Fighting fraud: How AI is strengthening banking security

By Shaghil Bilali

The banking and finance industry has found Artificial Intelligence to be useful in various areas such as sales forecasting and risk management. Yet, among its many applications, fraud detection in banking stands out as a particularly impactful use case. By leveraging a combination of supervised and unsupervised learning algorithms, Artificial Intelligence can enhance fraud detection capabilities. This is achieved by gaining a deeper understanding of customers' behaviors, enabling organizations to identify and prevent unauthorised activities more effectively.

Types of banking frauds 

Authorised push payment – In this type of fraud, the perpetrator typically deceives the payer into approving a payment to a fraudulent recipient. The recipient may present themselves as a genuine individual or business and may employ tactics like phishing to obtain authorisation for the payment.

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Account takeover – This type of fraud often involves tactics such as phishing, malware, or the use of unknown links in emails or text messages that trick the user into granting access to their account.

Phishing – Fraudsters can obtain a victim's personal and financial information by sending deceptive emails or text messages that trick the victim into disclosing such information.

Identity theft – Phishing is a common method used by fraudsters to acquire personal and financial information, such as banking PINs and social security numbers, which can lead to identity theft. 

Credit card theft- Email phishing or identity theft can provide criminals with access to your credit card details, enabling them to make purchases without the need for physical possession of the card.

Fake documents- It is common for fraudsters to create fake IDs, use fake applications, forge IDs, and illegally purchase consumer IDs.

Gauge hidden patterns – The use of AI-powered analytics can help identify patterns and potential trends of fraud and generate scenarios for such events. This enables banks to take appropriate steps to close loopholes and proactively prevent fraudulent activities from occurring.

Integration of data– Data from multiple branches and terminals is aggregated and analysed using AI and data analytics to identify patterns and potential trends of fraud, or to pinpoint the source of any fraudulent activity that has already occurred.

Gathering unstructured data – Data from diverse sources and branches is stored in data warehouses, where AI and data analytics are utilised to convert unstructured data into structured data that can be analysed for potential fraudulent activities.

Using accurate computation – The use of AI allows for the accurate computation of vast amounts of data, providing actionable insights that empower banking teams to work more efficiently and effectively detect potential fraudulent activities. By utilising analytics, it becomes easier to differentiate between genuine and fraudulent customers and their activities.