is still critical for anything to happen. Even the most accurate algorithms could go unused if customers didn’t trust the algorithm or the company creating the algorithm. This requires, to some degree, establishing a personal relationship, which robots are not capable of yet. People are wired to look to others to confirm they are making the “right decision,” whether it comes to cars or stocks. It’s a lot easier to trust a human than a faceless computer. The human element with regards to technology makes humans so much more useful. Ultimately, human contributions to the field are just as critical as technology’s, if not more.
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