Artificial intelligence is becoming part of recruitment, from candidate sourcing and CV screening to scheduling, assessment and applicant communication. For Forex, CFD, FinTech and payments employers, these tools can improve productivity while introducing questions about fairness, privacy, transparency and accountability.

ACCA’s September 2026 banking and financial-services report found that 55% of respondents were not confident about AI in recruitment. Reservations extended to senior leadership: 56% of board-level, senior executive and partner respondents expressed doubts about its use in selecting talent. [1]

Certain recruitment and employment AI systems can be classified as high-risk under the EU AI Act. The European Commission’s current implementation guidance gives 2 December 2027 as the application date for relevant Annex III requirements. This does not postpone other applicable data-protection, employment or AI obligations. [2]

For regulated employers, the practical challenge is to understand and supervise how technology influences hiring decisions.

1 Identify where AI influences recruitment

AI may already sit inside your applicant-tracking system, sourcing platform, assessment software or internal mobility tools. It can also enter the process through staff using generative tools to draft job descriptions and interview questions.

Distinguish administrative support from decision influence. Scheduling an interview is different from scoring an MLRO applicant or recommending automatic rejection. The Commission identifies employment tools such as CV-sorting software among potential high-risk uses; classification depends on the system’s purpose and relevant legal criteria. [2]

For specialist financial-services appointments, a keyword match cannot establish regulatory judgment, integrity, leadership or the depth of someone’s practical experience.

Employer action: record each tool’s purpose, the data it processes, the recommendations or decisions it affects, and the person responsible for its use. Include tools used by recruitment partners where they affect your selection process.

2 Review automatic rejection criteria

Higher application volumes can encourage employers to make filters progressively stricter. ACCA’s research describes growing AI use in sourcing, shortlisting and assessment alongside concerns about bias, reduced human interaction and missed talent. [1]

In specialist recruitment, terminology can obscure relevant experience. A candidate may describe client-money reconciliations differently from your job advertisement. A banking professional may have strong transferable payments knowledge without using brokerage-specific language.

Career breaks, unfamiliar job titles or experience gained in another jurisdiction require context. These characteristics should not become unexplained substitutes for a proper assessment of capability.

Employer action: separate mandatory requirements from preferences. Sample rejected applications and examine borderline cases. Check whether credible candidates are excluded because of terminology or career structure, and amend inappropriate filters.

3 Make human oversight meaningful

A person interviewing only the five highest-ranked candidates does not necessarily provide effective oversight of the earlier screening stage. The shortlist has already shaped who gets considered.

Recruiters should understand the criteria used, recognise missing information and have authority to challenge a recommendation. Record why a decision was made rather than retaining an unexplained score alone.

This matters particularly for C-suite roles, Compliance Officers, MLROs, Heads of Dealing, Risk leaders, Finance Directors and CTOs. Technical history is only part of the assessment. Employers also need evidence of judgment, ethical conduct, leadership and the ability to work with senior stakeholders.

Employer action: assign a suitably experienced reviewer to important selection decisions. Use structured interview evidence and role-relevant assessments. Treat AI outputs as inputs to an accountable process.

4 Assess the vendor and candidate data

A product demonstration should be followed by practical due diligence. Ask what data the provider collects, where it is processed, which subcontractors can access it, how long it is retained and whether it is used for model training.

Understand the available documentation, testing, logs, error-correction process and controls over model changes. Ask what evidence supports claims about accuracy and fairness. Where demographic analysis is appropriate, ensure it is designed and handled lawfully.

The Commission’s framework describes risk management, data quality, traceability, documentation and human oversight as central safeguards for high-risk systems. Responsibility differs between providers and employers deploying them. [2]

Employer action: involve HR, Legal, Data Protection, Information Security and the relevant technology owner before deployment. Confirm contractual responsibilities and review access controls. Apply proportionate governance rather than assuming every HR tool has the same risk or regulatory status.

5 Protect trust through a clear candidate process

Candidates should understand relevant AI use, the part of the process it supports and how to raise questions or request an adjustment. Provide disclosures required by applicable law and useful explanations beyond the minimum where they improve trust.

For scarce specialist and executive talent, early human contact remains valuable. A strong passive candidate may need a substantive discussion about the role, reporting line, company and career opportunity before deciding to proceed.

Automation can handle logistics while a recruiter assesses motivations, capability and fit. Personal interaction also helps clarify experience that a CV does not fully explain.

Employer action: design the process around the role. Explain the assessment stages, identify the human contact and establish a route for concerns. Monitor candidate feedback alongside speed and recruitment cost.

Conclusion

AI recruitment can improve efficiency when employers know how it works, use relevant criteria and retain meaningful responsibility for decisions. For regulated Forex, CFD, FinTech and payments businesses, the immediate priority is clear ownership, proportionate vendor review, documented selection evidence and candidate communication.

Start with an inventory of tools and a review of screening criteria. Build governance into the process now so that faster hiring also produces decisions the business can explain and trust.

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Key sources

[1] ACCA — Global Talent Trends 2026 Banking and Financial Services Insights, September 2026

[2] European Commission — AI Act framework and implementation timeline, checked 8 October 2026

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