Estimating Financial Risk Using AI in FinTech

Artificial Intelligence (AI) is gaining ground in a broad spectrum of industries, with its potential in the financial services sector particularly at a tipping point. As part of the growing subsector known as FinTech (Financial Technology), which involves using algorithms and other technology tools to process data toward improved financial activity, AI holds great promise in enhancing risk estimation among investors and the markets.

In his European Business Review article, “AI and Digital Resources in Fintech: Creating an evolutionary analytic platform for ‘risk’ estimation,” Stephan Kudyba, associate professor of business analytics and MIS at NJIT’s Martin Tuchman School of Management, explores the ability of AI to mine vast amounts of data and subsequently identify investor attributes and financial market asset classes tied to risk.

“Although forms of AI have been applied in various sectors of the financial markets, current applications are introducing more robust utilization,” Kudyba said. “Most recent AI initiatives are addressing some of the more structured/methodized applications of wealth/financial advisory that are based on more routine processes — that is, connecting investors with portfolio suggestions. Given its newness, there is still ample room for improvement, however. The processing power of computers is making its utilization much more widespread, and the abundance of descriptive parameters renders AI initiatives for financial wealth management as an evolving space where boundaries of risk estimation and matching will be pushed.”

He writes in his article that behavioral attributes pulled from investor activities — reactions to market volatility and general financial and consumption patterns, for example — can serve as valuable data resources for estimating risk. Such attributes are supplemented by digital records of consumers’ direct activities with a variety of technologies. All the data taken together, he suggests, can enable AI analysis to potentially estimate a more objective and detailed assessment of individual risk.

“The area of descriptive attributes of investors as they react to market moves may provide the most insightful new information regarding the estimation of risk profiles,” Kudyba noted.

Still, despite the value potential of AI in FinTech, Kudyba maintains that the optimal platform for risk estimation is a combination of financial experts, more traditional quantitative-based methods and AI-augmented decision support, along with data scientists who can unravel the data and create models.

“The wisdom of knowledge-rich financial experts is tough to beat. The human touch will be needed to oversee how AI is deployed, how to best leverage its results and to monitor how effective it is,” he said. “The real test will be AI’s performance in extremely volatile markets or with significant changes in market characteristics, where investors often need the wisdom of the true professional.”

And as for concerns of worker displacement stemming from AI in FinTech, Kudyba believes there will be an increased demand for “knowledge workers” with pure analytic and analytic management skills when it comes to the evolving character of risk assessment and wealth and financial market management.

“AI may displace more routine-based attributes of labor, but given the ongoing creation of new data and new technologies, we’ll need those that can apply the pure analytics of AI and those who know where and when to unleash its power,” he concluded. “Remember, AI, data and new technologies are causing a digital transformation, making some processes and sectors obsolete, but they are also creating a multitude of new markets and opportunities.”