Deepfake Fraud Cases in Indonesia Increase by 1550%
According to a recent UN report, cybercriminal groups in Southeast Asia exploited deepfake technology in 2023 to orchestrate romance scams, cryptocurrency fraud, and money laundering, stealing up to $37 billion. In particular, deepfake fraud in Indonesia saw significant growth in 2024. The VIDA report highlighted a 1,550% surge in AI-driven fraud cases related to the FinTech sector. These fraudulent activities leverage AI-generated fake videos, audio, and images, posing significant challenges to financial services and their identity verification systems.
FinTech companies must act swiftly to counter these threats by adopting advanced security technologies to safeguard their operations and users.
▶︎ Deepfake: Challenges and Strategies for FinTech
How Does Deepfake Impact Indonesia’s FinTech Industry?
Applications of Deepfake in Fraudulent Attacks
The rapid advancement of deepfake technology has made fraud increasingly difficult to detect. Here are some common applications:
- Generative Adversarial Networks (GANs)
GANs, a deep learning technology, use two competing neural networks to create highly realistic images and videos. Fraudsters employ GANs to forge identification documents or fabricate images of celebrities and executives for fraudulent activities. For example, a recent report highlighted scammers impersonating FTSE executives using deepfake videos to trick employees into unauthorized fund transfers. - Facial Image Synthesis and Dynamic Simulation
Fraudsters use facial synthesis technology to create facial models resembling their targets, coupled with dynamic simulations to mimic expressions and movements. These technologies are employed to produce fake video calls, convincing victims they are interacting with real individuals. For instance, an employee of a global engineering firm in Hong Kong fell victim to a deepfake video scam, leading to a loss of approximately $25 million. - AI Voice Cloning Technology
This technology precisely replicates a person’s voice characteristics, including tone, speed, and accent. Fraudsters exploit this to conduct voice phishing, impersonating executives to demand urgent wire transfers from finance staff. - Multimodal Fusion Technology
Combining voice, images, and videos, multimodal fusion technology creates even more deceptive content. Scammers use these tools to produce highly realistic videos that impersonate trusted individuals or institutions, tricking victims into sharing sensitive information or transferring funds.
Deepfake Attacks in the FinTech Industry
These fraudulent activities undermine the stability of the FinTech sector and erode user trust:
- Fake Identity Applications for Financial Services
Fraudsters use deepfake technology to create counterfeit identification documents and personal profiles to apply for loans, credit cards, or other financial services. These fake applications may result in financial losses for institutions and damage their reputations. Moreover, fraudsters might impersonate real users, bypassing identity verification systems with fabricated biometric data to access services illegally. - Executive Fraud
Scammers use deepfake technology to mimic the voices and appearances of company executives, issuing fake instructions to employees to transfer funds or disclose sensitive information. - Impersonation of Partners or Advisors
Fraudsters impersonate a company’s partners or advisors through deepfake technology, contacting targets via email, phone, or video calls to request sensitive data or monetary transfers. This type of scam exploits victims’ trust in the forged identities to achieve illicit gains.
Indonesia’s Government Response to Deepfake Fraud
Recognizing the threats posed by deepfake technology, the Indonesian government is implementing various measures to combat such risks:
- Establishing AI War Rooms
Since 2017, Indonesia has operated War Rooms to monitor and address online misinformation and fraud. Combining AI technology with expert teams, these War Rooms monitor digital activity 24/7, identifying and addressing potential threats. This proactive approach helps detect and prevent deepfake-related fraud. - Promoting Ethical AI Practices
The government emphasizes the ethical application of AI technologies, ensuring technological advancements align with social responsibility by implementing relevant policies and guidelines. - Enforcing the OJK Regulation No. 27 of 2024
This regulation strengthens oversight of digital financial assets, including cryptocurrencies.
How FinTech Companies Can Combat Deepfake Fraud
Using Biometric Technology to Block Deepfake Fraud
Biometric technologies have become indispensable tools for FinTech companies to counter deepfake and other emerging threats. Here are three core solutions:
- Liveness Detection
- Active Liveness Detection: Requires users to perform specific actions, such as blinking, nodding, or smiling, captured via camera to verify they are real.
- Passive Liveness Detection: Uses AI to analyze facial depth, skin texture, lighting variations, and blood flow details without user input to determine authenticity.
- ID Verification Technology
Combines ID document validation with facial recognition to match users’ selfies against uploaded ID photos. This technology uses deep learning models to identify facial key points, ensuring the user matches the individual in the ID. - Behavioral Biometrics
Analyzes user behavioral traits, such as typing speed, touchscreen pressure, and swipe patterns, to verify identities. This method leverages data models to learn user behavior and detect anomalies.
According to reports, Indonesian tech companies have begun exploring biometric technology to enhance user security. For example, ride-hailing platform Grab has introduced fingerprint recognition as an alternative to alphanumeric passwords, requiring drivers and users to verify their identities via selfies. Similarly, platforms like Gojek and e-wallet service Dana use facial recognition to prevent transaction fraud.
Authme Biometric Solutions
Authme’s identity verification system is specifically designed to counter deepfake threats, featuring:
- High-Precision Facial Recognition: Matches users to their ID photos, preventing unauthorized registrations. This technology achieved top accuracy in the 2023 NIST FRVT 1:1 Visa-Border comparison, with a recognition rate of 99.7%.
- ISO-Certified Anti-Spoofing: Authme’s passive liveness detection has passed ISO-30107 certification, demonstrating its capability to resist 2D photos, 3D masks, and deepfake attacks. In the Fime laboratory, Authme tested 1,350 attack samples per ISO-30107 standards and achieved a 100% success rate.
▶︎ What is Facial Recognition? Understand its Definition, Technical Principles, and Benefits
In addition to its ability to counter deepfake threats, Authme enhances the user experience by integrating AI technology with various flexible deployment models:
- Edge AI Technology: Allows devices to process data locally where it is generated, reducing latency, improving response times, and minimizing reliance on network bandwidth.
- Flexible Integration Methods: Offers multiple options, including on-premises, SDK, hybrid cloud, and no-code solutions. These modularized technologies reduce customization costs and ensure a 100% success rate in deployment.
▶︎ Learn More About Authme’s Biometric Solutions
Protect Your FinTech Business with Biometric Technology
Deepfake fraud is rapidly becoming a significant challenge for Indonesia’s FinTech industry. Companies must adopt advanced identity verification technologies, such as liveness detection and ID matching, to safeguard their operations and users.
Authme’s biometric solutions provide FinTech companies with robust tools to counter emerging threats, ensuring business security and trust.
Contact us today to learn more about combating deepfake fraud effectively!
