How Multimodal AI Is Revolutionizing Credit Card Fraud Detection

01 Extract Image Features to Enrich Information Dimensions

Compared with traditional structured data, image and audio‑video data contain denser information and natural spatial‑temporal cues, though they are harder to mine. The multimodal credit‑card application anti‑fraud AI model consists of a preprocessing module and a feature‑extraction module, applicable to attack identification, information verification, and extraction scenarios.

02 Multidimensional Cross‑Verification for Complex Fraud Scenarios

In AI‑deepfake attacks, fraudsters collect facial data and use face‑swap technology to bypass facial comparison; the multimodal model can detect subtle cues such as screen reflections and moiré patterns. In mask attacks, printed masks reveal color‑difference and gamut limits that the model can spot. In forged‑document attacks, the model leverages target localization and image classification to identify abnormal fonts, alignments, and other anti‑counterfeit features. Attention verification detects whether the subject’s focus aligns with genuine intent. Additionally, the model extracts auxiliary attributes such as facial age, pathological signs, and environmental context (e.g., hospital or bank settings) to enrich fraud‑risk decisions.

03 Summary and Outlook

Going forward, the ICBC Software Development Center will extend the multimodal anti‑fraud system beyond images to incorporate text, speech and other modalities, continuously adopting advanced techniques to strengthen fraud risk control and protect customers’ assets.