The continued increase in digital transactions in financial services means banks, credit unions, and lending institutions shows no signs of slowing: a 76% share of new financial accounts were opened via a digital channel in the last year, according to the PYMNTS and NCR Digital-First Banking Tracker. And with this increase comes new opportunities for bad actors, with financial services having a high and growing rate of fraud attempts.
How do financial service companies not only meet the fraud threats of today, but stay ahead of them? Financial services organizations can futureproof themselves against fraud by leveraging powerful AI. Utilizing AI to fight fraud also comes with the added benefit of a safer, more secure, and in most cases, streamlined user experience.
Commons Forms of Financial Services Fraud
Fraud has a huge bottom-line impact. And the impact of fraud goes far beyond financial repercussions. These fraud losses can damage both a brand’s reputation as well as its ability to retain customers. Here are some common forms of fraud in financial services:
Synthetic Identity Fraud: Difficult to stop, synthetic ID fraud occurs when a bad actor uses a combination of real and falsified information to create a new, synthetic, “Frankenstein” identity to open accounts and commit fraud. Synthetic ID fraud is of special concern to consumer facing FinTech companies offering lending services.
Card Not Present Fraud: Card Not Present (CNP) fraud occurs via the unauthorized use of a payment card when the cardholder does not physically present the card at the time of the transaction. Bad actors commit this fraud by gaining access to the information on the card’s magnetic strip, the payment card number, the card’s three-digit security code, and the cardholder’s name and address. About 47% of attempted payment fraud stemmed from CNP transactions last year.
Business Identity Theft: Up over 113% year over year, Business Identity Theft occurs when bad actors pose as representatives of an organization to get cash, credit, loans, resources, or equipment through fraudulent means, leaving a business with the debt.
Bot Attacks: Automation is not just a source of efficiency on the operational side, it’s a tool used by fraudsters. Automation can be used to create and submit hundreds of applications for card fraud, with bots used to access data and uncover card details.
Account Takeovers (ATO): In ATO, a bad actor acquires a customer’s account credentials through phishing or data breaches. Once logged in, this bad actor can change a customer’s account and personal info, fraudulently purchase a new device, and steal a customer’s identity to set up new accounts elsewhere. In financial services, 61% of attempted fraud attacks through mobile apps are account takeover attempts.
Why Use AI to Fight Fraud in Financial Services?
With fraud becoming more sophisticated, human analysts have limits on how fast and how accurate they can be with both fraud detection and investigation. And scale matters: financial institutions are often dealing with millions of transactions daily, making identity fraud detection challenging. Fraud detection systems using machine learning and analytics minimize fraud investigation time by 70 percent and improve detection accuracy by 90 percent.
The benefits for a company’s fraud prevention as well as its operating expenses are huge. These systems can process more data and they can also do so faster than manual review, identifying suspicious patterns that might take humans much more time to recognize in subsequent transactions. Therefore, the review time for information can be reduced
when AI can analyze these data points. This system is also truly scalable, as one system can easily go through all an organization’s data points. With continued use, an AI/ML-driven system will become more trained as it continues to go through more data.
Financial services companies can use AI in some of these leading ways:
1. Predictive analytics for fraud detection
Banks can use AI-powered predictive analytics to identify patterns and anomalies in transaction data that might indicate fraudulent activity. This enables banks to detect fraud in real-time before it causes significant damage. AI can sift through massive quantities of data, both historic and current, to predict any possible changes.
2. Scalable ID-Based Transactions
Use of AI can allow customers to transact both digitally and remotely on any device. AI can allow your business to verify and authenticate customer identity and eliminate any manual review that may slow down the customer onboarding or transaction experience. In addition, AI can continuously improve its verification procedures as it processes more data. Because of its speed, AI also can allow your operations to scale as it will not be bogged down by manual review.
3. Machine learning to build profiles and track behavior for fraud detection
Customer behavior is a key part of fraud detection. How can typical versus atypical customer behavior be understood? Machine learning algorithms can be trained to recognize patterns in vast amounts of transaction data that are indicative of both normal and fraudulent behavior. These algorithms could help build and sort customers into profiles that could be updated in real-time with each transaction. In addition, AI could determine if a certain transaction fits a pattern or should be flagged. Natural language processing algorithms can also analyze customer data and detect inconsistencies, such as typos and grammatical errors in transaction descriptions that might indicate fraud. Over time, the algorithms improve their accuracy in detecting fraud, leading to more effective fraud prevention.
4. Data accuracy, integrity, and integration
When used as part of document verification, AI can check that any official document has the features it should and that the data in those documents has no anomalies, including signs of tampering that can be undetectable to the human eye. Also, AI can be used to integrate data from various sources within one organization to find fraud patterns or trends. AI can also convert unstructured data into usable structured data which can ensure that data is usable for fraud analysis.
5. Chatbots
Banks can use AI-powered chatbots to communicate with customers and help them detect and report fraud. Chatbots can also be used to help customers resolve fraud-related issues, improving the overall customer experience.
6. Know Your Customer (KYC) measures
AI is a growing component of regulatory compliance. Using AI to verify identity and any identity documents, as well as facial biometrics, can drastically improve transaction time and create a seamless workflow- something customers will also appreciate.
Fraud has a huge impact on any business’s bottom line, and financial services companies are at the front lines in fighting an onslaught of increasingly sophisticated fraud threats. While fighting fraud, these companies must continue to offer their customers peace of mind that their information and transactions are safe and secure- all while offering a seamless experience. AI is a powerful tool for financial services companies to fight fraud by leveraging its ability to analyze large amounts of data, detect patterns, and identify anomalies. With AI-powered solutions, companies can ensure their customers can have it all.