AI Fights Fraud: How the Use of AI Technologies in Banking Forges the Fight against Fraudsters

Virtually every credit card and debit card user has had their card suspended due to suspicious activity—and unfortunately fraud has not slowed with the rest of the world during the pandemic. In fact, since the beginning of the COVID-19 outbreak, 40% of financial services firms have seen an increase in fraudulent activity—according to a LIMRA survey—leading notable banks and even the FBI to issue fraud alerts to their communities.

Over the past few years, many technologies have come onto the market that help banks and credit unions catch out-of-the ordinary activity and alert the card holder as quickly as possible. However, with more people making deposits and taking part in financial activities digitally via apps and chatbots due to current stay at home orders, the onus is solely on the technology to detect the fraudulent activity. Now more than ever, banks and other financial service providers need to implement AI technologies so they can become even more capable of identifying fraudulent patterns and data points that rudimentary, rule-based software can easily miss. Here are the three ways AI technology helps banks with fraud detection:

1. Maintains User Trust

In recent years, companies have invested in AI primarily to improve efficiency by automating mundane tasks like data entry. However, according to a recent report from MIT Technology Review, organizations have expanded its use to improve the customer experience by increasing personalization and bringing a deeper level of customer understanding. This use of AI is particularly important for communicating with customers who could potentially be the target for fraudulent activity.

Detecting fraud is critical for banks to build trust with their customers. Leveraging a technology like conversational AI can alert banks to fraud warning signs so they can instantly notify the affected customer, give them the option to verify those suspicious transactions and then suggest next steps for fraud resolution. Banks should specifically look toward conversational AI providers who offer solutions with natural language understanding (NLU), which digests text and voice, translates it into computer language and produces a text and audio output in a natural way that humans can easily understand. This goes beyond simply offering customers an experience personalized just by their name and account details—it creates a more human interaction that connects them interpersonally through a language they are most familiar with, fostering trust between the customer and financial service provider.

2. Processes Data for Anti-Money Laundering

Anti-money laundering (AML) is another area where banks are beginning to tap into the power of AI. With hundreds of thousands of wire transfers a day totaling trillions of dollars—not to mention the various privacy laws designed to protect customers—it’s almost impossible to identify every instance of money laundering. Nevertheless, banks are required to do everything possible to identify and help combat money laundering. While banks have been using rule-based software to identify money laundering for some time, AI offers a significant improvement as it learns, grows and adapts with each experience. Much of this is due to AI’s ability to process large quantities of data and see trends, patterns and outliers in a much larger context than the average human could easily discern.

3. Aids Compliance Operations for Risk Prevention

As part of the fight against financial crime, governments across the world require their financial institutions to put in place AML compliance programs that oversee internal AML policies and ensure the organization remains compliant with important regulations. However, managing AML legislation has proven to be a challenging task for compliance officers. According to Accenture’s 2019 Compliance Risk Study, compliance officers have reported being overworked and exhausted – resulting in potentially detrimental human-caused errors. As a result, there is an increased urgency to improve compliance productivity and shift operations from “check-the-box” to a risk-prevention outlook.

Organizations that incorporate AI into their businesses are forced to re-imagine their processes – a common barrier to technology adoption. For example, with traditional compliance processes, humans might look at 15% of a bank’s loans to ensure things are being done correctly, while AI processes can review 85% of the data. This not only improves accuracy, but it also means banking employees can be freed up to do more meaningful work.

With the rise of AI, banks have a new tool to handle any number of tasks that are traditionally time-consuming, labor intensive and prone to mistakes. Whether it be document processing, anti-money laundering, fraud detection, risk prevention or customer service, AI offers a level of support that is unparalleled in the history of banking. Best of all, with an increasing focus on privacy, AI represents a viable way to use that data in a safe, trusting manner.


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