How artificial intelligence is revitalizing the world of finance

“Ignoring technological change in a financial system based upon technology is like a mouse starving to death because someone moved their cheese.” — Chris Skinner. 

The word “fintech” — the cutting-edge fusion of finance and technology — has gained more popularity in the last few years than ever before. Artificial intelligence is transforming the field of finance as we know it through advancements in risk assessment, fraud detection and management, algorithmic trading, robo-advisory, and more.

In the risk assessment sector, new algorithms are being developed to assess whether someone is eligible for a loan. These algorithms are deeply rooted in machine learning, so the technology learns over time. Traditional analysts are more prone to error and bias than Al and machine learning, which employ clear-cut and logical thinking matched with consistent stamina to analyze copious amounts of data. Plus, the obvious factor: these technologies can go through cases considerably faster than humans. These same machine learning algorithms can be used to assess the eligibility of someone for a credit card and even postulate personalized interest rates. This customized judgment is more advanced and comprehensive than traditional credit scoring. This is because it is done through evaluating thousands of pieces of relevant data from a person which allows AI to make a more well-informed decision. 

Algorithmic trading is the process in which instantaneous decisions are made to carry out trades based upon detectable patterns. High-speed and devoid of emotional skews, AI has dominated the trading field. In fact, 80 percent of all United States trade operations are facilitated by AI algorithms today. Furthermore, these new systems are growing stronger through utilizing natural language processing mechanisms, a branch of AI dealing with teaching computers to comprehend and analyze human-written text. Accordingly, these algorithms are now starting to sift through alternative data sources like useful information in news articles and other online sites to make rational trading decisions. 

In fact, 80 percent of all United States trade operations are facilitated by AI algorithms today.

E-criminals who attempted and succeeded in credit card fraud a few years ago would have virtually no success today. AI can recognize and report transactions made in locations a client usually isn’t at and withhold abnormal amounts of money that are out of the client’s specific spending zone. Plus, because these machine learning algorithms are constantly learning, if a client tells the service that it has incorrectly detected fraud for a normal transaction they made, it will take that information into account and make even more accurate decisions when examining future transactions. The powerful, recurring idea here is that machine learning can never move backward — it will only get smarter and smarter. 

Robo-advisory, a group of automatic investment advisory services, has grown relevant in forecasting the investment and wealth management market. Employing sophisticated algorithms to formulate investment decisions, robo-advisor services weigh a client’s personal preferences like risk appetite and financial goals against unpredictable forces like market volatility. Moreover, these services do not require financial knowledge to use, encouraging the self-management of finances. 

The powerful, recurring idea here is that machine learning can never move backward — it will only get smarter and smarter. 

As Skinner implied, fintech is here — and it is here to stay. In addition to these AI innovations, there are plenty more already in use and others in the making. In fact, financial institutions are in the process of developing quantum algorithms. Quantum computers essentially carry out calculations based on the probability of an object’s state in advance, giving them the ability to work with significantly more data compared to traditional computers. They process and analyze substantial and unstructured financial data at unthinkable speeds, performing a task in as little as three minutes that would take a supercomputer thousands of years. Quantum computers will especially be of use in areas where algorithms are driven by live data streams and involve extensive random noise like real-time equity pricing. 

There is no doubt that new advancements in financial technology are going to keep coming — the question is, are businesses and consumers ready to keep up?