By Brad Powar
AI adoption in financial advice is gathering pace. According to Opinium (2024), 14% of Independent Financial Advisers (IFAs) already use AI tools, with a further 23% planning to adopt them in the next 12 months. These tools are typically applied to administrative workloads, operational efficiency and in particular cases, low-value clients, and are increasingly seen as a lever to improve client management and productivity. This aligns with broader trends in financial services, where AI is being deployed for onboarding, risk assessment, content generation, and fraud detection (Oliver Wyman & UK Finance, 2023).
However, while the opportunities are well publicised, risks around AI adoption are less frequently scrutinised. The Financial Conduct Authority (2024) recently launched a live testing regime to assess AI’s role in regulated advice, an indication that the regulator sees both the potential and the pitfalls. This is particularly relevant for IFAs, whose reputations rest on fiduciary responsibility and client trust. Errors caused by poor vendor choice or immature technologies could affect not only firm operations, but thousands of clients.
The market is currently flooded with early-stage and non-specialist providers. Many are selling early stage solutions with high technical promise but low regulatory alignment. Case studies such as Bench (TechCrunch, 2024) and Skybox (Calcalistech, 2025) demonstrate the risk to continuity when AI vendors collapse, taking client data and critical infrastructure with them.
Given that many AI vendors lack experience in financial services (particularly in regulated markets), risks around compliance, explainability, data security, and resilience are elevated. A recent study by UK Finance and Oliver Wyman (2023) noted the difficulty financial firms face in validating AI models developed by third parties, particularly when the internal logic is opaque or not designed with sector regulation in mind.
This report outlines the core use cases of AI for IFAs, and assesses the associated risks, particularly those stemming from poor vendor selection. It concludes with a set of due diligence considerations and regulatory best practices for firms looking to adopt AI responsibly.
Artificial intelligence (AI) is progressively becoming integral to the financial advice market, with nearly two in five IFAs either currently using or planning to implement AI solutions within the next year (Opinium, 2024).
According to recent insights from Opinium’s Voice of the Adviser Q4 Pulse report, 14% of advisers already leverage AI, and a further 23% anticipate adopting it shortly, highlighting a clear trend toward digital transformation in financial services.
Advisers predominantly view AI as beneficial for managing lower-value clients, enhancing operational efficiencies, and significantly reducing the time spent on repetitive and administrative tasks (Opinium, 2024).
Despite initial reservations about automation displacing jobs within the sector, there is a growing consensus, with a net positive perception (+21%), indicating that advisers increasingly recognize AI’s potential to improve productivity and client management (Opinium, 2024).
AI use cases in the financial advisory market are becoming increasingly sophisticated. Financial advisers are leveraging AI primarily in areas such as automated client onboarding, risk assessment, and financial planning. For example, predictive AI models are extensively used by larger wealth managers and banks to:
Moreover, AI is increasingly employed (mostly by larger institutions) to offer personalised investment strategies tailored to individual client profiles, significantly improving client engagement and retention (FT Adviser, 2023). These personalised strategies utilise AI to analyse large datasets encompassing client behaviour, market conditions, and historical financial performance. This capacity not only enhances the precision of financial advice but also helps advisers proactively manage risks and optimise client portfolios in response to dynamic market conditions (Unbiased, 2023).
As financial advisers seek competitive advantages and operational efficiencies, the integration of AI is set to continue, reshaping how advice is delivered and how client relationships are managed (Opinium, 2024).
While the integration of AI presents substantial opportunities for financial service providers, it also introduces a spectrum of risks, particularly pronounced when institutions partner with unsuitable AI vendors or fail to manage deployments responsibly.
Partnering with third-party AI vendors can expose sensitive client data to external breaches if those vendors lack robust data governance. UK Finance and Oliver Wyman (2023) warn that even interactions with AI models can extract personal data from training sets, raising significant regulatory and reputational risks.
Many AI vendors lack familiarity with financial sector regulations, workflows, or risk models. The Bank of England and FCA (2024) note that this can result in poorly performing or non-compliant models. The Financial Stability Board (2024) highlights that weak governance controls from such vendors increase the likelihood of operational failure and biased outcomes.
AI models often operate as “black boxes,” making it difficult for advisers to understand or justify decisions. Maple et al. (2022) observe that vendors offering limited model transparency hinder firms’ ability to detect bias, explain outcomes to clients, or satisfy regulatory requirements.
Poorly vetted AI systems may replicate existing data biases, leading to discriminatory outcomes like unjustified credit denials. According to the Financial Accountant (2023), inadequate bias mitigation strategies heighten firms’ exposure to legal, ethical, and reputational harm.
AI systems deployed without sufficient validation may produce inaccurate outputs. Unbiased (2023) highlights the risk of “hallucinations”, AI-generated errors that, in critical use cases such as fraud detection or investment advice, can lead to major financial and reputational damage.
UK Finance and Oliver Wyman (2023) identify AI-generated deepfake audio as a tool for financial fraud. With real-time video deepfakes emerging, current ID verification methods may be undermined, exacerbating fraud risks.
Over-reliance on the same third-party providers can standardise vulnerabilities across institutions. Maple et al. (2022) note that a breach or failure at a widely used vendor could result in cascading disruptions to financial stability and market operations.
The Bank of England and FCA (2024) caution that many firms are trialing early-stage technologies, often marketed as innovative but unfit for regulatory environments. Early-stage solutions lacking clear ROI or maturity pose operational, compliance, and reputational risks, especially for smaller firms with limited AI expertise.
AI reliance introduces new failure modes, such as data drift, degraded models, or erroneous outputs, that traditional risk frameworks may not capture. The UK Finance and Oliver Wyman (2023) report urges firms to update their resilience planning accordingly.
Dependency on small or unstable AI vendors creates continuity risk. The collapse of Bench (TechCrunch, 2024) and Skybox (Ctech, 2025) left thousands of clients without access to core financial services. Failures in vendor service levels or cybersecurity can disrupt operations and damage customer trust.
As AI becomes increasingly embedded within the financial services industry, IFAs must exercise careful judgment in selecting appropriate AI partners. The right partnership has the potential to transform operational efficiency and client outcomes, whereas the wrong choice can expose firms to serious regulatory, reputational, and financial risks.
Strong data governance and privacy protocols are critical. The Bank of England and FCA survey (2024) also identified data risks as one of the most prevalent concerns surrounding AI adoption. Given the sensitivity of client financial information, advisers must ensure that any prospective partner adheres to stringent data protection standards, including full compliance with GDPR and other sector-specific privacy regulations.
An AI partner’s familiarity with the financial services domain is another key selection criterion. Research from Gartner (2024) noted that a substantial proportion of finance functions (62%) currently lack formal processes to assess and prioritise AI applications. Choosing a partner with demonstrated experience in financial services increases the likelihood that solutions will be fit for purpose, effectively aligned with regulatory expectations, and capable of addressing industry-specific challenges.
Transparency and explainability must be regarded as non-negotiable criteria. According to the Bank of England and Financial Conduct Authority (2024), almost half of financial services firms reported only a partial understanding of the AI systems they use, often due to reliance on opaque third-party models. Without clear insights into how AI solutions operate, advisers may be unable to justify advice outcomes to clients or regulators, undermining trust and compliance efforts – which could potentially lead to sanctions.
To mitigate the risk of over-reliance on technology, the scope of automation offered by the AI partner must also be considered carefully. The Bank of England (2024) reported that more than half of AI use cases within financial services involve some form of automated decision-making. Advisers must determine whether the degree of automation aligns with their business model and whether sufficient human oversight will be maintained to guard against over-reliance on technology.
Third-party dependencies represent a further area of risk. The same Bank of England study (2024) found that a third of AI applications in financial services are built on third-party infrastructure, with significant concentration among a few major providers. Advisers should therefore assess the extent to which a prospective partner’s solution depends on external technologies, as excessive reliance can introduce systemic vulnerabilities and complicate accountability.
Governance frameworks must also be scrutinised. Encouragingly, the Bank of England (2024) found that 84% of firms have appointed a designated individual accountable for AI risk management. Advisers should verify that any potential partner has similarly robust internal governance, with clearly defined roles and effective mechanisms for risk identification, escalation, and mitigation.
To safeguard business continuity, firms must evaluate AI vendor dependencies with the same rigour applied to core infrastructure and ensure redundancy, auditability, and human oversight are built into AI-driven processes from the outset.
Finally, regulatory awareness and alignment are indispensable. The Financial Stability Board (2024) has highlighted AI’s potential to amplify systemic financial risks if deployed without adequate controls. In particular, the Bank of England (2024) has indicated that future stress tests may include AI-driven market shocks. Advisers must therefore favour partners who are actively engaged with regulatory developments and who can demonstrate a commitment to safe, ethical, and compliant AI usage.
As the adoption of AI accelerates across financial services, advisers and regulated firms must hold their vendors to increasingly rigorous standards of transparency. At a minimum, AI providers should be required to report on their internal practices for detecting and mitigating algorithmic bias, safeguarding client data, and ensuring system security and resilience. The U.S. National Institute of Standards and Technology’s (NIST) AI Risk Management Framework (2023) outlines this expectation clearly, identifying bias management, security assurance, and accountability as core principles of trustworthy AI. NIST further recommends that all AI actors, developers, deployers, and integrators, adopt formal governance and reporting processes across the AI lifecycle, from data sourcing to real-world impact measurement (NIST, 2023)
Selecting the right AI partner is not merely a procurement exercise; it is a strategic decision with profound implications for the adviser’s reputation, client relationships, and operational resilience. A thorough evaluation, grounded in transparency, domain expertise, data governance, and regulatory awareness, is essential to ensure that the benefits of AI can be realised without exposing firms to unnecessary risk.
From a regulatory perspective, new obligations are emerging to reinforce these expectations. In the U.S., the Securities and Exchange Commission has proposed rule 206(4)-11 under the Investment Advisers Act, which would mandate due diligence and ongoing monitoring of service providers performing ‘covered functions’. This includes AI vendors whose technology is integral to the adviser’s ability to deliver compliant services. Under the rule, advisers would be expected to assess vendor competence, risk controls, data integrity, subcontracting arrangements, and termination procedures, and document these as part of their regulatory recordkeeping requirements (SEC, 2022)
The rule also affirms that investment advisers remain liable for their fiduciary duties even when critical services are outsourced, and disclosure alone does not relieve them of this responsibility.
In the UK, while there is not yet a standalone AI regulation, advisers are subject to the FCA’s operational resilience regime and consumer protection duties. These require firms to identify material suppliers, understand the risks they pose, and ensure that clients are not exposed to harm from undisclosed system failures or data misuse. As such, advisers should embed clauses into contracts requiring vendors to notify them of incidents affecting data integrity, model performance, or client-facing outcomes, and to maintain logs of their bias audits, security assessments, and risk mitigation efforts. These records will be essential not only for compliance but also for defending client trust in an increasingly AI-mediated advisory landscape.
Selecting the right AI partner is not merely a procurement exercise; it is a strategic decision with profound implications for the adviser’s reputation, client relationships, and operational resilience. A thorough evaluation, grounded in transparency, domain expertise, data governance, and regulatory awareness, is essential to ensure that the benefits of AI can be realised without exposing firms to unnecessary risk or worse, such as permanent reputational damage, financial loss, and erosion of client trust that may be impossible to recover.
In a sector built on confidence and continuity, a flawed AI integration is not just a risk, it’s a threat to the very foundation of the financial advisory business.
