Legal liability in the use of financial algorithms

Legal liability in the use of financial algorithms

Financial algorithms are sets of mathematical or computational rules and procedures designed to solve problems related to analysis and decision-making in the financial sector. These algorithms are used to model, predict, and optimize various aspects of the financial market, from asset investment to risk management.

From a digital law perspective, there are two regulations that directly affect these algorithms: the General Data Protection Regulation (GDPR) and the Artificial Intelligence (AI) Act of the EU. These are two key regulatory frameworks that impact the legality of financial algorithms, especially those that handle large volumes of personal data or use AI in their processes. Both regulations have significant implications for the protection of individuals’ privacy, transparency, fairness, and accountability in the use of technologies such as financial algorithms.

The GDPR directly affects financial algorithms when they process personal data. The implications for these algorithms include the following:

• Collection and processing of personal data: financial algorithms that use personal data (such as credit history, banking transactions, or purchasing behavior) must comply with the principles of the GDPR. This includes the need to obtain explicit consent from individuals, unless there is an alternative legal basis (such as the performance of a contract or compliance with a legal obligation).
• Transparency and explanation of automated decisions: the GDPR establishes that individuals have the right to be informed about the logic, significance, and consequences of automated processing of their data, especially in decisions that significantly affect them (e.g., an algorithm determining whether a credit is approved or rejected). This refers to the automation of decisions (Art. 22 of the GDPR). In the case of financial algorithms, users must understand how decisions related to their credit profile, investments, or any other automated transaction are made.
• Right to human intervention: if a financial algorithm makes important decisions (such as rejecting a loan), the GDPR grants individuals the right to request human review of that automated decision.
• Data security and protection: algorithms must incorporate adequate data security and data protection measures to prevent privacy violations. This includes the use of techniques such as encryption, pseudonymization, and other measures to protect against unauthorized access to personal data.
• Right to access and rectification: individuals have the right to access the data that an organization holds about them and to request the rectification of that data if it is incorrect. This can affect the functioning of financial algorithms, especially in areas like credit analysis.

Financial algorithms that analyze personal data for credit, risk, or investment decisions must take these principles into account and offer transparency and control options for users. Additionally, they must have mechanisms in place to ensure the security and privacy of the data used.

AI Act and financial algorithms

The AI Act sets specific rules to ensure that AI is used safely, transparently, and responsibly. Its main implications for financial algorithms are as follows:

• Risk-based classification of AI: the AI Act classifies AI applications based on the risk they pose to individuals’ rights and safety. The classification ranges from minimal risk to high risk. Financial algorithms that make decisions about credit, investments, or risk are generally classified as high-risk AI, which implies stricter requirements for their application.
• Transparency requirements: for high-risk AI (such as those that make automated decisions about credit or investments), providers must ensure that users understand how the algorithm works. They must also provide clear explanations of how decisions are made, which aligns with the GDPR’s requirements for explainability and transparency.
• Risk management and auditing: financial algorithms must undergo compliance assessments and continuous oversight to ensure they operate consistently with the principles of the law. This includes external audits, testing, and regular updates to verify the accuracy and effectiveness of the algorithms.
• Human intervention: in some cases, the AI Act requires that automated decisions be interruptible or modifiable by human intervention, especially in decisions that significantly affect individuals, such as credit or insurance decisions.
• Protection against bias and discrimination: financial algorithms that use AI must be designed to avoid discrimination and bias in decision-making. The AI Act mandates the mitigation of bias risks in algorithmic models, which is particularly relevant in the context of financial decisions (e.g., in credit scoring algorithms).

Financial algorithms that make automated decisions about credit, insurance, investments, etc., must be transparent, fair, and auditable. This involves implementing measures to ensure non-discrimination, validating the data used, and allowing for human intervention when necessary.

Interaction between GDPR and the AI Act

Although the GDPR and the AI Act are distinct regulations, both complement each other to ensure that AI technologies, such as financial algorithms, are used responsibly. The use of financial algorithms is deeply affected by both data protection regulations and AI regulation within the EU.

Financial algorithms must ensure transparency, fairness, and security in the processing of personal data, as well as be auditable and allow for human intervention when necessary. Financial institutions must be aware of these regulatory frameworks and ensure compliance to avoid sanctions and protect individuals’ rights.