In the evolving fintech sector, Artificial Intelligence is now a fundamental innovation, driving development in everything – from merchant risk management to fraud detections. However, as AI technologies develop, so do the potential threats. For fintech companies like ours, understanding these risks and implementing robust measures is essential to safeguard our operations and maintain trust of our merchants. What are the possible threats brought by AI and what are tips to minimise those risks?
Sensitive data protection
AI systems, particularly those involved in processing and analysing payment data, can be targeted by attackers seeking to exploit vulnerabilities. If AI models are not robustly secured, they can become entry points for fraud. Moreover, AI-driven solutions can facilitate new types of fraud if they are not adequately trained to recognize and respond to evolving fraudulent tactics.
Prevention Tips:
- Encrypt sensitive data both in transit and at rest using advanced cryptographic methods.
- Use secure channels for data transmission.
- Perform routine security audits and vulnerability assessments to identify and address security weaknesses.
- Implement AI-driven anomaly detection systems that adapt to new fraudulent patterns over time.
- Conduct thorough security reviews and penetration testing on AI systems before deployment.
Impersonation threats
AI technologies have equipped fraudsters with tools to create even more sophisticated scams. These include deepfake audio and video capabilities, which can be used to impersonate individuals in high-stakes financial scenarios.
Prevention Tips:
- Implement multi-factor authentication and biometric verifications to reduce the risk of impersonation.
- Use verified and secure communication channels for discussing sensitive information.
- Join industry forums or networks for sharing intelligence about new AI-driven fraud techniques and threats.
Malware
Cybercriminals are also using AI to develop more sophisticated attack vectors, such as polymorphic malware, which can alter its code to avoid detection, and AI-driven social engineering attacks at scale.
Prevention Tips:
- Ensure that your software, operating systems, and applications are up to date with the latest patches.
- Implement advanced threat protection solutions that include behavioural analysis and machine learning capabilities.
- Harden your IT infrastructure by following security best practices for configuration.
- Develop and maintain an incident response plan that includes procedures for dealing with AI-driven cyber threats. Regularly test the plan and update it based on emerging threats.
- Conduct regular security training for employees to recognize and respond to AI-enhanced phishing attempts.
Data breaches
Payment card acquirers handle sensitive customers’ information. The use of AI requires the collection, storage, and processing of large volumes of data, raising concerns about data privacy and protection, as AI’s dynamic capabilities could lead fintech companies to unintentional data breach.
Prevention Tips:
- Stay abreast of regional and global regulatory changes affecting AI deployment in fintech sector.
- Ensure compliance with regulations such as the General Data Protection Regulation (GDPR) and/or the Payment Card Industry Data Security Standard (PCI DSS).
- Engage in scenario planning and simulations to understand potential compliance risks under different AI use cases.
Risk assessments by AI
AI models can amplify biases present in their training data. In the context of payment card acquiring, this could lead to unfair treatment of certain customer segments, such as denying services based on influenced risk assessments. So, to speak AI would behave unpredictably. This not only affects customer satisfaction but can also lead to legal challenges, reputational damage, and operational risks.
Prevention Tips:
- Diversify data sources and incorporate fairness-aware algorithms in model training processes.
- Use both synthetic data and real-world data to test the system’s response to unusual or unexpected situations.
- Continuously update AI models and retrain them with new data to adapt to changing dynamics.
- Ensure that the training datasets are as comprehensive and varied as possible, covering a wide range of scenarios.
- Regularly audit and test AI models for bias by analysing performance across different customer groups.
- Utilize transparent and interpretable machine learning models when possible.
- Design AI systems with built-in fallback strategies that can trigger human intervention or switch to a more reliable system when the AI behaves unpredictably or fails.
- Involve experts from various fields – such as data science, cybersecurity, risk, and domain-specific areas in the development of AI systems. This helps in foreseeing potential failures from different perspectives and designing more resilient systems.
The integration of AI into fintech processes offers unprecedented opportunities for growth and efficiency. However, with these opportunities new risks come that must be actively managed. By staying informed about the latest AI threats and adopting a proactive risk management approach, companies can not only navigate these challenges but also leverage AI as a significant competitive advantage. Remember, in the realm of AI, preparation is the key to resilience.
Rokas Muraska, Chief of Risk & Security Officer at PAYSTRAX
