Model Poisoning: Threat and Implications for Fraud and Credit risk

Introduction

The advent of Artificial Intelligence (AI) has reshaped industries, streamlining processes and services. In the banking sector, AI has become a cornerstone, powering enhanced fraud detection, personalized customer interactions, and data-driven decision-making. However, as AI's capabilities evolve, so do the tactics of malicious actors. One emerging threat gaining prominence is dataset poisoning attacks. Collaborative efforts by experts from Google, ETH Zurich, NVIDIA, and Robust Intelligence have brought this issue to the forefront, illuminating how cybercriminals can exploit vulnerabilities in AI systems. Their comprehensive research, detailed in a paper on the arXiv preprint server, has unveiled two distinct types of dataset poisoning attacks, raising concerns about their potential impact on fraud management and credit decisioning within the banking sector.

The Crucial Role of Quality Data in Banking Operations

In the banking landscape, AI systems play an instrumental role in fraud management and credit decisioning. These systems draw insights from extensive historical financial data to detect fraudulent activities and evaluate creditworthiness. However, this dependence on data also renders AI vulnerable to manipulation, opening avenues for malicious actors to compromise these critical functions.

Fraud Management Example

Consider an AI-driven fraud management system tasked with identifying suspicious transactions. If manipulated data portraying fraudulent transactions as legitimate infiltrates the training dataset, the AI system might miss actual instances of fraud, exposing the bank to financial losses and reputational damage.

Unveiling Split View Poisoning in Banking Operations

Researchers have brought to light the split view poisoning technique, which poses risks to both fraud management and credit decisioning processes. This method capitalizes on the expiration of internet URLs, which can be acquired by malicious actors. Expired domains are populated with fabricated financial data. When AI systems access these deceptive sources and absorb the manufactured data, they inadvertently introduce inaccuracies into their learning process. Consequently, the AI system's performance becomes compromised, resulting in skewed fraud detection and flawed credit assessments.

Credit Decisioning Example

Imagine an AI-driven credit decisioning system assessing loan applications. If manipulated data portraying risky applicants as low-risk is injected into the training dataset, the AI system could erroneously approve loans to individuals with higher default risks, leading to financial losses for the bank.

Frontrunning Poisoning: Concealed Threats in Banking Operations

The second form of attack, frontrunning poisoning, poses a substantial risk to AI systems employed in banking operations. By tampering with trusted data repositories, attackers can introduce false financial indicators or economic data. AI systems that rely on these repositories for accurate information may then incorporate the deceptive data, leading to skewed credit decisions, faulty fraud management, and potential financial losses.

Mitigating the Threat: Building Defenses for Banking Operations

To safeguard against the looming threat of dataset poisoning attacks in the realm of banking, adopting proactive measures is crucial:

1. Data Vigilance: Rigorous data validation and cleansing processes can help identify and eliminate tainted data from training sets, ensuring accurate fraud detection and reliable credit assessments.

2. Adversarial Training: Integrating adversarial examples into AI model training enhances their resilience against manipulated inputs, strengthening fraud management and credit decisioning outcomes.

3. Ongoing Audits: Regular assessments of AI models against known attack patterns enable the early detection of compromises in fraud management and credit decisioning algorithms.

4. Human Expertise: Involving domain experts in AI model development and validation offers an additional layer of defense against manipulated data, bolstering the accuracy of decision-making processes.

Conclusion

The unveiling of dataset poisoning attacks through collaborative research underscores the evolving challenges within banking operations. The potential repercussions of compromised AI systems underscore the urgency of developing robust defense mechanisms. By acknowledging the vulnerabilities inherent in AI, particularly in the context of fraud management and credit decisioning, and by embracing proactive measures, the banking sector can navigate this intricate landscape. Striking a balance between harnessing AI's transformative potential and safeguarding against malicious manipulation will be pivotal in ensuring the resilience, security, and trustworthiness of banking operations, fraud management, and credit decisioning processes.