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Mar 27, 2026 · Information Security

When Deepfakes Cost $25 Million: The End of Video‑Call Authentication

A 2025‑2026 deep‑fake attack on Arup’s finance team used publicly gathered intelligence to create a real‑time, AI‑generated video of the CFO and colleagues, resulting in a $25 million transfer and exposing the economic asymmetry that makes video‑call authentication unreliable, prompting a shift to multi‑channel, zero‑trust verification.

Identity verificationSecurityZero Trust

0 likes · 28 min read

When Deepfakes Cost $25 Million: The End of Video‑Call Authentication

Black & White Path

Mar 17, 2026 · Information Security

What Lies Behind AI Model Poisoning Exposed in the 3·15 Cybersecurity Crackdown

The 2026 CCTV 3·15 report uncovered four major cyber‑security black‑gray‑market schemes—AI large‑model data poisoning, private‑domain marketing targeting seniors, fraudulent stock‑recommendation scams, and pseudo‑scientific height‑increase fraud—revealing how technical loopholes, platform governance gaps, and societal anxieties enable precise consumer exploitation.

AI model poisoningGEO optimizationRAG vulnerabilities

0 likes · 23 min read

What Lies Behind AI Model Poisoning Exposed in the 3·15 Cybersecurity Crackdown

Tencent Cloud Developer

Jan 14, 2025 · Information Security

Can Database Signatures Prevent Tampering? An Analysis of Financial Risk Controls

The article revisits the debate on tampering with WeChat balances, explaining that joint database signatures can detect but not stop alterations, that risk‑control checks and code safeguards block unauthorized withdrawals, that identity verification prevents cross‑account transfers, and that a layered, real‑time monitoring system is essential for robust fund protection.

Database SecurityWeChatfinancial fraud

0 likes · 6 min read

Can Database Signatures Prevent Tampering? An Analysis of Financial Risk Controls

DataFunSummit

Jul 3, 2022 · Artificial Intelligence

Graph Neural Network Approaches for Internet Financial Fraud Detection

The talk examines how the COVID‑19 pandemic accelerated online financial services and fraud, outlines the challenges of traditional and internet‑based fraud detection, and presents graph neural network solutions—including PC‑GNN and AO‑GNN—demonstrating their effectiveness on real‑world and public datasets while discussing future research directions.

AUC optimizationfinancial fraudfraud detection

0 likes · 12 min read

Graph Neural Network Approaches for Internet Financial Fraud Detection

Efficient Ops

Dec 29, 2021 · Artificial Intelligence

How AI Model Risk Governance Maturity Is Shaping Financial Fraud Prevention

The article details China's new AI model risk governance regulations, the CAICT 2021 GOLF+ IT Governance Forum, Tongdun Technology's successful maturity assessment for financial gambling‑fraud risk models, and insights from executives on implementation challenges, benefits, and future plans.

AI Governancefinancial fraudmachine learning

0 likes · 13 min read

How AI Model Risk Governance Maturity Is Shaping Financial Fraud Prevention

58 Tech

Nov 25, 2020 · Databases

Design and Implementation of a Financial Fraud Detection Graph Network Using JanusGraph

This article presents a comprehensive overview of building a financial fraud detection graph network, covering background challenges, graph schema design, a four‑layer architecture with JanusGraph, data import pipelines, quality assurance, performance optimizations, and practical applications such as risk scoring, association analysis, and id‑mapping.

JanusGraphRisk analysisdata pipeline

0 likes · 22 min read

Design and Implementation of a Financial Fraud Detection Graph Network Using JanusGraph

AntTech

Nov 1, 2018 · Artificial Intelligence

Heterogeneous Graph Neural Networks for Malicious Account Detection (GEM) – Overview of Ant Financial’s CIKM 2018 Paper

This article introduces the GEM method, the first heterogeneous graph neural network designed for malicious account detection, explains the nature and characteristics of malicious accounts, describes why graph neural networks are effective, and presents experimental results from the authors' CIKM 2018 study.

AI securityCIKM 2018financial fraud

0 likes · 8 min read

Heterogeneous Graph Neural Networks for Malicious Account Detection (GEM) – Overview of Ant Financial’s CIKM 2018 Paper

When Deepfakes Cost $25 Million: The End of Video‑Call Authentication