3 Ways AI Is Driving the Future of Finance

3 Ways AI Is Driving the Future of Finance

Colocation data centers are the engines for traders - and exchanges - all over the world.

Every facet of financial management has been reshaped by data. From consumer banking services, to commercial financing, to trading, to bookkeeping—all of it is driven by the movement of digital information. 

And where there’s data, there’s IT infrastructure. It’s no secret, for instance, that data centers are the engines of modern-day Wall Street. Or that every time you log into your online banking portal, you’re indirectly interfacing with a server in a data center. Simply put, there’s no modern finance without the data center. 

Equally—if not more—important, there is no future of finance without adaptable data center services. Artificial intelligence and machine learning are as resource-intensive as they are transformative. They parse near-cosmic quantities of financial information in seconds, and the conclusions they draw influence transactions on a magnitude that would make King Midas weak at the knees—we’re talking tens of trillions of dollars. Machine learning, for its part, can actually improve upon its own performance over time. 

This caliber of intelligence demands extraordinarily reliable data center infrastructure with redundant power, highly efficient cooling, PCI-compliant security, carrier-neutral connectivity and much more. 

To give you a better idea of what’s at stake, let’s review a few of the finance-related functions that have been or are in the process of being reimagined by AI:

High-frequency trading

No one can reliably predict the future, but AI algorithms can come close. They’re much better than we  humans at factoring in the plethora of variables that influence market patterns, and they are impervious to logical bias errors and emotional decision-making. Wall Street firms rely heavily on computer-driven data analysis to make automated decisions based on near real-time data flows. In the game of electronic trading, advantages over the competition are measured in mere milliseconds.  

Accordingly, many traders and investors choose to situate their data centers as close to the New York Stock Exchange’s data centers as possible. It’s just a little bit less distance light has to travel. And obviously, the faster traders can analyze data, the more quickly they can make decisions. So, not only do they need close to zero latency for their AI-based computer models, but they also need Herculean processing power. The onus here is partly on the data center’s facility team: They need to ensure high-speed connectivity with networks, and reliable power and cooling infrastructure capable of sustaining high-density racks running  AI and ML workloads. A failure on any of these fronts could lead to millions if not billions of dollars lost in missed opportunities.

Credit risk analysis

Risk is factored into any financial transaction that involves lending money or assets—whether it’s consumer or commercial loans, revolving lines of credit or equipment leasing. The process of assessing whether a certain customer qualifies for a particular credit card or loan, and at what interest rate, requires an immense amount of number-crunching—so does launching a financial product, such as a new airlines reward credit card; how can a bank be sure it won’t lose money on its miles program? 

It all boils down to risk analysis—poring over massive amounts of financial and non-financial information to determine whether particular applicants will make suitable customers. Machine-learning algorithms can do this much faster and more effectively than humans can, and far more accurately than traditional, linear statistical models. They can, for all intents and purposes, help predict which customers are likelier to default on loans based on non-linear relationships and adjust those projections over time based on what it learns. According to Forbes, more credit unions will be leveraging machine learning in 2020 to drive up the rate of loan approvals without introducing more risk.

Fraud Detection

Insurance companies, consumer banks, credit unions and lenders take sources of fraud very seriously. That’s why many financial services are increasing their reliance on AI to monitor for, and prevent, everything from wire fraud to hijacked bank accounts, identity theft, money laundering, insurance fraud and more. In many cases, the same digital conveniences that enable the faster movement of money and real-time transactions also create real-time risks. The more immediate a transaction, the more quickly you must be able to determine its validity.  

In fact, increased automation itself will introduce new liabilities to mitigate. Deloitte argued that, as insurance companies increasingly automate claims management, more people will feel compelled to attempt insurance fraud. And curiously, the best way to solve this potential issue is with more automation—e.g., AI and ML-powered fraud detection resources.

The big picture: AI is the new foundation for financial services

Low-latency, high-frequency trading systems, credit-risk assessments, insurance claims processing, financial robo-advisors, and the fraud detection systems that help protect these digital services are all increasingly powered by AI. 

At the end of the day, AI is powered by IT equipment that lives in data centers. If AI is the foundation for financial services, the data center is the foundation for AI. Ergo, the future of AI-powered financial services starts with more reliable, cost-effective and resilient data center services.

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