How AI is the Future of Banking Fraud Detection

Fraud looms like a black cloud over the banking industry. Cybercriminals are becoming increasingly sophisticated in their methods, and traditional fraud detection systems simply can’t keep pace. Now, the conversation is shifting to fraud detection using AI in banking — it’s a beacon of innovation and potential solution in the ongoing war against deceit and deception.

The Association of Certified Fraud Examiners found that fraud costs organizations an estimated 5% of their annual revenue, amounting to billions annually. Banks can stem these losses by catching bad actors early in the act — or even better, by noticing behavioral trends and patterns that can stop fraud before it occurs. To do this, financial institutions need to incorporate AI into their fraud detection tactics.

How AI Is Used to Prevent Banking Fraud

Bank fraud comes in many shapes and sizes, from stealing customers’ account numbers to opening fake accounts and racking up charges. AI can identify multiple types of bank fraud to limit the impact of bad actors. 

Banking relies on several document-based identity checks to prevent fraud — pay stubs, tax statements, and utility bills, for example. With so many paper and digital documents, this creates many opportunities for fraudsters to use forged or fake documents to open accounts and quality for loans.

It’s easier than you think. While AI can be used to prevent document fraud, it’s also a tool that enables fraud. Fraudsters can use AI to generate fake IDs and other document types. These fakes are extremely realistic and can not only fool the human eye, but also identity verification technology. 

Identity theft is a widespread problem where fraudsters pretend to be someone else in order to acquire loans, goods, and services in their name without repaying the debt. The Bureau of Justice Statistics reported that about 23.9 million people in the U.S. experienced identity theft in 2021. 

AI can flag identity theft by analyzing vast amounts of data in real time and recognizing patterns. This proactive approach enables banks to identify and prevent fraudulent transactions before they occur.

Credit card fraud is the most common type of identity theft, with nearly 426,000 cases of credit card fraud reported to the Federal Trade Commission (FTC) in 2023. Digital channels have made credit card theft easier since you don’t have to have a physical card to conduct credit fraud.

By analyzing a user’s spending patterns, locations, and other data points, AI can identify purchases that deviate from the user’s normal behavior and flag them as being potentially fraudulent. 

Phishing is the act of posing as an individual, business, or entity to acquire personal information, such as credit card numbers and passwords, via email or other types of messages. Depending on the information that the cyber criminal is able to get their hands on, phishing usually leads to data breaches and/or financial loss. Banks and financial institutions are particularly susceptible to phishing, as Scaler’s 2023 ThreatLabz Phishing Report found that finance, insurance, government, healthcare, and education are the five most targeted industries. 

AI is a double-edged sword when it comes to phishing. Generative AI can be used to create convincing emails, text messages, and even phone calls that look like they come from someone or a business that you trust. At the same time, financial services companies can fight phishing by using AI to identify potentially malicious messages based on the content, tone, links, and attachments.  

Benefits of AI Fraud Detection

AI is a significant improvement over traditional methods of detecting banking fraud, which rely on rule-based systems and human review, for many reasons. These benefits help banks protect customers and minimize their losses. 

AI’s ability to process and analyze large amounts of data in real time and constantly monitor activities and transactions allows it to prevent fraud before it happens and trigger immediate action. By proactively stopping fraudulent activity, banks can reduce potential losses and improve customer satisfaction.   

AI’s effectiveness improves over time by constantly learning from your data and refining its approach. The more data it consumes, the better it can make connections between various activities and spot potential fraud early. 

Sophisticated tools and methods now make it nearly impossible to detect whether a document or image has been manipulated with the naked eye. AI doesn’t just visually look at documents or images to detect alterations; it also analyzes metadata. Details that aren’t obvious to the human eye will be detected by AI, allowing your fraud detection system to catch things that employees wouldn’t be able to.

AI can process large volumes of data quickly to identify potential fraud. It’s much faster than human employees at comparing datasets and documents. Plus, it can find hidden connections that humans might not know to look for.

By using large volumes of data, AI reduces false positives that human or rules-based fraud detection can be prone to. As your AI models ingest and process more data, accuracy continues to improve and create optimal outcomes.

Using Instabase to Automate Document Fraud Detection

Manually verifying documents is time-consuming, prone to errors, and risky. A better approach is to use Instabase, an AI-powered collection of apps that can be used for a wide variety of use cases, including detecting document fraud. Best-in-class financial services companies use Instabase to achieve the best outcomes for their customers by:

Instabase uses generative AI to extract, analyze, and validate data across a variety of document and file types. You can use Instabase to verify passports and other forms of identification, signatures, account numbers, and other details. Instabase includes logic checks, which detect things like whether amounts add up on an invoice, irregular pay periods, and that total pay and pay periods match.

For fraud detection, Instabase works with organizations such as Resistant AI, whose AI financial crime prevention tools complement Instabase’s data extraction and logic checks. Resistant AI can detect advanced image tampering and forgery that eludes the human eye. Companies that combine Instabase with Resistant AI’s Document Forensics can build end-to-end, automated document verification workflows that increase the speed and accuracy of document processing.