AI adoption in financial services: Balancing risks and regulations

by Travis Deforge, Director of Cybersecurity Engineering, Abacus Group

Artificial intelligence (AI) is revolutionising the financial services industry, providing significant opportunities for operational efficiency, improved decision-making, and enhanced competitive advantage. A recent survey conducted by the City of London Corporation and KPMG forecasts that the integration of AI in financial services will generate an extra £35 billion ($44.68 billion) in revenue for UK businesses over the next five years.

However, as financial services firms increasingly rely on AI technologies, they face the challenge of navigating a fast-evolving regulatory landscape with varying approaches across different regions. To unlock AI’s full potential while ensuring compliance, financial institutions need to understand both the current regulatory frameworks and emerging trends, especially in the European Union (EU), the United Kingdom (UK), and the United States (US), where policymakers are developing distinct AI regulations. With the right governance strategies and a proactive stance, firms can maintain compliance and harness the benefits of AI.

What does the AI regulatory landscape look like?

The regulatory landscape for AI is still in its early stages, but significant frameworks are emerging, particularly in the EU, UK and US. In the EU, the AI Act is among the world’s first comprehensive pieces of legislation targeting AI usage, classifying AI systems into four categories based on risk: minimal, limited, high, and unacceptable.

High-risk systems, such as those used for credit scoring and fraud detection, are subject to stringent requirements, including rigorous testing, transparency, and human oversight​. Additionally, existing laws like the General Data Protection Regulation (GDPR) impose further obligations on data privacy and protection, adding another layer of compliance for firms utilising AI technologies.

By contrast, the UK has taken a more pro-innovation approach. While there is no AI-specific legislation at the moment, regulators such as the Financial Conduct Authority (FCA), the Prudential Regulation Authority (PRA), and the Bank of England have laid out strategic guidelines. Their focus is on principles like transparency, fairness, and accountability rather than imposing prescriptive regulations. This principles-based approach fosters innovation while ensuring financial stability and consumer protection.

In the United States, the Securities and Exchange Commission (SEC) is actively shaping its approach to AI regulation within the financial sector. The SEC emphasises transparency and disclosure in AI usage, encouraging firms to clearly communicate how AI is integrated into their operations. Additionally, the SEC emphasises fairness and non-discrimination, urging robust testing to prevent biases in AI models, and advocates for strong governance frameworks to ensure accountability and effective risk management.

This divergence in regulatory approaches poses a significant challenge for firms operating across the EU, UK, and US. Financial institutions must navigate the EU’s strict regulations under the AI Act, adapt to the UK’s flexible, principles-based guidelines, and comply with the SEC’s emerging requirements in the US. Managing these differing regulatory expectations requires comprehensive and adaptable compliance frameworks that can accommodate multiple standards alongside overlapping data privacy laws like the General Data Protection Regulation (GDPR) in the EU and the UK’s Data Protection Act.

Balancing innovation with risk management

AI offers enormous potential for financial services firms to streamline operations, improve customer experiences, and make more informed decisions. However, these benefits come with significant risks, especially in terms of data misuse, bias, and transparency. AI models require large datasets to function effectively, which can expose firms to compliance risks if they fail to protect sensitive information or inadvertently introduce bias into decision-making processes.

The black-box nature of many AI models – where the rationale behind decisions is not easily explainable—presents another challenge. Regulators are increasingly demanding that firms ensure AI systems are both transparent and accountable. This requirement is critical in high-stakes applications like credit scoring or investment decision-making, where opaque AI decisions could lead to regulatory scrutiny or reputational damage​.

Financial firms must implement robust risk management frameworks that address these requirements. This includes conducting regular AI risk assessments, monitoring for potential biases, and ensuring that all data used in AI models is secure and compliant with data protection regulations.

Additionally, AI governance frameworks should be established to provide oversight and accountability across the entire AI lifecycle, from development and deployment to monitoring and auditing. This includes establishing AI-specific policies and procedures that outline how AI systems should be developed, deployed, and monitored. Governance frameworks should also incorporate regular audits to ensure ongoing compliance with both internal policies and external regulations. By staying ahead of regulatory developments and investing in strong governance practices, firms can build resilient AI systems that deliver value without exposing them to undue risks.

Proactive solutions for compliance

To stay ahead of the regulatory curve, financial services firms must adopt proactive strategies that ensure compliance with both current and emerging AI regulations. A key element of this approach is investing in specialised skills and targeted training. Employees at all levels, from developers to compliance officers,

must be equipped to understand AI-related risks and manage them effectively. Regular training and education are essential to ensure that staff can navigate the complexities of AI technologies and the associated regulatory obligations.

In addition to employee training, firms should focus on developing responsible data practices. This includes ensuring that all data used in AI systems is ethically sourced, free from bias, and protected under stringent security protocols. Data minimisation – collecting only the data necessary for AI to function – can help reduce exposure to regulatory risks, particularly under data protection laws like GDPR​.

Drawing on third-party expertise can help firms comply with emerging AI regulations. By integrating cybersecurity and governance risk compliance (GRC) services, specialists can implement comprehensive controls that not only ensure compliance but also enhance security across the board.

Continuous vulnerability management is also an important exercise that helps firms identify and mitigate risks in real-time, while vendor due diligence ensures that third-party AI providers comply with the same rigorous standards. Penetration testing further strengthens this framework, identifying weaknesses before malicious actors exploit them​.

Building a Resilient Future

The adoption of AI in financial services presents both significant opportunities and challenges. While AI can enhance efficiency and decision-making, it also introduces complex risks related to data privacy, bias, and compliance.

As AI continues to evolve, financial services firms will face increasingly complex challenges related to ethical use, regulatory compliance, and technology integration. Future advancements, such as autonomous decision-making and predictive analytics, will push the boundaries of traditional financial models, necessitating even more stringent governance and risk management frameworks.

Looking ahead, firms that invest in AI responsibly, balancing innovation with ethical considerations and regulatory compliance, will be better positioned to stay competitive. By staying agile and anticipating both technological and regulatory shifts, financial institutions can harness AI’s potential while safeguarding against future risks. The ability to adapt and evolve in this rapidly changing landscape will define the success of firms in the years to come.