Digital fraud today rarely looks suspicious. Many attacks use real credentials, familiar devices, and normal user behavior. As a result, fraud often goes undetected until losses occur. This shift explains why AI is increasingly used for fraud detection.
Recent studies show that global banking losses from cybercrime and fraud reached USD 485.6 billion in 2023, while account takeover attacks grew by 150% worldwide. These incidents are difficult to stop not because they are technically complex, but because they appear legitimate.
So how does AI actually help?
Traditional fraud detection relies on rules and thresholds. AI works differently. It analyzes behavior over time, how users interact with systems, how devices behave across sessions, and how actions are sequenced.
An individual login or transaction may look normal. AI flags risk when the overall pattern does not align with genuine user behavior, even if no rule is broken.
This is critical because modern fraud is designed to stay within acceptable limits.
Fraud does not always start with money movement. Research on account takeovers shows that 64% of cyberattacks on Indonesian SMEs involved unauthorized account access, often using valid credentials rather than technical exploits.
This pattern is explored further in research on account takeover behavior, which highlights how attackers increasingly focus on early-stage access rather than immediate financial theft.
Read also: What is Account Takeover and Why Is It a Big Problem?
AI helps surface these risks earlier by correlating identity signals, interaction timing, and behavioral consistency allowing suspicious activity to be detected before financial damage occurs.
The rise of deepfake technology has further reduced the effectiveness of traditional checks. Studies show that deepfake-related fraud incidents grew by more than 900% annually in earlier years, and losses exceeded USD 250 million in 2020 alone.
Static image or document checks are no longer reliable. AI detects these attacks by analyzing facial motion, texture consistency, and interaction signals that indicate whether input comes from a real, live human.
AI used for fraud detection works because fraud itself has changed. When attacks hide behind normal-looking behavior and valid data, detection must move beyond rules.
By analyzing patterns, learning what “normal” truly looks like, and identifying risk early, AI provides a practical response to how modern fraud operates.