As progressive technologies, personalization, artificial intelligence, and Big Data gain momentum, traditional banking and financial systems undergo a major overhaul.
Technological startups are increasingly claiming the share of the financial market, and artificial intelligence already replaces entire departments.
Every second, Google receives more than 20 search queries about finance and banking. In order to retain the existing and win new audiences, companies in the financial sector should transform digitally in the first place.
In this article I’ll explore 8 machine learning use cases in FinTech and will try to explain why there’ll be no future FinTech without AI and Machine Learning.
Global trends in B2C FinTech
To move from traditional financial structures to technology driven ones, it is necessary to revise the entire business architecture of the organization.
“Financial companies should give users the experience of Instagram or Tinder, rather than the basic, traditional services that exist now,” says Brian Solis, lead analyst at the Altimeter Group.
In 2018, Google analyst and consultant Aram Asatryan outlined 5 global trends of B2C FinTech
1. Removing impediments in customer journey (61%)
More than 60% of bank executives realize that if their technology solution contains a bump for the customer, the chance of losing that customer is very high. According to the results of a large-scale study of 250 financial organizations, there are 5 steps towards better customer focus:
For example, Bank of America has developed a virtual assistant named Erica for its mobile application. In the first three months, a million users turned to Erika! It (or better She) tells the user how much money they have on their account, gives advice on saving money and helps with funds transfer. The virtual assistant recognizes text and voice commands, and its capabilities continue to expand.
2) Using Big Data and machine learning (57%)
More than 50% of modern banks consider Big Data and Machine Learning (ML) to be key FinTech trends of 2019 and beyond. These technologies help automate processes, reduce costs and risks, personalize marketing messages, etc. Some banks invested resources and efforts in these areas a while ago, ahead of their conservative peers.
For example, JPMorgan launched their COIN (Contract Intelligence) application that checks as many loan agreements in just a few seconds as the bank lawyers were able to check in 360,000 hours per year previously.
Let us illustrate the benefits of ML algorithms with a small example. Dash Financial, one of the global FinTech leaders, has recently won the Waters Technology Awards in the category “Best Buy-side algorithmic/DMA product or service”. Dash developed a toolkit aimed at optimizing trading performance and reducing commission costs. The ability to customize the system behavior at any level and in any category is one of the distinguishing features of the product. In conjunction with the zeal to provide full transparency in real time for all operations, the company’s clients receive a full-fledged, investor-oriented trading infrastructure.
A set of Dash touch algorithms has its own customization wizard that allows you to adjust the parameters, behavior and techniques in accordance with the investor’s goals and needs without having to rewrite the source code or work through long development cycles. The results of any implemented change are instantly and clearly displayed in the dashboard – a detailed, real-time web interface that provides access to each component of the application and execution process.
Dash customers get the opportunity to gradually monitor where their requests are sent, as well as the state of the market and the depth of quotations (up to a microsecond in the process of execution). This tool is a key element of algorithmic identification, analysis and further optimization and adaptation to the investor’s goals.
Over the past 10 years, companies have managed to collect a lot of data using a variety of channels and now it’s time to apply algorithms to this array of information. ML algorithms will help companies go beyond traditional reporting and dive deeper into the essence of the information collected. They will also be useful in analytical forecasting, helping companies make decisions instantly. Data collection, identification of patterns, smart classification and machine learning will change the FinTech market entirely in the next five years. And there are no doubts this will happen.
Today, such FinTech segments as stock trading and lending have already integrated machine learning algorithms into their activities to speed up decision making. It is safe to say that the application of ML algorithms by FinTech companies is gaining traction and will definitely reach its highest point in a few years from now.
3) Omni channel approaches (42%)
Today, people are less likely to come to the service provider’s office to get a service and more often do everything on a smartphone. Therefore, it is important for them to instantly receive accurate and comprehensive answers to their questions, and through channels that are most convenient for them. Therefore, financial organizations turn more often to mobile applications, social networks, instant messengers, and voice assistants.
According to Gartner forecasts, 85% of brands’ communication with the consumer will be automated by 2020. For example, Citibank created a chatbot for Facebook Messenger that answers customer questions without delays and in the most natural way as a human assistant would do: “What is my balance?”, “What was my last transaction?”, “How many bonus miles do I have left?” and so on. The bot conducts consultations about personal accounting without taking the client out of the messenger.
4) Increased use of open APIs (35%)
As more banks and financial organizations go through digital transformations, open APIs will be used on a larger scale to enable online payments, funds transfer, encrypted operations, etc.
Below is the matrix of the most popular open APIs used extensively in FinTech.
5) Collaboration with custom FinTech development companies (27%)
As a custom software development provider specialized in FinTech solutions development, 8allocate sees a substantial increase in requests for creating ML algorithms, as well as AI and Blockchain-based solutions from banks and financial organizations. You can see what kind of FinTech solutions are most outsourced to 3rd party vendors in our Portfolio.
8 ways ML takes FinTech to the next maturity level
1) ML is already used in the majority of trading operations
As per the academic research estimates, 50-70% of trading operations in stock markets, 60% in the futures market and more than 50% in the jewelry market are carried out using some sort of AI and ML.
As was noted at the recent conference on financial technologies held at the Michigan School of Law, machine learning and artificial intelligence are used more and more widely in financial data analysis, securities trading, and investment consulting.
2) AI is required to process huge amounts of data
Currently, the amount of digital data doubles every two years.
Artificial intelligence is not just an important, but a vital tool for analyzing the enormous amounts of data generated in the world every minute. The International Data Corporation estimates that the global digital data volume will reach 44 zettabytes (one zettabyte = one trillion gigabytes) by 2020. If you load this data into an iPad Air tablet and put them in a row, they will make a chain six times longer than the distance from Earth to the Moon (in 2013, the data volume was 4.4 ztab – two thirds of the distance from Earth to the Moon).
3) Neural network data allows you to build a strategy for the next trading day
Analysis of data for 1995-2000 and the forecast for 2001 performed using AI have shown that the neural networks can provide up to 150% more information for building future trading strategies compared to the traditional buy-and-hold approaches.
4) ML and AI help detect market manipulation
In May 2017, The Economist published an article reviewing machine learning use cases. Besides mentioning that starting from 2019, financial analysts will have to pass a professional AI exam in order to get a degree, the article makes some interesting conclusions regarding the use of AI in trading.
Here is one of the examples given in this article. Since 2013, Castle Ridge Asset Management, one of the leading asset management companies, has been able to get a gross annual average income of 32% using sophisticated machine learning systems. This high income is partly due to the fact that the AI received data on 24 transactions before they were announced. The ML algorithms revealed these trades using tell-tale signals indicating a low volume of insider trading.
Last year, RoninAI, a startup creating ML algorithms for cryptocurrencies, revealed numerous market manipulations due to the unusual social sentiments.
5) Artificial intelligence works well during the financial meltdown
Studies have shown that AI algorithms can help in making more profitable investment decisions. For example, if they were applied to the components of the S&P 500 index from 1992 to 2015, the stocks portfolio selected by the neural network would show annual double-digit returns, with the greatest profit being achieved during periods of financial turmoil.
Initially, AI algorithms showed the highest annual yield (334%) in 1999, one year before the maximum value of the dot-com bubble. This figure was exceeded in 2000 (the annual yield of 545%), when the dotcom bubble burst and technology companies lost billions of market capitalization.
The largest rebound occurred in 2008, when the annual profit of 681% fell during the peak of the financial crisis.
In particular, the largest decline (over 100%) was noted in October 2008, one month after the collapse of Lehman Brothers, and this is the strongest decline in the period from December 1992 to October 2015. Finally, in October 2011, a positive income of 35% was achieved, which coincided with the peak of the crisis of the European debt market.
Thus, it’ll be right to conclude that machine learning algorithms are particularly effective during periods of strong market shocks.
6) Artificial Intelligence successfully predicts prices for all types of traditional and new asset classes
Many studies show that AI can significantly outperform existing trading strategies, such as buy-and-hold, in a wide range of asset classes.
Researchers believe that machine learning algorithms generate a much higher absolute income combined with a higher Sharpe ratio (investment portfolio performance indicator).
A study by Lukas Schulze-Roebbecke proved that artificial neural networks can show significantly better results with a lower standard deviation for copper futures.
Another study conducted by Jinxing Han Gould from the University of Oklahoma showed that Forex market indices can be predicted with a neural network that uses backpropagation techniques to get the maximum profit.
An interesting article published by Emerald Journal outlines the reasons why advanced approaches such as artificial neural networks and fuzzy logic are more effective than the traditional ones.
The article presents a table summarizing the strengths of some machine learning algorithms used as advanced valuation methods for real estate objects.
7) Profitability as a result of AI adoption significantly exceeds the average level of market profitability
Magnus Eric Hvass Pedersen, a staff member at the University of Southampton, conducted a study on “Using artificial intelligence for long-term investment” in January 2016. The purpose of the study was to determine the optimal composition of the portfolio when applying AI for long-term investment. The study showed that from 1995 to 2015, his AI model surpassed the S&P 500 index by an average of about 18% per year. It worked particularly well in a period when stocks were either greatly overvalued, like during the apogee of the dot-com bubble in 2000, or undervalued, like during financial crises.
8) Hedge funds that use AI deem more effective than the traditional funds
The use of artificial intelligence in the hedge fund industry is still at an early stage: today, some hedge fund managers are turning to AI as an additional tool, continuing to use intuitive methods in investment and risk management. At the same time, many funds are already using the machine management of technical aspects of both the actual trading and risk management with minimal involvement of fund managers.
To conclude, machine learning and artificial intelligence will help increase work efficiency and reduce operating costs as far as FinTech goes. A personalized and multi-channel approach can improve the customer experience. And if financial technologies are developing too slowly, it’s because there are still people behind the processes, not machines.