Artificial intelligence (AI) has revolutionised the financial sector, especially in the fintech space. These companies have integrated AI to improve operational efficiency, personalise services and optimise decision-making. The adoption of AI has allowed fintechs to offer more agile solutions tailored to customers’ needs, transforming the way financial services are provided.
This new adoption also brings with it the emergence of new legal challenges.
What legal challenges do lawyers face when working with AI in fintech?
The integration of artificial intelligence (AI) into the fintech sector raises a number of legal challenges that lawyers must address to ensure regulatory compliance and the protection of users’ rights.
Identifying legal grey areas in the implementation of AI
The rapid evolution of AI has at times outpaced the ability of existing regulations to govern its use. This has created areas of legal uncertainty, especially in fintech, where AI is used to assess credit risk, detect fraud and manage investments. Lawyers must identify and analyse these grey areas in order to advise companies properly on how to operate within the current legal framework and anticipate possible regulatory changes.
The lawyer’s role in automated decisions and the lack of algorithmic transparency
AI systems in fintech often take automated decisions that can significantly affect customers, such as approving loans or detecting fraudulent activity. The lack of transparency in the algorithms used can make it difficult to understand how these decisions are made. Lawyers are responsible for ensuring that companies implement mechanisms that make algorithmic processes explainable and that customers can challenge automated decisions when necessary.
Legal management of reputational risk and bias in automated processes
The use of AI can introduce bias into automated processes, which could result in discrimination or unfair decisions. These biases can lead to significant reputational risks for fintech companies. Lawyers should advise on carrying out regular audits of AI systems to detect and correct bias, as well as on developing internal policies that promote fairness and transparency in the use of these technologies.
How can lawyers understand and tackle algorithmic bias?
Algorithmic bias in fintech can lead to unfair or discriminatory decisions, and lawyers must identify and mitigate these biases to ensure fairness and regulatory compliance.
Types of bias in AI models
Bias in AI can arise from a variety of sources:
- Data bias: Training data that reflects existing prejudices.
- Design bias: Decisions made when building the model that introduce partiality.
- Interpretation bias: Errors in interpreting the model’s results.
Tools for detection and prevention
To address these biases, both technical and legal tools are used:
- Algorithmic audits: Assessments to identify and correct bias in models.
- Fairness testing: Statistical methods that detect disparities in outcomes.
- Legal frameworks: Implementation of regulations that require transparency and fairness in AI systems.
Building legal frameworks with shared responsibility
It is essential to establish legal frameworks that assign clear responsibilities among AI developers, providers and users. These frameworks should include:
- Transparency: An obligation to document and explain how the algorithms work.
- Ongoing oversight: Regular monitoring to detect and correct emerging biases.
- Legal liability: Definition of the legal consequences of failing to comply with the established regulations.
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What regulations must fintechs take into account when using AI?
The integration of artificial intelligence (AI) into the fintech sector is subject to a range of regulations that seek to ensure its ethical and safe use.
Regulatory landscape: AI Act, GDPR, consumer protection rules
The European Union’s AI Act establishes a legal framework for the development and use of AI systems, classifying them according to their level of risk and setting specific requirements for each category. Fintechs must assess their AI systems to determine their classification and comply with the corresponding obligations.
The General Data Protection Regulation (GDPR; Reglamento General de Protección de Datos, RGPD) imposes obligations on the processing of personal data, including data used by AI systems. Fintechs must ensure transparency in the use of data, obtain appropriate consent and allow users to exercise their rights over their data.
In addition, consumer protection laws require fintechs to ensure that their AI systems do not give rise to discriminatory or misleading practices, thereby protecting users’ rights.
Legal obligations relating to risk management, data privacy and explainability
Fintechs must implement risk management systems that identify and mitigate potential negative impacts of AI. This includes the continuous assessment of systems to detect and correct biases or errors.
As regards data privacy, it is essential that fintechs adopt technical and organisational measures to protect personal information, guaranteeing its confidentiality and integrity.
The explainability of AI systems is crucial for maintaining user trust and complying with regulations. Fintechs must be able to explain how and why their systems take particular decisions, especially in processes that directly affect consumers.
Legal recommendations for mapping, auditing and classifying AI systems according to their risk
To comply with regulations, fintechs are advised to carry out a thorough mapping of all the AI systems they use, identifying their purpose and scope. They should then audit these systems to assess their compliance with current regulations and detect possible areas for improvement.
Classifying AI systems according to their level of risk allows fintechs to prioritise actions and resources to ensure regulatory compliance. This classification should be based on criteria established by the regulations and take into account factors such as the potential impact on users and society.
Designing internal AI governance policies in fintech
Fintechs must develop internal policies that regulate the use of AI and address aspects such as transparency, data protection and the management of risks associated with automated systems. Lawyers, with their knowledge of technology law, are key to drafting and overseeing these guidelines, ensuring that they are aligned with the applicable regulations.
Overseeing the system’s legal life cycle: from validation to post-launch control
Legal responsibility does not end with the implementation of an AI system. Its entire life cycle must be overseen, from initial validation to post-launch monitoring. Lawyers must make sure that systems comply with regulations at every stage, identifying and mitigating any legal risks that may arise during their operation.
Emerging profiles: “legal engineer”, “AI compliance officer”, “ethics counsel”
The intersection between law and technology has given rise to new professional profiles. The “legal engineer” combines legal knowledge and technical skills to develop solutions that facilitate regulatory compliance. The “AI compliance officer” specialises in ensuring that AI systems comply with the relevant regulations. The “ethics counsel” advises on the ethical implications of using AI, ensuring that business practices are responsible and transparent.
Which legaltech solutions can make AI compliance easier?
Legaltech solutions offer specialised tools that facilitate regulatory compliance and the management of risks associated with the use of AI.
Regulatory watch and legal risk monitoring tools
Fintechs can use platforms that automate the tracking of legislative changes and relevant regulations.
These tools make it possible to:
- Stay up to date in real time on new regulations affecting the use of AI.
- Assess the impact of these changes on the company’s operations and products.
- Implement proactive measures to mitigate emerging legal risks.
For example, the EU Artificial Intelligence Regulation (AI Act) imposes specific obligations on companies that develop or deploy AI systems. Regulatory watch tools help ensure ongoing compliance with these provisions.
Platforms for automated documentation and compliance reporting
Proper documentation is essential to demonstrate regulatory compliance.
Legaltech solutions offer:
- Automatic generation of legal documents and internal policies adapted to current regulations.
- Preparation of compliance reports detailing the measures adopted and their effectiveness.
- Secure, organised storage of all relevant documentation for future audits.
These platforms make it easier to manage efficiently the documentation required by regulations such as the AI Act, which requires transparency and traceability in AI systems.
Integrating legal AI for prediction, risk detection and continuous audits
AI itself can be an ally in regulatory compliance.
Fintechs can implement AI systems that:
- Analyse large volumes of data to identify patterns and anticipate possible breaches.
- Automatically detect legal risks associated with the company’s operations and products.
- Carry out continuous audits, ensuring that internal processes are aligned with the applicable regulations.
These solutions enable constant, proactive oversight, reducing the likelihood of penalties and improving the company’s reputation in the market.
How can you apply this knowledge in day-to-day legal advice?
The integration of artificial intelligence (AI) into the fintech sector raises legal challenges that lawyers must address with clear criteria and good practice.
1. Assessing AI systems: criteria and good practice
To carry out an effective legal audit of an AI system, it is essential to consider these four pillars:
- Transparency and explainability: Ensure that algorithms are not “black boxes”. Decisions must be auditable and understandable to regulators and users.
- Data protection: Verify strict compliance with the GDPR, guaranteeing privacy by design (Privacy by Design) and information security.
- Risk management: Implement protocols to identify and mitigate algorithmic bias or errors that could give rise to civil or reputational liability.
- Regulatory compliance: Align the technology with current regulations, especially the EU AI Act and sector-specific financial regulations.
2. Practical examples of application
The impact of AI is felt mainly in three critical areas:
| Use case | Legal challenge |
| Credit scoring | Guaranteeing non-discrimination and the customer’s right to obtain an explanation when credit is refused. |
| Biometric verification | Complying with the rules on processing special category data and ensuring accuracy to prevent identity fraud. |
| Anti-fraud models | Maintaining constant oversight to balance detection effectiveness with respect for users’ rights. |
3. Suggested workflow
When taking part in an AI project, it is advisable to follow this strategic path:
- Continuous training: Keep permanently up to date with the rapid evolution of the legislative and technological framework.
- Initial analysis: Understand the project’s technical objectives and their legal implications from day one.
- Risk assessment: Identify critical points in terms of privacy, intellectual property and potential bias.
- Interdisciplinary collaboration: Maintain smooth communication with developers and data scientists to translate legal requirements into code.
- Implementation of controls: Establish monitoring mechanisms and periodic audits of the system’s performance.
Frequently asked questions on legal liability and AI in fintech
What obligations does a fintech have when deploying AI in its processes?
Fintechs that implement artificial intelligence must comply with the European Union’s Artificial Intelligence Regulation (AI Act), in force since 2024. This regulation classifies AI systems according to their level of risk and sets specific obligations for each category. Fintechs must:
- Ensure the transparency and traceability of the AI systems they use.
- Implement risk management and data governance measures, especially in high-risk systems.
- Provide adequate training to professional users on the use of AI.
- Ensure compliance with data protection regulations, such as the GDPR, and other applicable sector-specific laws.
Failure to comply with these obligations can lead to significant penalties, including fines of up to 7% of annual global turnover or €35 million, as well as the withdrawal of products from the market.
Who is liable for unforeseen bias that leads to financial discrimination?
If an AI system in the fintech sector gives rise to financial discrimination because of unforeseen bias, liability falls on the natural or legal persons involved in its development and use. Since AI does not have legal personality, it cannot be held legally liable. Therefore, the developers, providers and professional users of the AI may be held liable for the damage caused.
Can a lawyer influence the technical design of a system to limit its legal risk?
Lawyers play a crucial role in AI governance within fintechs. They can influence the technical design of AI systems to limit legal risks by:
- Taking part in the design of internal AI governance policies.
- Overseeing the system’s legal life cycle, from validation to post-launch control.
- Working with technical teams to ensure that systems comply with current regulations and are transparent and explainable.
In addition, professional profiles such as the “legal engineer”, the “AI compliance officer” and the “ethics counsel” are emerging, combining legal and technical knowledge to address the challenges that AI presents in the financial sector.
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