Artificial Intelligence and Machine Learning are two of the latest technologies that are poised to transform the face of this world in almost every sphere, including various business sectors as well. And the arena of finance is no exception to this. In a way, the leaders of the financial world are gradually realizing that whether it’s about assessing credit risks or enhancing network security, it is actually going to prove more beneficial in future to train the machine systems themselves take care of things rather than employing human resources for operating them.
Explained in simple terms, machine-learning involves a technological framework that banks upon a combination of complicated algorithms that analyze data, process information and draw useful inferences on the basis of insights gained. Though the financial enterprises tend to be more cautious about adopting newer technologies given the high risks involved in their business processes, various technological giants such as Amazon and Google have already begun deploying machine-learning into their operations for quite some time now. There are many ways in which machine-learning may turn highly useful for the financial enterprises. Let’s have a look upon some of them.
Evaluating Financial Risks
One of the foremost concerns for any financial firm anywhere in the world is related to the potential risks involved in its core business processes. Any financial activity needs to be duly protected against any possible risks in advance, with the help of thorough analysis. For example, a financial enterprise normally banks upon a team of efficient and expert risk analyzers who leverage the loan and credit-related data pertaining to any potential client firm accumulated through various sources, and then offer a risk report to the company.
Using machine-learning solutions, the same financial company may rely upon smart analytics and the observations made by enhanced data analytics tools and Artificial Intelligence tools, to gain an even deeper and insightful assessment of the scenario. When used, these insights may help the company a great deal in minimizing its financial risks.
Forecasting Financial Investments
The world of financial investments has gained momentum across the individuals and enterprises belonging to all market segments across the globe, especially during the last few decades, thanks to the arrival of modern telecommunication technologies. As the volume of investments being made in the stock and future options is increasing, especially small and medium investors are getting more concerned about the safety of their investments and looking for sensible and trustworthy forecasts. Machine-learning may turn out to be a helpful tool in this regards as well.
For example, both small and big investors are using predetermined functions of online financial platforms for enabling automated purchases and sales of stocks and other financial products. Up to some extent, these platforms are able to suggest the investors what financial decisions they must take in accordance with the future possibilities, still, almost always the investors need a dedicated human advisor to tell them what actually needs to be done. But the advanced machine-learning based solutions take the things way ahead. They are actually able to assess and analyze the past market data, current business scenario and possible trends, to predict future market movements to a much better degree of accuracy than those human analysts.
Enhancing Financial Security
Financial fraud is a matter of grave concern for both the investors and financial companies alike, across the globe. Investors do need an assurance from their capital investment enterprises that their funds are going to remain secured, while those companies in turn have a serious responsibility to ensure that the hard-earned funds of their clients are safe. Usually, teams of highly capable data and cyber security experts are hired and equipped with high-end security tools and solutions to maintain that level of data and funds protection. Still, machine-learning may transform the scenario completely, if used properly.
For example, a stock management company offering its services to individual clients making small investments may spend a huge sum upon hiring dedicated teams of security experts and purchasing annual licenses for enhanced security solution sets, yet the fact remains that the cyber mafia and hackers to work relentlessly upon upgrading their skills and making their attacks even more lethal. If the same company spends those funds in developing a machine-learning based infrastructure that automatically updates itself with latest discrepancies and weaknesses found in data security platforms, and the technologies to combat them, the system might evolve itself into a robust and powerful ever-expanding financial security stonewall for the enterprise.
On the whole, we might conclude that though traditionally the financial enterprises worldwide have been relying upon a combination of human experts and technological solutions to make things easier for them, the trends clearly suggest that adopting an entirely Artificial Intelligence and machine-learning based solutions is going to be the smart order of the day for them in future.