Artificial Intelligence (AI) has transformed fraud detection in finance, enabling real-time transaction monitoring and faster fraud prevention. However, as AI-driven systems evolve, so do threats like machine learning model poisoning — a targeted cyberattack that manipulates AI models to bypass security and enable undetected fraud. This can lead to financial losses, regulatory challenges, and reputational damage.
Also referred to as ‘data poisoning,’ machine learning model poisoning is a type of cyberattack in which an attacker deliberately introduces malicious or misleading data into the training dataset used to build a machine learning model. This leads to the model generating inaccurate or biased predictions. Without robust safeguards, financial institutions face significant risks.
Impact of data poisoning on financial services
Machine learning model poisoning can generate false negatives, allowing fraudulent transactions to bypass detection, or false positives, wrongly flagging legitimate transactions. Attackers can also exploit this tactic for large-scale financial crimes, such as enabling suspicious transactions to appear compliant with regulations.
Industry research has shown that injecting poisoned data into training sets can cause AI models to misclassify inputs, leading to errors. It can also weaken cybersecurity models, reducing their ability to detect threats.
Traditional fraud detection systems were designed to recognize established fraud patterns, but model poisoning disrupts this process by corrupting the AI’s learning itself. This makes conventional approaches insufficient, requiring a new set of proactive defense strategies.
AI — a double-edged sword in fraud prevention
A 2024 AI Fraud Financial Crime Survey by BioCatch reveals that 74% of financial institutions worldwide are currently utilizing AI for financial crime detection, with all respondents expecting an increase in financial crime activity in the near future. Beyond the immediate threat of financial losses, regulatory compliance is another critical concern. Financial institutions operate under strict regulatory frameworks that require them to uphold rigorous fraud prevention measures.
Amid rising digital fraud, the Reserve Bank of India has urged financial institutions to strengthen cybersecurity. In February 2025, RBI Governor Sanjay Malhotra warned of increasing fraud in digital payments, stressing the need for stronger security. To combat these threats, the RBI introduced exclusive domain names (bank.in, fin.in) to enhance credibility and is piloting MuleHunter.AI, an AI/ML tool by RBIH to detect fraudulent mule accounts.
Ensuring AI security and integrity
Mastercard has been leveraging AI-driven fraud prevention for years, continuously innovating to counter emerging cyber threats. At the core of this effort lies Mastercard AI Garage, a dedicated hub that integrates advanced machine learning with robust security protocols to enhance fraud detection. AI Garage leverages self-learning algorithms and real-time anomaly detection to proactively identify and mitigate cyber risks, refining fraud models to adapt to evolving tactics while minimizing false positives.
Mastercard’s AI systems analyze over 75 billion transactions annually, detecting fraudulent patterns with exceptional precision while ensuring a seamless customer experience. Last year, Mastercard integrated generative AI into one of its key transaction risk monitoring solutions, enabling the analysis of one trillion data points and improving fraud detection rates by an average of 20% — with some cases seeing up to 300% gains in accuracy.
Another cutting-edge fraud monitoring solution by Mastercard utilizes a proprietary Generative Adversarial Network (GAN) algorithm to mimic issuer authorization systems and dynamically identify fraudulent transaction patterns in real time. It follows a three-tier approach. It first exposes issuers to over 25,000 GAN-generated fraud patterns to identify vulnerabilities, then triggers real-time alerts when suspicious transactions are detected, and finally enables them to block flagged transactions.
A proactive approach to countering data poisoning
To safeguard AI models from adversarial attacks, financial institutions must move beyond conventional security measures and adopt a multi-layered, AI-driven defense strategy. The following proactive measures can significantly reduce the risks posed by machine learning model poisoning:
- Data integrity and validation – Implementing stringent data validation processes, such as differential privacy techniques and blockchain-based verification, ensures that AI models are trained on secure, unaltered datasets.
- Adversarial training and simulation – By exposing AI models to synthetic fraudulent data and adversarial testing, institutions can enhance resilience against manipulation and improve anomaly detection capabilities.
- Real-time threat monitoring – AI-powered monitoring systems can dynamically detect and flag irregular transaction patterns before they escalate into security breaches.
- Restricted model access and encryption – Implementing zero-trust architectures, end-to-end encryption, and strict access controls can minimize the risk of unauthorized modifications to AI models.
- Collaborative threat intelligence – Cross-industry collaboration among banks, regulatory bodies, and AI security researchers can play a crucial role in identifying and mitigating emerging fraud tactics.
Future of AI-powered security
As AI fraud tactics evolve, the financial sector must adopt next-generation security measures. Self-adaptive AI models that learn from attacks and adjust defenses in real time will be key to countering data poisoning.
Decentralized federated learning can enhance fraud detection while protecting data privacy, reducing the risk of large-scale model poisoning. By keeping sensitive data on local devices rather than a central repository, organizations can practically limit attackers’ ability to inject poisoned data.
AI security is not just about fraud prevention — it’s about preserving trust in financial transactions. Institutions that ignore model poisoning now risk exposure to growing threats. Staying ahead requires greater innovation and smarter application.
