Introduction to Machine Learning in Finance
The global financial market is undergoing a structural change. Various factors such as consumer preferences, new laws, and technological advances are driving this change.
All of these factors affect the size of the financial data explosion and the work required to write, process, and use it.
As standard techniques, traditional analytical methods, and mathematical methods continue to fail, artificial intelligence has a major impact on these processes in financial and AI consulting.
The new dynamics of the world requires a radical change in the thinking and level of emergence of economic scientists around the world.
This pressure must take place at an unprecedented rate, bringing profitable companies that have not been able to take full advantage of them.
That is why the World Economic Forum is driving the effects of AI and machine learning on the new Financial Model Model 3 exaggerated, real success, and fears.
In this article, we discuss seven cases of the use of AI financially that help expands this global transition. We will include some of their benefits and hopefully eliminate some misconceptions about artificial intelligence, its purpose, and the real-life ROI of the financial sector.
Before we proceed there’s a question that I would like to answer for the readers “What is Machine Learning for the Finance Sector?”
What is Machine Learning for the Finance Sector?
To answer this question, one has to answer the basic and fundamental question which asks: What is Machine Learning?
Well, according to all major sources “Machine learning is an application of artificial intelligence (AI) that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed.
Machine learning focuses on the development of computer programs that can access data and use it to learn for themselves.”
So now that we know what is Machine learning, let’s answer the main question, “What is Machine Learning for the Finance Sector?”
Machine learning for the Finance Sector is the utilization of a variety of techniques to intelligently handle large and complex volumes of information such that it helps the financial organization make financial gains in the short as well as in the long run of things.
Now, getting along with the article, let’s see the 7 Best Examples of Machine Learning Applications in Finance Sector:-
“Robo-advisor” is a term that had actually been unheard of in the last five years, but is now a common term amongst the elites of the financial markets.
Although the term is misleading and does not include robots, instead, Robo-advisors like Betterment are essentially a set of algorithms that have been designed to measure financial portfolio goals to handle consumer risks.
Customers record their goals (for example, at age 65, they retire at a cost of $ 250,000.00), years, income and current financial assets.
The advisor (commonly referred to as the “treasurer”) then transfers the investment to asset classes and financial instruments to serve the purposes of the consumer.
The system calculates changes in users’ intentions and real-time changes in the market, always aiming to find the best fit for the user’s initial goals.
Robo-Advisors have received a huge deduction for millennial clients who do not need a physical advisor to feel comfortable about investing and are unable to guarantee the fees paid by human advisors.
Gone are the days when there used to be a checklist to check whether a person is eligible for a certain loan or insurance scheme.
These are modern times and today things are done in a faster way. Loan and Insurance underwriting agencies today use a complex set of applications and algorithms to check the merit of the application of those applying.
Using ML, underlying trends of defaulting, and forgery in the case of insurance can be detected which can then update the database and the machine can learn for further future use. This way ML ends up saving millions of dollars for Loans and Insurance providers.
Algorithmic trading has been around essentially since the 1970s and involves the use of complex AI-based systems that make extremely fast trading decisions.
Algorithmic Trading is known to make hundreds of millions of decisions a day hence the term High-Frequency Trading which is also a subset of Algorithmic Trading.
Although most of the hedge funds, as well as Financial Institutions, are not keen enough to discuss their trading strategies, it is widely believed that Machine Learning and Deep Learning with a combination of AI has a great role to play in the same.
Another important usage of Machine Learning in the Financial Sector, Fraud Detection is one major use of ML done across Financial Institutions across the world.
If you combine more accessible computing power with a more accessible internet, you get a dangerous combination that can easily be used by fraudsters and hackers.
While previously Financial Fraud Detection was anything but a set of complex rules, in the modern world it has evolved to become something more than just a checklist.
Modern Fraud Detection software involves detecting unique behaviors or anomalies using Machine Learning and implementing a set of pre-destined security measures as soon as any discrepancy is detected in the system. Thus the modern fraud detection more of Prevention than a cure.
Sentiments and News Analysis has become a big part of day to day working even for financial markets in this age of social media.
Take the example of Stock Markets in concerns with the recent ongoing Coronavirus Pandemic in the world. Stock Markets are never shy to react to any global news that might affect the companies that are listed on the exchange.
This is the reason why ML-based Sentiment/News Analysis is becoming important for such financial structures that depend on day to day occurrences in the world so that they have a way to predict what to expect from the market as a whole.
Sales and Recommendations of Financial Products:
There are applications today for the sale of automated financial products, some of which do not include machine learning (but, other law-based programs).
Robo-Advisor can propose a portfolio change and many insurance suggestion sites can use AI to target a specific auto or home insurance program.
In the future, personalized and measurable personal assistant apps will be seen (by Millenials only) as more reliable, purposeful, and reliable than personalized advisors.
While Amazon and Netflix can recommend books and movies better than any living “human” expert, ongoing discussions with personal finance assistants can do the same with financial products, as we start with the insurance industry.
Usernames, passwords, and security queries may not be a sign of user safety within five years. Banking and financial security is a high-profile game (rather than releasing your bank account information to a small group of strangers, and for good reason, you can enter your Facebook world).
In addition to malicious detection applications currently being developed and used in fraud, future security measures may require facial recognition, voice recognition, or other biometric information.
I hope you got to know the various examples of machine learning applications for finance industry and how useful it is for people.