AI and Finance: The Role that Machine Learning Will Play in the Finance Sector
Devashish Shrestha, Kathmandu, Nepal
Machine Learning is pervading the world of finance obvious by how “financial institutions around the world are making large-scale investments in AI (with) the trend (not) expected to slow down anytime soon” with “ the World Economic Forum (WEF) project(ing) this number to reach US$10 billion by 2020” as mentioned in “AI to Transform the Finance Landscape: WEF Report.” The stakes are pretty high and there is a lot to gain from machine learning. Certain areas of the finance sector where machine learning is making an impact is process automation, intelligent insight generation, portfolio management, high-frequency trading, risk management, and fraud detection. As all of these form the major pillars of finance, machine learning will play a huge role in revamping the finance sector.
Automation is becoming an important trend in many industries and the world of finance being no exception. In any industry, human potential should be used in tackling bigger problems and addressing larger concerns than simply fulfilling mundane tasks that are highly repetitive in nature. It is estimated that “finance professionals are spending just 17 percent of their time on strategic activities, with a lack of automation serving as the culprit for much of this inefficiency” as stated in “How Machine Learning Can Optimize Finance Processes.” However, automation that is made possible by machine learning is “eliminating manual administrative work entirely” and allowing corporate and “finance professionals” to focus more on handling strategic tasks and work that is “more rewarding and of higher value.” A growing number of “institutions are utilizing automated processes,” in bringing forth a wave of changes in the “banking, finance and insurance,” industry, as mentioned in the article, “The Future is Here: AI and Machine Learning In Financial Services.” An example would be chatbots that are replacing “live customer service representatives, saving a great deal of both time and money within customer management” as mentioned in the article. Automation is made possible through AI systems powered by machine learning algorithms that learn from “data before automating certain features” said “The Effects of Artificial Intelligence on the Finance Industry.” Therefore, automation can increase efficiency by letting humans make more effective use of their time by putting their attention on more meaningful endeavors like driving innovation and devising successful business strategies.
Financial advising is another area where machine learning is making a huge impact. Financial advisors take certain factors into account like a client “financial targets, assets, age, and income” before they provide recommendations in selecting the appropriate stock or investment option as mentioned in the article “Machine Learnings Impact on the Finance Industry” on Medium. Machine learning algorithms possess the ability to carry out the same tasks with much more efficiency, effectiveness, and accuracy than human advisors. This plays a big role in portfolio management in terms of diversifying a client’s portfolio by assessing the risks between different assets and letting them choose the right combination of assets to invest in as stated by Yves-Laurent Kom Samo, Founder & CEO, Pit.AI Technologies during the Rise of Machine Learning in Asset Management Conference, in the article “Conference Recap: The Rise of Machine Learning in Asset Management.” They are based on “new statistical learning techniques that are more finance focused and not” dependent “on old models or beliefs” as further state
Trading is an essential activity in the world of finance. It was traditionally done by humans but now can be carried out by computer systems of incredible power and capacity in what is known as “high-frequency trading” as mentioned in the article “Machine Learnings Impact on the Finance Industry.” These computers can perform fast-paced transactions and “generate profits at significantly higher” rates than “human traders”. They can take into account multiple factors like timing, price, and quantity and leverage them much more effectively to produce considerably better results while executing trade operations. The models they generate are based on machine learning algorithms that can accurately predict stock prices, analyze trends, make recommendations and even make decisions on behalf of humans. But unlike humans, machine learning algorithms powered decisions are more informed and free from bias as they rely more on facts and figures than raw instincts. So they are useful in “avoiding high-risk margins and inefficiencies” caused by human traders especially in “overheated markets.”
Due to the advent of the internet, banking and trading activities are carried out online. The internet is always vulnerable to cyber attacks from hackers and spammers attempting to steal private and confidential information of clients and use it for their own benefit. Say, if your bank login details are uncovered by cybercriminals, they can use it to transfer money from your account to theirs or use your credit card to make purchases. So, it is of utmost importance to notice such activities and stop them immediately before more damage can be done, which is where fraud detection comes into play. “Fraud detection is a top priority for financial institutions” and “is quite crucial for banks in order to gain the credibility of customers as the article Machine Learning’s Impact on the Finance Industry” further mentions. Machine learning algorithms can spot unusual spikes or irregularities in real-time data and alert the banks of “potential fraudulent activity” in what is called “anomaly detection.” An example of this would be detecting credit card fraud where the algorithm notices anomalies in the transactions of a client that might be quite different from his past spending behavior as mentioned in the article “5 Ways AI is Transforming the Finance Industry. Hence, machine learning is making banking more secure and reliable.
The area of finance where machine learning is probably most useful is in intelligent insights generation. “The Effects of Artificial Intelligence on the Finance Industry” the article states that Artificial Intelligence systems can crack into “existing and newer data to look for trends and patterns,” as well as “efficiently examine raw data to excavate important information.” Machine Learning can “fundamentally transform financial forecasting,” as mentioned in “Seeing the Future More Clearly: How Machine Learning Can Transform the Financial Forecasting Process.” They can generate models that learn from data and produce more accurate forecasts over time at faster speeds, “enhance the volume and type of data that can be used” in making forecasts, automate basic forecasting tasks and letting analysts focus on more important issues that may impact a business.
Machine learning can even be an effective remedy for preventing any sort of financial calamity by changing “the way financial institutions approach tighter compliance requirements and risk management” as mentioned in “Managing Financial Risk With Machine Learning.” For instance, machine learning algorithms are used to come up with models that can determine the creditworthiness of clients with great accuracy. The reason behind the “2008–09 financial crisis was the poor credit scoring models” used by banks to give out loans to clients that failed to take into account the possible risks associated with it, as the article further argues. Hence machine learning based models are “designed and deployed in the large banking system(s) to approve or deny loans.”
It’s clear that machine learning is playing an instrumental role in transforming major areas of finance. Its helping clients become wise investors, efficiently executing a trade at a break-neck speed with minimal human intervention, winning the trust of customers by creating a more secure banking environment, mining huge amounts of data in generating useful strategic insights for companies, automating frequently performed tasks and letting finance professionals focus on higher level objectives. It’s helping reduce risks by coming up with better models that can more accurately determine the creditworthiness of clients and prevent the finance sector from collapse. Whether it be at the level of benefiting clients and finance professionals, corporations and institutions or the economy of nations as a whole, machine learning is surely going to change the world of finance in a major way.
