Alan Dix explores AI and Social Justice
Author of Introduction to Artificial Intelligence
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by Ryan Gould
ARIZONA - Today, we’re seeing the positive effects of AI integration into the finance sector. It comes in the form of robo-advisors that make investment recommendations and offer market insight to consumers. We see it in the chatbots that have become an integral channel through which banking customers find answers to inquiries. And of course we see it in AI-driven sales and marketing, which allows finance institutions to personalize services for their customers and better understand their needs by analyzing data sets that become more massive all the time.
It’s at this inflection point where AI meets big-data that we find some exciting solutions in the consumer-protection space. Moreover, financial institutions are utilizing big-data and AI tech to not only offer better protections for consumers, but to beef up their security suites as well. Below we look at three ways in which they are achieving this.
Finance companies have been heavily invested in robotic process automation for some time. The accelerated automation of many finance business processes, such as automating certain transactional activities and high-frequency repetitive tasks, is as Forbes said, a “gateway drug to AI.” Now major firms using AI to continue this trend of rapid automation.
Take JP Morgan Chase, for example. They’ve been implementing AI solutions such as data extraction and document capture in order to better comply with anti-money-laundering regulations. And increased financial stability leaves many institutions with high hopes for the future of AI. As cognitive computing continues to evolve, it will eliminate the need for intermediaries, thus reducing costs for the finance institutions and reducing transaction fees.
For millions of consumers the greatest impediment to enjoying a level playing field in the world of financial services is lack of access. A big part of consumer finance protection is making market and financial services fairer so more consumers can participate. After all, according to the Consumer Financial Protection Bureau the concept of “financial invisibility” affects millions of Americans.
To whit, some 15% of consumers in rural areas over age 25 are classified as credit invisible. The reasons for this are easy to guess, as rural consumers are less likely to rely on digital banking solutions and instead conduct most of their personal and small-business banking and financial transactions in physical branches.
What’s exacerbating the problem for this demographic is the fact that banks everywhere are closing physical branches (especially in rural areas) in favor of transitioning to a solely digital presence. What the modern world lost in physical bank branches has now been replaced with digital lending platforms. And what’s the engine powering these solutions? You guessed it, AI.
The benefit is that machine learning makes it easier to reach these otherwise financially invisible rural consumers. New AI algorithms have improved the accuracy of credit-score calculations, which means financial institutions can expand the credit of consumers previously labeled as credit-poor.
Also helping to eliminate the technological gap is the simple advent of online shopping. First Amazon, and now Walmart, have made e-commerce retail a part of daily life for most people, whether they live in the city or the country. This engagement in the commerce space means that consumers otherwise unaccustomed to online banking solutions are having an easier time entering the digital financial world.
In the finance industry, fraud accounts for billions of dollars in losses every year. And banks are spending around that much on fraud-prevention tactics to just to keep pace with a nefarious criminal element that is ever more technologically savvy. Effective fraud-prevention solutions are worth their weight in gold precisely because they not only protect the interests of financial institutions, but the interests of their account holders as well.
There’s a case to be made that the most effective way to protect banks from fraud, at this moment in time, is with artificial intelligence. IBM, for example, has gone all in on big-data as a central component to fraud prevention, and they’ve developed fintech security solutions accordingly. What their AI engines do is analyze large data sets looking for the anomalies typically associated with fraud. AI can identify common fraud signs like specific attack patterns, tax scams, anomalies in consumer habits, fraudulent transfer activities, and more.
But what AI does that humans can’t do is analyze such huge amounts of data that it can not only identify even the most sophisticated incidents of financial fraud, but anticipate future attacks as well.
While it’s clear that AI will continue to transform financial services for the foreseeable future, there are also inherent risks involved. The heavy reliance on AI, for example, can increase a bank’s digital footprint, thus exposing them to greater cybersecurity threats. Also, while AI excels at processing simple tasks, it has a decidedly tougher time with ethical considerations. So for the time being ethical decisions in finance should be the purview of human beings as opposed to the robots.
Author of Introduction to Artificial Intelligence