Original article was published on Artificial Intelligence on Medium
Is Machine Learning taking over the Financial Ecosystem?
Real-world applications of Machine Learning in Finance
From the very first transactions being exchanging goods to dealing with cryptocurrency, finance has come a long way. As in any other domain, technology has become an integral part of the finance ecosystem. From an Automated Teller Machine (ATM) to withdraw your cash, to algorithmic trading, the technology around finance has evolved and it keeps evolving rapidly. In this article, I’ll be talking about one of the most (if not the most) influential branches of technology that has been taking over finance, Machine Learning.
What is Machine Learning and why is it important?
Machine Learning is an application of Artificial Intelligence that provides computers the ability to learn from experience without being explicitly programmed.
With the advancement in technology, the rate of data generation has skyrocketed. To give a number, 2.5 quintillions (That’s 18 zeros!) bytes of data are being generated on a daily basis. ‘Big Data’ is the name given to this tremendous amount of data from which a considerable percentage is financial data. It is not possible to analyze and gain insights from big data even with the traditional software tools that we have/had since all of them are rule-based which means they have to be explicitly programmed on what to do. That’s where Machine Learning comes into play. As it was mentioned in the definition, Machine Learning learns from the experience (in this case big data) without being explicitly programmed. Therefore, Machine Learning is the technology that can effectively make use of this immense amount of data that is being generated even as you read this article. That is why Machine Learning stands out and it is important.
Now, let me shed some light on the practical aspects by going through a few of the applications of Machine Learning in the field of finance.
1. Algorithmic Trading
Algorithmic trading is when a machine learning-enabled computer (or a system) trades in stock markets, Forex (Foreign Exchange) markets, etc. on behalf of a human. This will give a competitive edge as Machine Learning algorithms can analyze thousands of data sources simultaneously and execute hundred and thousands of trades per day which exceeds human capabilities. This is called High-Frequence Trading (HFT). Furthermore, a human who is involved in trading can sometimes be sentimental therefore not objective one hundred percent which is not a case in algorithmic trading. ‘Aidyia’, a Hong Kong-based company runs a hedge fund that utilizes Machine Learning to make all stock trade decisions.
2. Portfolio Management
Portfolio management is basically the wealth and investment management of an individual or an organization. In order to do that there’s is a Machine Learning solution, Robo-Advisor. The name is quite misleading as there are no robots involved in this. What a robo-advisor does is advise the client on a portfolio to optimize his/her wealth. Let me simplify this by using an example. Let’s say there is a young person who wants to retire at age 60 with $200,000 in savings. When the client enters his current financial standings and other related details, robo-advisor will provide the client with a range of investment opportunities and financial instruments to use based on clients’ risk preferences in order to reach the goal of having $200,000 at the retiring age.
3. Fraud Detection
While traditional fraud detection systems rely heavily on a robust set of rules Machine Learning based fraud detection systems can learn even to cope up with the new potential security threats. Machine Learning algorithms compare the new transaction (or any other financial activity) against the account history and assess the likelihood of that said transaction being fraudulent. If that likelihood is over a certain percentage that particular transaction is flagged or even automatically denied. The advantage over the human operator is that Machine Learning based system can quickly weigh the transaction details over thousands of data points in previous transactions. Additionally unlike traditional software tools that do not use Machine Learning, ones that do adjust themselves according to the changing habits of the account owner. Also, there is research going on to detect fraudulent financial documents using Natural Language Processing (NLP) which will be revolutionary if succeeds.
4. Assessing credit-worthiness/Insurance Underwriting
Assessing the credit-worthiness of a client is time & money consuming yet a vital task of any financial service providing organization. Separate divisions exist in organizations to get this done. Guess what? Machine Learning is up for the task! Data Scientists train models on thousands of previous customer profiles from which algorithm identifies the underlying trends. Using a well-trained model, a bank or any other financial organization can predict whether a certain customer will pay back the loan, through which the number of non-performing loans can be reduced. From an insurance company’s perspective, they can assess the likelihood of their insurance candidate having to go through a health treatment or meeting with a road accident based on that person’s gender, age and a series of other factors. Based on that prospects the insurance company can underwrite the insurance. ‘Lemonade’, a United States-based insurance company takes a similar Machine Learning-based approach.
5. Sentiment/News Analysis
Sentiment Analysis is going through enormous unstructured data such as photos, audio files, social media posts, blogs, etc. to determine market sentiment. Gaining insights on market sentiment helps with marketing segmentation, strategic marketing, etc. which is a topic for another article. If I stick to finance aspects here, sentiment analysis directly helps with investment decisions. For example, if there’s a negative sentiment towards certain products of a company or the company itself it is not advisable to invest in that particular company cause share prices are likely to go down in the future vice versa. Natural Language Processing (NLP) techniques are highly used in when it comes to analyzing the sentiments.
Let’s get real!
Apart from applications mentioned above, there are more scenarios where Machine Learning is applied in Finance ecosystem. However, apart from a few real-world systems, Machine Learning hasn’t really made its mark in the field of finance yet. Why? Most of the Machine Learning solutions are still in the development stage and Research and Development related to Machine Learning are costly! Another reason would be, traditional organizations are still not ready to automate the process of taking highly critical financial decisions because a lot depends on it. In other words, there is still a lot of room for improvement. That being said, tech giants and reputed universities are investing a lot of resources for research in this area. Google’s ‘Google AI’ program is just one example. With the availability of big data and all this research going on Machine Learning is rapidly evolving. The day when the finance sector is revolutionized by the current wave of advancements in Machine Learning is not far away.