AI in finance is a double edged sword. On one side, it’s a key driver of innovation and productivity; on the other, it’s a breeding ground for data breaches and operational risk. Though these dangers are well-known, governance has yet to keep up with the unprecedented speed of development and adoption. In this article, we help firms understand the pillars of AI compliance, from defining clear roles and responsibilities to collecting automated audit trails.
Over 80% of global financial services firms, regulators, and fintechs are adopting AI in some capacity, with use cases spanning front end and back end operations.
For many employees, this is cause for celebration. But for those in more senior compliance, operational, and legal roles, it’s the reason behind many sleepless nights.
Despite its benefits, AI poses a welter of compliance risks — and governance has yet to keep pace.
Though AI-specific regulations are beginning to pop up in disparate parts of the globe, most firms are having to use their best judgment and retrospectively shape existing frameworks around their rapidly expanding AI initiatives.
This begs the question: what are the requirements of AI compliance in financial services? And how can firms protect themselves from existing and future risks?
In this article, we walk through the dangers of AI, and piece together a 7-pillar approach to achieving compliance.
Many regulators are cautiously in favor of AI-driven innovation, with bodies such as the FCA even creating their own “AI Lab” initiatives.
Cautiously is the word to focus on here, however. No matter how vast the opportunities, there’s no avoiding the existing and emerging risks of AI.
Consider widely accessible consumer tools like ChatGPT, which are now ubiquitous in many organizations — financial services included. Beyond poor auditability and traceability, these tools are becoming a hot target for cybercriminals, who use clever jailbreak techniques to access and compromise sensitive data. Couple this with typical “shadow AI” challenges (users forgetting to “opt out” of model training permissions, or entering highly confidential data), and your chances of a data breach increase significantly.
But the dangers don't end there.
Currently, software development is the most mature AI application in the financial sector, with 42% of firms harnessing AI for this specific purpose. This isn’t an automatic security issue. However, poorly “vibe-coded” or unvetted applications can lead to accidental market manipulation, biased decisions, systemic operational failures, and, once again, data breaches. This is exacerbated by the rapid acceleration of AI development and adoption.
As AI permeates through every faction of financial operations, oversight becomes impossible. Firms struggle to find the time to step in when errors or vulnerabilities arise (assuming they’re able to spot them in the first place). And, when systems break, teams may lack the knowledge to either fix the problem or revert back to manual alternatives.
Regulators are no stranger to these AI risks — hence their cautiousness. Yet, despite this, AI-specific regulations are practically non-existent within the financial services industry.
In the UK and USA, leading regulators express no intention of creating bespoke legislation. Instead, they encourage organizations to interpret existing frameworks to suit their AI models and use cases.
That being said, state-level laws and — across the Atlantic — the EU AI Act provide some more relevant guidance, which may be helpful to refer to when “adapting” your financial standards.
The sheer velocity of AI, coupled with lagging governance and regulatory intervention, may leave many firms feeling uncertain about their obligations.
In the following section, we outline 7 fundamental pillars of AI compliance in the financial services industry. This will help you plan for future AI adoption in a secure, scalable, and auditable way.
There must be clear ownership, accountability, and senior manager oversight at every point in the AI lifecycle, from the conception and development stages to the ongoing maintenance. This applies whether you’re building or buying tools.
When overseeing the responsible use of AI tools, financial firms must be conscious of data privacy and security obligations, consumer rights, and AI bias and discrimination. Ultimately, AI systems must deliver good customer outcomes — not just internal productivity gains.
Your IT teams may be aware of the “dos and don’ts” of AI usage, but your bank tellers and financial advisors may not.
To ensure the secure and responsible use of AI technologies, create infallible AI acceptable use policies, dictating which tools employees can and cannot use. This is critical for preventing the use of dangerous “shadow AI” tools.
Be explicit about approved AI use cases, too. For example, can underwriters use AI to assess risk? Or is it strictly for summarizing client data and speeding up information finding?
Complement these policies with trackable AI-specific training, ensuring your e-learning tool of choice records each course completion for auditing purposes.
Record keeping is non-negotiable for any AI use. According to the EU AI Act, all organizations with general purpose AI systems must keep a record of up-to-date technical documentation. For high risk AI systems, requirements are taken up a notch.
On top of these technical records, your AI tools must automatically capture a searchable, timestamped audit log of interactions, queries, decisions, and behaviors.
This is crucial for proving compliance and defending any action your organization makes. If you reject a loan based on information given to you by an AI model, you need to show why you arrived at that decision.
AI tools must only index approved internal data sets — particularly if they’re high risk. This limits the likelihood of hallucinations, bias, and discrimination.
When making decisions or producing generative content, the system must prove where it got its answer. For example, AI search functionality must cite any pages, documents, or data sets in any overview, with direct links to the source material.
Your AI tools must respect your data privacy commitments and internal policies, and inherit your strict, role-based permissions.
For instance, a retail branch teller should not be able to use an AI chatbot to query corporate M&A activity or high-net-worth client portfolios. The AI should only surface, summarize, and link to content that the user is authorized to access.
Outside of typical cross-border transactions and fraud detection use cases, any sensitive data should be stored and processed within each firm’s jurisdiction.
This complicates AI adoption, as many commercially available tools span global regions and countries.
To satisfy sovereignty and data protection requirements, as well as safeguard your organization from geopolitical risks, it’s better to use localized AI platforms (hosted on-premises or in a local cloud environment) with a private framework.
AI systems may be able to synthesize data and come to intelligent conclusions, but it cannot and should not authorize high risk actions.
All critical decisions, such as risk management and lending decisions, must involve mandatory human verification to mitigate bias, false conclusions, and other errors that AI systems may be prone to.
If your firm is looking to accelerate AI adoption without risk, your best bet is to adopt a secure, regulator-friendly platform.
At Claromentis, our comprehensive digital workplace solution contains a suite of practical AI tools, built with your tough regulatory requirements in mind. These tools include intent-based AI search, site-wide assistants, document-specific chatbots, and generative AI news and blogs.
Here’s what sets our AI-powered platform apart from unvetted tools and pesky shadow AI:
In addition to this, our wider digital workplace solution provides the foundations needed to bolster AI compliance and audit readiness in your firm.
The AI-enabled Policy Manager simplifies the complete policy lifecycle, from draft and distribution to tracking and competency-building. Automated InfoCapture workflows help you capture regulatory evidence and generate the precise audit trails needed to ascertain compliance. And our integrated LMS empowers you to deliver and certify targeted AI, security, and data protection training, ensuring every employee operates confidently and compliantly.
If you’d like to find out more about our secure, AI-enabled digital workplace, book a quick discussion call with one of our experts.