The Buck Starts Here: How AI Shapes The Future Of Money – Forbes

For a long time, financial institutions had a buttoned-down reputation when it came to innovative thinking. Nowadays, even the most conventional and risk-averse parts of the economy are looking at Artificial Intelligence, not long ago considered an experimental, bleeding edge technology.

Wall Street is the financial district of New York City. It is the home of the New York Stock Exchange, the world's largest stock exchange by market capitalization of its listed companies.

Nowhere is the change more dramatic than in Financial Services. Companies use AI across the transaction chain, from spotting complicated credit card scams to easing their regulatory burden. There are reasons on both the client and provider side why this is happening now. The financial institutions have more and better data, and tech companies have lower-cost, better performing algorithms.

Many of the most popular uses of this once-risky technology, it seems, are now risk management, in surprising new ways. Here are a few of them:

Fight fraud

Cyber crime cost the global economy as much as $600 billion in 2017, according to the cyber security firm McAfee. A big part of that is online fraud.

AI-based fraud mitigation technologies go through petabytes of data in the blink of an eye, so merchants and financial institutions can spot erratic or suspicious card usage in real-time payments. Visas new suite of AI-based tools, offered to clients without an additional fee or sign-up, evaluate a flowing stream of transactions and rely on self-teaching algorithms, rather than static sample datasets and fixed rules, to evaluate transactions as they happen. Firms like Mastercard, TSYS, and First Data have introduced similar AI-driven fraud tools.

AI finds patterns in massive datasets, making it good for spotting complex money-laundering schemes, like when groups of people or businesses act in a coordinated way to set up accounts and push through transactions, some of which may involve dirty money. Natural-language processing, an AI technique that can detect and determine connections between names or groups of people, is useful for finding detection-avoiding strategies, like false names, altered spellings, and aliases.

AI may also help reduce phone scams, when integrated with voice biometrics that identify a users voice signature. Organizations can more quickly flag and reroute fraudulent phone calls to their appropriate cyber crime teams, before the bad guys have access to an individuals financial account.

Bad actors endlessly seek new ways to commit crimes. Since much of AI continually trains on new data, its an effective way to keep fraud detection models sharp.

Reach the unbanked through better lending

Traditionally, the best way to judge risk in lending is through credit scoring. Systems like FICO take into account data ranging from income and payment history, to savings account balance or past credit utilization. It is only accurate, however, for those whose credit and banking history is well recorded. Hundreds of millions of underbanked people, with poor data histories, get missed. According to oneWorld Bankestimate, about 68% of adults have no credit data aligned with a private bureau, and therefore no credit score.

AI can help. One fintech startup, Lenddo, uses machine learning algorithms to comb through thousands of nontraditional data pointssuch as social media account use, email subject lines, internet browsing, geolocation data, and other behavioral traitsto find patterns that can determine a customers creditworthiness. Similarly, Tala provides microloans to individuals in the emerging markets via a smartphone app. Tala can see the size of the applicants network and support system, a helpful guide to judging risk. Data also reveals whether the applicant pays their bills on time. This type of data has proven more meaningful than traditional credit scoring, enabling Tala to send money to an approved borrowers smartphone in just a few minutes.

Equifax launched a credit scoring system, called NeuroDecision, that fuses the ability of neural networks with traditional methods to evaluate risk predictions, including predictions for consumers with flawed or insufficient credit, while providing reason codes that allow businesses to meet regulatory requirements.

Everybody should have the means to open a bank account. It enables them to become active participants in the economy, save for education, and improve their lives. The expansiveness of AI-based credit monitoring offers the ability to put banking into the hands of tens of millions of people who were falling through the cracks of the traditional banking system.

Personalize interactions

Every financial institution wants to know its customers better. Most have plenty of data about their customers; now they just need to tailor it to their customers needs. This is where AI-driven bots can play a role, and these days that bot is a banker.

One example of this is Erica, the virtual assistant embedded in Bank of Americas mobile app. In just over a year, it has been used by more than 6 million people and has processed 35 million client requests.

Erica combines predictive analytics and natural language to help Bank of America mobile app users view their balances, get credit scores, transfer money between accounts, send money with Zelle, and schedule meetings at financial centers. Customers can interact with Erica in several ways, including voice, texting, or a tap on the phone screen.

The more users who interact with Erica, the more it learns, and the better it becomes at providing help. AI is a strong tool for building personalized relationships.

Enforce financial regulations

Money is a heavily regulated commodity, often subject to complex and extensive regulations designed to define acceptable behavior. In the U.S., federal financial agencies receive guidelines from Congress, and these guidelines are often supplemented by state, local, and industry rules. Compliance is a tough problem, particularly when there is no clean boundary between acceptable and unacceptable behavior based on readily observable factsfor example, the requirement that a bank operate in a safe and sound manner.

If the regulations are more machine readable, AI should be able to assist in compliance, whether its an equity transaction for a trader or something more complex. Predictive machine learning can be a valuable input for financial supervisors identifying issues for further analysis.

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The Buck Starts Here: How AI Shapes The Future Of Money - Forbes