Pensions, mortgages, inheritance tax, retirement planning. These are the highest-stakes decisions a person makes, and they are increasingly asking AI for guidance. Financial advice sits at the very top of Google's YMYL hierarchy. If your firm lacks the structured data to prove its credibility, AI platforms will not take the risk of recommending you. A competitor with the right schema will get that referral instead.
Google classifies financial advice as YMYL - Your Money Your Life - meaning it can directly affect a person's financial wellbeing. AI platforms apply the strictest possible trust requirements before recommending any financial adviser. Without structured proof of regulation, qualifications and individual adviser credentials, your firm will not be considered.
Consider what is at stake when someone asks an AI platform to recommend a financial adviser. They might be deciding where to put their pension. They could be remortgaging their home. They might be looking for inheritance tax advice after losing a parent. These are not casual purchase decisions. They are life-altering financial choices.
AI platforms understand this. Google, ChatGPT, Perplexity and every other AI search tool applies additional scrutiny to financial advice recommendations. They are not just looking for a business that says "we are financial advisers" on a web page. They are looking for machine-readable, structured proof that the firm is FCA-regulated, that individual advisers hold recognised qualifications, and that the business has a verifiable track record.
That proof comes from schema markup. Specifically, it comes from FinancialService schema, Person schema with qualification data, and hasCredential properties that reference your FCA registration. Without these, AI platforms have no structured way to verify that your firm is what it claims to be. In a YMYL category, "unverified" means "not recommended".
The range of financial queries reaching AI platforms is broader than most IFA firms realise. These are not just generic "find a financial adviser" searches. They are specific, high-intent questions where the person is ready to take action:
Every one of these queries represents a potential client whose lifetime value could run into thousands of pounds in recurring fees. The financial advisers who appear in these AI responses are acquiring clients that their competitors never even knew were searching.
Financial advice has one of the most complex schema requirements of any local business type. This is because the YMYL classification means AI platforms demand more structured proof at every level - business type, individual adviser credentials, regulatory status and specific service areas.
People choose a financial adviser based on individual trust, not just the firm name. Person schema makes each adviser's qualifications, credentials and specialisms visible to AI - turning individual credibility into a structured signal that AI platforms can use to justify a recommendation.
Think about how people select a financial adviser. They do not just pick a firm. They want to know who will be sitting across the desk from them, what that person's qualifications are, how long they have been advising, and whether they specialise in the area that matters - pensions, mortgages, inheritance tax or whatever the need is.
AI platforms mirror this decision-making process. When someone asks "find a Chartered financial planner for pension advice in Manchester", the AI is looking for Person schema that matches those specific criteria. It wants to find an individual with Chartered status, pension advice experience, and a location in or near Manchester.
If your website lists three qualified advisers with impressive credentials but none of that information is in schema markup, AI platforms cannot see it. Your site is just text to them. Meanwhile, a competitor whose advisers have Person schema with full qualification data will be cited because the AI has structured confirmation that those people meet the searcher's requirements.
For financial advisers, Person schema is not a nice-to-have. It is the single most impactful schema type you can implement, because it turns your team's expertise and qualifications into the kind of machine-readable trust signal that AI platforms require before making a recommendation in the YMYL space.
Every legitimate financial adviser in the UK is authorised and regulated by the Financial Conduct Authority. Your FCA registration number is publicly verifiable, which makes it an ideal candidate for structured data. Through the hasCredential property in schema markup, we encode your FCA authorisation number, the issuing body (Financial Conduct Authority), and the nature of the authorisation.
This matters for AI search because it gives the platform a machine-readable, verifiable proof point. In a category where misinformation could cost someone their life savings, AI platforms want to be certain they are recommending a regulated firm. Your FCA number in schema is that certainty.
The same principle applies to Chartered status from the Chartered Insurance Institute (CII), and to individual qualifications like DipPFS (Diploma in Regulated Financial Planning) and APFS (Advanced Diploma in Financial Planning). Each of these can be encoded as a hasCredential property, building a layered trust profile that distinguishes your firm from competitors who may hold the same qualifications but have not made them available as structured data.
Google classifies financial advice as a YMYL (Your Money Your Life) topic. This means AI platforms apply the strictest E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) requirements when deciding whether to cite a financial advice firm. Schema markup is the primary mechanism through which your firm's E-E-A-T credentials become visible to AI. Without it, your qualifications, FCA status and client track record exist only as unstructured text that AI platforms cannot reliably parse or verify.
We start with a free AI Visibility Snapshot. You receive a scored report showing exactly where your financial advice firm stands in AI search, which schema you are missing, what your competitors have implemented, and what to fix first.
From there, schema implementation starts from £295. Monthly monitoring to catch schema errors before they cost you referrals starts from £79 per month, with no lock-in contracts.
For context, consider the lifetime value of a single financial advice client. Annual review fees, ongoing portfolio management charges, and the potential for additional advice needs over decades of a relationship. A single new client acquired through AI search will typically cover the cost of full schema implementation many times over. The real question is not what it costs, but how many potential clients are finding a competitor instead of you right now.
Implementation takes 48 hours from sign-off. Google typically indexes new schema within 2 to 4 weeks. AI citation visibility - meaning being recommended in ChatGPT, Google AI Overviews or Perplexity responses - usually follows within 4 to 8 weeks as those platforms refresh their data sources.
Financial advice queries benefit from the high-trust, low-competition dynamic in AI search. Because so few IFA firms have correct schema markup, the barrier to standing out is lower than you might expect. Once your firm's FinancialService schema, Person credentials and Service definitions are indexed, you enter a very small pool of firms that AI platforms can confidently recommend. In many local markets, you may be the only schema-verified financial adviser.
Get a free AI visibility report showing exactly how ChatGPT, Google AI Overviews and Perplexity currently see your firm. We will tell you what is missing, what your competitors have in place, and what to fix first.
