AI Fraud Detection in Financial Institutions

AI and Banking

Artificial Intelligence (AI) is a computer system that can imitate intelligent human behavior. AI is often used when it is too complicated or costly to hire humans to do a job that computers can do reliably. AI is dramatically changing how banks detect fraud in their customer-facing endpoints. In the past, most banks relied on rules that were often prone to false positives and negatives. They also used a lot of manual processes that made it difficult to scale and keep up with constantly evolving cyber threats. AI takes a much more dynamic approach to fraud detection, using machine learning algorithms to identify patterns and behaviors that human eyes might miss. This article explores how AI transforms banks' fraud detection in their customer-facing endpoints. It also touches upon some of the most common types of bank account fraud and emerging threats like synthetic identity fraud.

How Is AI Used in Fraud Detection

Fraud detection is the most common use of AI in banking and is also one of the most lucrative. Banks have traditionally focused their fraud-detection efforts on a small set of known fraudulent activities; for example, if someone tries to log into an account using an IP address from another country, that could be a red flag. AI can help detect a much broader set of suspicious activities. For example, suppose a customer suddenly starts making many transfers from her account, or a new customer starts making large deposits into his account. In that case, that could indicate that the customer is engaged in money laundering — or if someone starts withdrawing from ATMs in a part of the country far away from where the account was opened, that could be a red flag for identity theft. AI can help banks develop new ways to detect fraud and flag those activities for human investigation.

AI and Machine Learning

AI uses machine learning to detect patterns, then runs several tests that include "what-if" scenarios to determine whether a customer's behavior is strange. This helps reduce false positives — fraud alerts that turn out to be legitimate customer behavior. Say, for example, that you're a customer at a major online bank. Your fraud-detection algorithm decides to flag an unusually large deposit you recently made into your account. The AI system analyzes your deposit transaction and compares it to past transactions. It looks at your other account activity to see if there's anything out of the ordinary. It also looks at other customer accounts to see if any other large deposits might be linked to you. If the algorithm detects something suspicious, it alerts an analyst. The analyst reviews your transaction and other account activity to determine if it's legitimate. The analyst will shut down the transaction, freeze your account, and notify you if it isn't. This entire process takes a few seconds or minutes, whereas a manual process might take days or weeks.

Different Types of Fraud in a Bank

Fraudulent transactions can take many forms. For example, identity fraud occurs when someone uses your name to open new accounts, make purchases, or obtain loans. Account takeover occurs when someone steals your login credentials and accesses your account. Credit card fraud occurs when scammers use stolen credit card numbers to make purchases; account takeover is often involved in credit card fraud. Other types of fraud include false claims, identity theft, check fraud, money laundering, and tax fraud. Fraudulent transactions can also be interrelated, which makes them even harder to detect. For example, if a criminal steals an identity and uses it to open a bank account, she can use that account to receive stolen funds from other fraudulent accounts. A criminal might also use stolen identities to open fraudulent accounts, deposit stolen funds into those accounts, and then withdraw the money from ATMs at one time to avoid being flagged as suspicious account activity.

Emerging Types of Fraud

One of the most common types of fraud that banks are now fighting is synthetic identity fraud. This is when criminals use false identities to open fraudulent accounts. These criminals use stolen identities, false documents, and stolen Social Security numbers (SSNs) to trick a bank into opening an account for them. They sometimes use real people's names and SSNs, but the people whose information was stolen don't know they're being used to open fraudulent accounts. These types of fraudulent accounts are very hard to detect. They also allow criminals to rely less on stolen credit card numbers to make fraudulent purchases. The owner can sometimes notice and report the fraudulent charge if a credit card is stolen. But if a bank account is fraudulently opened, the victim may not find out until he gets a letter from the IRS asking him to pay taxes on income he didn't earn.

Application of Artificial Intelligence in Banking

Banks' same AI technologies to detect fraud can also be used to prevent fraud. AI can flag suspicious activities and stop them before they happen. For example, if a customer withdrawal exceeds the daily limit, an AI system can flag that for manual review. And AI can also be used to deliver a better customer experience. For example, AI can use customer data, such as account history, past purchases, and payment patterns to give customers better recommendations. If you're trying to decide between two similar products, AI can help you pick the better one by analyzing your history and accounting for your location, the time of day, and the weather. Banks can also use AI to make the compliance process less tedious for employees and more accurate. For example, AI can help flag suspicious activities before they happen and ensure that certain transactions are logged accurately, even if an employee is working in a chaotic environment.

Conclusion

Artificial intelligence is revolutionizing fraud detection in banking. AI-based fraud detection systems can look at a wide range of data, including account metadata, customer behavior, account relationship data, and transaction data, and use that data to detect fraud before it happens. This helps detect a broader range of fraud than was possible in the past and does so far more accurately than human fraud detection systems. Artificial intelligence can also prevent fraud and deliver a better customer experience. Banks can use AI to flag suspicious activities before they happen and help ensure that employees follow policies correctly.