Introduction
The financial industry – which finds its origin in the 15th century – is known for its bureaucracy and its reluctance to the implementation of new innovations. However, recent technologies – such as improved communication devices, word-wide internet accessibility, and access to large digital databases – have had a profound effect on the operational principles of the banking industry, initiating a period of digitalization and online banking services.
In addition, the drastic increase in computing power and the widescale availability of large databases have prospered the development of powerful new techniques such as artificial intelligence and machine learning. This article will provide an overview of the working principles of machine learning and artificial intelligence and their potential use-cases within the financial industry.
What is Machine Learning?
Machine Leaning – closely related to and usually considered as a sub-field of Artificial Intelligence – is the automated process of detecting usable patterns within data by means of clever computer software. The detection of these patterns is performed with the help of machine learning algorithms, which are specifically designed to be able to deal with complex and large data sets. Nowadays, a vast number of machine learning algorithms are available, each with its own unique characteristics and properties.
Due to the digitalization process and the exponential increase in online banking services, the amount of available financial data – regarding stock prices, consumer spending patterns, and financial indicators – has skyrocketed. Since artificial intelligence and machine learning are heavily reliant on the availability of data, these technologies have become increasingly more relevant within all aspects of banking and financial services. In what follows, an overview will be provided regarding the opportunities that the use of artificial intelligence poses to the key players within the financial industry.
- Artificial Intelligence for Credit Decisions and Risk Management
Customer risk assessment and credit decision making are important activities that may have serious consequences for the bottom line of financial institutions. Currently, it is common practice to utilize simple rule-based assessment tools – in combination with superficial financial indicators – to decide whether a customer is eligible for obtaining a loan. However, such rule-bases systems lack the capability of analyzing complex data and subtle consumer patterns which – if aggregated – may have a significant impact on the credit rating assessment.
Currently, the total US consumer debt – including mortgages, auto loans, credit card loans, and student loans – amount to a total of 13.86 trillion dollars, allowing every small improvement to have a significant effect on the financial institution’s return on loans. In its core, the lending business – which allows the collections of large amounts of data – is a data-driven business, making it naturally suited for the implementation of artificial intelligence and machine learning to aid in credit decision making.
Indeed, machine learning models are able to significantly increase the accuracy of risk assessment and credit making decision tools due to their ability to find subtle patterns within large amounts of data. In addition, machine learning models are able to deal with alternative data sources – including customer’s geolocation, internet browsing, or social media activity – which may provide valuable information when determining a customer’s credit score and allowing more accurate predictions.
At last, implementing a data-driven decision-making strategy eliminates all forms of subjectivity from the credit rating equation. This may reduce the number of legal disputes – initiated by rejected customers who feel mistreated – which could potentially have harmed the financial institution’s reputation.
- Artificial Intelligence for Security Trading and Investment Decision Making
Before the introduction of the computer, the field of security trading and investments was subdivided into two fields: fundamental analysis and technical analysis. Within the field of fundamental analysis, the investor’s goal was to acquire company shares at a lower price with respect to its intrinsic value. In comparison, technical analysis rejects the fundamental business properties and solely relies on stock chart reading to determine lucrative buy and selling points.
However, increasingly more processing power has allowed the software to take into account a wide variety of different indicators – including fundamental business properties, technical indicators, and media coverage – and respond to changing market conditions within fractions of a second. These new trading techniques – which are heavily reliant on machine learning and artificial intelligence – are often referred to as algorithmic or high-frequency trading. By feeding machine learning models with large quantities of economic and financial data – as well as geopolitical indicators, industry-specific data, and sentiment data –, such algorithms are able to create sophisticated investment strategies that are able to detect interesting trading opportunities.
According to Deltec Bank, “More and more people are relying on such algorithms to eliminate the emotional factor out of the investment equation and, subsequently, hope to improve their investment returns.” This is illustrated by the steep rise in assets under management by Quantitative Hedge Funds (Quant Hedge Funds), which is the collective name for hedge funds that rely upon algorithmic or systematic strategies for implementing trading decisions. Whereas Quant Hedge Funds managed around 500 billion dollars in the year 2010, this figure has almost doubled to a total of 940 billion dollars in assets by the year 2017.
- Robot Process Automation
Financial institutions invest a considerable amount of money into pay employees for executing highly repetitive time-consuming jobs such as data extraction, complying with Know Your Customer (KYC) regulations, and retrieving documents. Whereas these business operations do provide value to financial institutions, the highly educated employees performing such repetitive tasks could be allocated to carry out more profitable, cognitive tasks.
Artificial Intelligence and machine learning – capable of detecting characters and on-screen pictograms – have enabled the development of intelligent software that is able to automate most of these highly repetitive tasks. Commonly referred to as Robot Process Automation (RPA), this software can be initiated during closing time or lunchbreak, alleviating employees from carrying out repetitive tasks, increasing efficiency, and reducing payroll due to reduced workforces.
Large financial institutions – including JP Morgan Chase and Ernst & Young – have already invested large sums in the development of intelligent RPA-systems. These institutions report an obtained cost-reduction of 50% to 70% for highly repetitive tasks such as data extraction and document registration. In addition, RPA implementations allow employees to focus on challenging cognitive tasks rather than repetitive tasks, therefore increasing employee satisfaction rates.
Conclusion
Whereas many industries are in the midst of implementing innovative technologies to enhance their business environment, the financial and banking industry are still reliant on cumbersome, long-standing processes. However, the use-cases provided in this article show that there exist real opportunities for financial institutions to reap the benefits of new, data-driven processes empowered by artificial intelligence and machine learning. It is certain that, in the future, artificial intelligence and machine learning will become intertwined with the financial industry, resulting in more data-driven decision making, increased efficiency, and improved customer experiences.
Disclaimer: The author of this text, Robin Trehan, has an Undergraduate degree in economics, Masters in international business and finance and MBA in electronic business. Trehan is Senior VP at Deltec International www.deltecbank.com. The views, thoughts, and opinions expressed in this text are solely the views of the author, and not necessarily reflecting the views of Deltec International Group, its subsidiaries and/or employees.
About Deltec Bank
Headquartered in The Bahamas, Deltec is an independent financial services group that delivers bespoke solutions to meet clients’ unique needs. The Deltec group of companies includes Deltec Bank & Trust Limited, Deltec Fund Services Limited, and Deltec Investment Advisers Limited, Deltec Securities Ltd. and Long Cay Captive Management.
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