The Stronger the Automation, the Stricter the Risk Control: How Licensed Institutions Use AI Technology to Combat AI Fraud

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Financial environments with stronger automation continue to drive improvements in business efficiency, while simultaneously making AI-driven fraud methods increasingly diverse and covert. Data shows that fraud losses in the United States in 2024 already exceeded $12.5 billion, marking a significant increase from the previous year, with experts pointing out that AI played a central role in this growth. The table below reflects the current state of technology application in financial institutions dealing with AI fraud:

Criminals leverage AI to generate text, images, audio, and video, carrying out various new types of attacks such as social engineering, phishing, and financial fraud. The combination of synthetic media and automation greatly increases the speed and stealth of fraud. Licensed institutions urgently need to upgrade their AI risk control systems to proactively identify and defend against these intelligent threats.

Key Takeaways

• Financial institutions need to accelerate the upgrade of AI risk control systems to address increasingly complex AI fraud methods.
• Generative AI makes fraud techniques more covert; financial institutions must adopt intelligent risk control engines for real-time monitoring.
• A multi-layered defense system can effectively identify and prevent deepfake attacks, improving overall risk management capabilities.
• Industry collaboration and regulatory coordination are key to combating AI fraud; information sharing enhances defense capabilities.
• Continuous investment in AI technology upgrades is essential for financial institutions to protect customer assets in highly automated environments.

AI Fraud Challenges in Highly Automated Environments

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Increasing Intelligence of Fraud Methods

Highly automated financial environments are driving the rapid evolution of AI fraud techniques. Generative AI not only dramatically scales up fraud but also makes advanced attacks such as deepfakes and intelligent phishing more widespread and low-cost.

• Generative AI has self-learning capabilities and can continuously optimize strategies to evade fraud detection systems.
• The generation of deepfake videos, fabricated voices, and documents has become simple, allowing criminals to easily impersonate executives, employees, or customers.
• Traditional rule-based detection systems struggle to handle complex threats such as AI-driven synthetic identities and automated personalized scams.
• The underground industry also combines blockchain, virtual currencies, and other new technologies to further enhance the stealth and attack efficiency of fraud tools.

Typical Cases and Risks

In recent years, AI-driven fraud incidents have occurred frequently, causing enormous financial losses. In 2024, U.S. financial institutions suffered up to $4.7 billion in losses related to AI fraud.

• In 2023, deepfake incidents in the fintech sector increased by 700% year-over-year.
• In one case, during a video conference at a Hong Kong company, all participants except the real executive were deepfake clones, resulting in $25 million being transferred to a fraudulent account.
• Deepfake-enhanced “CEO fraud” cases are increasingly common, with criminals using AI to impersonate executives and bypass traditional identity verification, leading to major losses.
• It is projected that by 2027, generative AI-based email fraud losses will reach $11.5 billion.
• Account takeover (ATO) has become the fastest-growing fraud category, with attack frequency and losses continuing to rise at double-digit rates.

Threats Faced by Licensed Institutions

Licensed financial institutions face multiple AI fraud threats in highly automated environments.

• Deepfake technology can generate highly realistic identity verification credentials, such as voice samples and video verification; fraudsters can even use synthetic executive voices to authorize transfers.
• Fake customer videos are used to bypass remote identity verification processes; AI-generated personas build trust through long-term interaction before committing financial fraud.
• Identity theft during customer onboarding, account takeover after account opening, and large-scale synthetic identity fraud all place higher demands on risk control systems.
• AI-driven fraud can occur at every stage of the customer lifecycle and is highly personalized and scalable.
• Industry reports indicate that AI-driven fraud has become the biggest threat facing financial institutions in 2026, with regulators paying close attention to related risks.

Financial institutions must accelerate the upgrade of AI risk control systems and adopt adaptive AI prevention platforms to effectively address the continuously evolving intelligent fraud challenges in increasingly automated environments.

Upgrading the AI Risk Control System

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Intelligent Risk Control Engine

In highly automated environments, financial institutions are deploying intelligent risk control engines to counter increasingly complex fraud threats. Intelligent risk control engines enhance risk identification and response capabilities through multi-layered defenses and multi-agent collaboration. The table below shows the key components of a typical AI risk control engine and their functions:

Intelligent risk control engines are highly adaptive and can respond to new fraud behaviors in real time. Compared with traditional systems, AI risk control engines achieve lower false positive rates and faster response times through pattern and anomaly recognition. BiyaPay employs intelligent risk control engines in global payment, remittance, and real-time fiat-to-digital currency conversion scenarios to monitor transaction behavior in real time, identify potential risks, and safeguard fund security for Chinese-speaking users.

AI systems can identify abnormal patterns in multiple small transactions and promptly detect money laundering activities. By analyzing large datasets in real time, AI risk control engines effectively reduce fraud occurrence rates and enhance risk prevention capabilities in highly automated environments.

Real-Time Monitoring and Behavioral Analysis

Real-time monitoring and behavioral analysis are at the core of AI risk control systems. Financial institutions use machine learning, natural language processing, and computer vision technologies to dynamically track user behavior and transaction patterns. BiyaPay deploys real-time monitoring systems in global payment and digital currency trading services, analyzing user login, device usage, transaction frequency, and other behavioral characteristics to promptly detect abnormal activities.For licensed institutions, risk control is not only about detecting anomalies, but also about verifying those anomalies against the actual business path. A service such as BiyaPay, positioned as a multi-asset wallet, covers cross-border payments, fund management, and trading-related scenarios; when the system detects high-frequency conversion, abnormal device logins, or cross-market fund transfers, institutions should not rely on account behavior alone, but also check whether the user genuinely reached the relevant function pages, such as the exchange rate comparison tool, international remittance, or stock information page.

The value of this approach is that it maps “model alerts” back to real operating paths, reducing misjudgments caused by relying on a single feature alone. BiyaPay holds relevant financial registrations in jurisdictions including the United States and New Zealand; for licensed business flows involving USD, USDT, or HKD, consistency between the official domain, function entry points, account permissions, and behavioral logs is itself an important basis for identifying AI-enabled fraud.

• Feedzai uses real-time machine learning tools to identify fraudulent payment transactions and reduce risks in the financial services industry.
• MindBridge applies machine learning and anomaly detection to analyze financial data, uncovering control violations and errors that traditional methods may miss.
• HSBC uses natural language processing and machine learning to monitor internal communications, detecting compliance risks and insider trading indicators.
• AI algorithms analyze network and user behavior data to identify potential security incidents in real time.
• AI agents leverage behavior, device, content, and network signals to help various industries reduce losses and protect genuine customers.

AI systems track user behavior, such as login patterns and device usage. Sudden changes may indicate fraudulent activity. AI assesses risk the moment an event occurs and immediately selects an action. This speed can stop risk propagation before inconsistent activity stabilizes. AI not only monitors individual transactions but also analyzes long-term user behavior. For example, if a customer usually logs in from one city but suddenly attempts to access the account from another country, the system automatically issues an alert. AI detection uses machine learning algorithms to quickly predict and handle crime, verifying transaction authenticity before completion.

Multi-Agent Collaborative Defense

Multi-agent collaborative defense mechanisms significantly enhance the overall effectiveness of AI risk control systems. Each AI agent focuses on specific tasks and improves the comprehensiveness of risk management through collaboration. BiyaPay adopts multi-agent systems in global payment, remittance, USDT-to-USD/HKD conversion, and other business scenarios to evaluate transaction compliance in real time, accelerate decision-making, and reduce compliance risks.

In highly automated financial environments, multi-agent systems can coordinate various risk agents and human stakeholders to improve overall defense capabilities. However, when implementing multi-agent systems, financial institutions still face challenges such as regulatory compliance, data governance, system integration, and data quality. Dependence on human judgment for exceptions and constraints from regulation and model governance limit the speed of converting AI from pilot projects to actual operations.

Generative AI and Risk Governance

Generative AI plays an important role in financial risk governance. Financial institutions adopt dedicated governance frameworks for generative AI, establishing human-machine collaborative supervision mechanisms to monitor generative AI reasoning processes and ensure controllable risks. BiyaPay uses advanced identity verification methods—such as liveness detection algorithms and document-centric identity verification—in digital currency trading and global payment scenarios to confirm user authenticity and prevent synthetic identity fraud.

• Financial institutions ensure the security of generative AI applications through comprehensive vendor due diligence and AI impact assessments.
• Record-keeping and activity logging ensure all operations are traceable and meet audit requirements.
• Generative AI can analyze transaction patterns in real time and detect anomalies, significantly enhancing fraud detection capabilities.
• AI-driven models can learn and evolve, adapting to new fraud strategies and improving risk prevention in highly automated environments.
• Generative AI makes detecting fraudulent content difficult, rendering traditional methods ineffective. Financial institutions use AI tools for fraud prediction, analyzing historical data to forecast future fraudulent activities.
• Generate scenarios and identify potential suspects to assist in investigating suspicious activities.

In the context of increasing automation, financial institutions must continue investing in upgrading AI risk control systems, building multi-layered defense, real-time monitoring, multi-agent collaboration, and generative AI governance architectures to effectively address the constantly evolving intelligent fraud challenges.

Balancing Risk Control Automation and Compliance

Multi-Layered Defense System

In highly automated environments, financial institutions continuously build multi-layered defense systems to counter AI-driven fraud threats. Traditional rule-based detection systems are no longer sufficient to handle complex attacks. AI fraud detection platforms analyze transaction patterns in real time, flag anomalies, and adapt to constantly evolving fraud strategies. Behavioral analysis models learn normal account or user behavior and promptly capture anomalies missed by traditional systems. Industry experts believe financial institutions need to adopt adaptive AI fraud prevention platforms that integrate new data sources such as voice authentication and behavioral biometrics to enhance overall defense capabilities. Multi-layered defense systems not only focus on anomaly detection but can also simulate potential risks before attacks occur, improving the forward-looking nature of risk early warning.

Privacy Computing and Data Security

Data security and privacy protection have become core issues in upgrading automated risk control systems. When deploying AI risk control systems, financial institutions often face data quality challenges such as duplicate customer records and incomplete historical data. Some documents exist only as unstructured files, increasing data governance difficulty. To ensure data security, institutions adopt privacy computing, data desensitization, and distributed storage technologies to prevent sensitive information from being leaked during analysis and transmission. When processing customer data, AI models must strictly follow data minimization principles to reduce unnecessary data exposure. Continuous data quality management and security audits help improve the compliance and reliability of risk control systems.

Compliance Innovation and Regulatory Collaboration

Compliance innovation has become key for financial institutions in addressing automated risk control challenges. Regulators require banks to explain AI decision-making processes and document and continuously monitor model behavior. Institutions must meet new governance requirements, including explainability and performance monitoring. Several international financial institutions have improved the precision of regulatory monitoring through AI-driven compliance solutions. For example, one major bank uses natural language processing technology to automatically extract key points from regulatory revisions and suggest compliance manual updates, reducing manual review time by over 60%. Another institution deploys multi-agent systems to evaluate transaction compliance in real time and accelerate compliance decision-making. The table below shows practices of some institutions in compliance automation:

Human-machine collaboration and AI governance architectures provide solid assurance for compliance. AI systems automate large volumes of compliance tasks, while humans focus on high-risk and complex scenarios, achieving both compliance and efficiency. Financial institutions need to continuously optimize governance architectures, proactively adapt to regulatory changes, and promote balanced development of risk control automation and compliance innovation.

Industry Trends and Future Outlook

Continuous Investment in AI Risk Control

The financial industry is increasing continuous investment in AI risk control systems. Major licensed institutions are constantly optimizing algorithm models to improve the accuracy and response speed of risk identification. Hong Kong licensed banks have already made AI risk control engines a core infrastructure in cross-border payment, digital currency conversion, and other scenarios. Industry data shows that global financial institutions’ annual investment in AI risk control will grow at double-digit rates over the next three years. Continuous technological upgrades help institutions dynamically respond to new fraud methods in highly automated environments and protect customer asset security.

Industry Collaboration and Regulation

Industry collaboration and regulatory coordination have become key to combating AI fraud. Licensed institutions enhance overall defense capabilities by sharing threat intelligence, jointly establishing blacklists and abnormal behavior databases. Regulators continue to improve AI risk control-related policies, promoting the implementation of model explainability and data security standards. Regulatory bodies such as the Hong Kong Monetary Authority encourage financial institutions to participate in industry sandbox testing to verify the compliance and effectiveness of new technologies. Industry alliances and regulatory collaboration help form unified risk prevention standards and reduce systemic risks.

Technological Evolution and Challenges

AI risk control technology continues to evolve but also faces many challenges. Increased model complexity brings explainability issues, while data silos and privacy protection pressures continue to grow. Some institutions still have shortcomings in system integration, data governance, and talent reserves. In the future, the industry needs to strengthen human-machine collaboration, improve AI governance architectures, and drive risk control systems toward intelligence, automation, and sustainability. Only by continuously adapting to technological changes can financial institutions achieve dynamic balance between risk and compliance and build robust defense systems.

AI risk control has become the core weapon for financial institutions to combat AI fraud. Continuous upgrading of risk control systems can effectively improve risk identification and defense capabilities. Regulatory collaboration and information sharing build a solid defense line for the industry. In the future, financial institutions need to closely monitor AI technology evolution, maintain the forward-looking and dynamic adaptability of risk control systems, and jointly safeguard financial security.

FAQ

What Is AI-Driven Fraud, and Why Must Financial Institutions Take It Seriously?

AI-driven fraud refers to criminals using artificial intelligence technology to carry out attacks such as deepfakes and automated phishing. U.S. market data shows that losses caused by AI fraud continue to grow. Financial institutions must take it seriously to protect customer asset security and ensure compliant operations.

How Do Financial Institutions Balance Risk Control Automation with Compliance Requirements?

Financial institutions achieve a balance between risk control automation and compliance innovation through multi-layered defense systems and AI governance architectures. Institutions adopt privacy computing, data desensitization, and other technologies to ensure data security while meeting regulatory requirements for model explainability and compliance.

How Do AI Risk Control Systems Identify and Prevent Deepfake Attacks?

AI risk control systems combine behavioral analysis, liveness detection, and multi-factor identity verification to effectively identify deepfake audio/video and synthetic identities. By monitoring user behavior and transaction patterns in real time, the system promptly detects anomalies and prevents fraudsters from using synthetic content to bypass verification processes.

What Are the Main Challenges Licensed Institutions Face in Building AI Risk Control Systems?

Licensed institutions face challenges such as data quality, model explainability, system integration, and talent reserves. Some historical data is incomplete, and increased model complexity brings compliance pressure. Institutions need to continue investing in technological upgrades, improve governance architectures, and enhance overall risk prevention capabilities.

How Can the Industry Enhance AI Risk Control Defense Capabilities Through Collaboration?

The industry enhances overall defense capabilities by sharing threat intelligence, jointly establishing abnormal behavior databases, and participating in regulatory sandbox testing. Regulators promote standardization and compliance innovation, helping the industry form unified risk prevention mechanisms to effectively address AI-driven fraud threats.