A senior marketing leader at an Indian bank ran a quiet experiment last quarter. They typed ‘best savings account interest rate India’ into ChatGPT, Perplexity, and Google’s AI Overview – the three surfaces increasingly used by Indian consumers researching financial products before they ever visit a comparison site or a bank’s homepage.
Their brand didn’t appear in a single answer.
The brand has a national footprint, several thousand crore in deposits, and consistent paid media spend across digital and television. None of that translated to citation in the moment of consideration. The AI didn’t see them.
This is a measurable, well-understood phenomenon – and once a brand sees how AI citation actually works, the path to being present in the answer becomes a question of engineering, not luck.
The shift isn’t coming. It’s already happened.
Gartner has projected that traditional search engine volume will fall by around 25% as users migrate to AI-powered conversational assistants for high-intent queries (Gartner, 2024). IDC’s 2024 research found that 45% of users already rely on generative AI tools for research and brand discovery, and McKinsey’s 2025 State of AI survey reported that 79% of organisations globally are now actively deploying generative AI. The migration the earlier forecasts described is no longer a forecast – it is the current behaviour of the consumer researching a financial product.
For BFSI specifically, the exposure is amplified by the nature of the category’s high-intent queries. ‘How to apply for FASTag.’ ‘Is this bank safe for my savings.’ ‘Best fixed deposit rates for senior citizens.’ ‘Difference between savings and current account.’ These are exactly the query patterns AI Overview, ChatGPT, and Perplexity now answer directly – increasingly without sending a click through to any brand’s website.
Which brands appear in those answers is decided by the authority signals AI models can read – and those signals are something a brand can deliberately build.
What AI models actually read when they decide which brand to cite
Most Indian banks have spent the last decade building branded authority. The strategies that won the previous era – SEO for keyword rankings, performance marketing for direct response, brand campaigns for awareness – did exactly what they were designed to do.
But AI models don’t rank pages. They evaluate sources. They look for topical depth across the entire web, structured signals that search engines can read, third-party citations that validate authority, and content patterns that signal expertise rather than promotion.
Most large BFSI sites, when audited against these criteria, reveal three structural conditions. Schema markup that is missing or inconsistent across product pages. Link equity that has bled away through years of site migrations and pages deleted without 301 redirects. Keyword architecture aligned with how the brand wants to be searched, not how Indian users actually search.
These are not branding failures. They are the kind of technical decay that accumulates quietly on any large legacy site over multiple platform updates and content cycles.
The work is to diagnose each of these conditions precisely and rebuild the foundation underneath them – methodical, technical, and entirely within a brand’s control.
What ‘AI-ready’ looks like – in measurable terms
The foundation work isn’t speculative. It produces measurable results inside traditional search before AI search even enters the picture.
One recent BFSI engagement makes the pattern concrete. A leading Indian payments bank rebuilt its organic foundation across mid-2025 – working across keyword architecture, technical recovery, on-page signal strengthening, and off-page authority amplification, in partnership with Lyxel&Flamingo’s Search Intelligence practice. Non-branded performance shifted measurably across every metric.
Generic click-through rate lifted by 167%. Generic ranking improved by 62% – moving the brand from page two and three of generic search results into consistent first-page visibility. Total organic clicks grew by 39% across the site. Generic clicks specifically – the audience the brand was not yet reaching – grew by 73%.
Source: Google Search Console, June-October 2025 measurement window versus the immediately preceding three-month period.
What that proves is not that GEO works. It proves that the foundation underneath GEO – the technical authority signals AI models are evaluating right now – can be rebuilt methodically, and the gains compound in both traditional and emerging search surfaces simultaneously.
And the compounding has already shown up in the AI surfaces themselves. Across the four months from November 2025 to March 2026, the bank’s citations within Google’s AI Overview grew by 355%, and AI Overview impressions grew by 423%. On Microsoft’s Bing AI surface – where it started from a near-zero citation base – it moved from effectively no presence to consistent citation, with the number of unique brand pages cited within Bing AI rising by 208% across the same window.
Source: Ahrefs and Bing Webmaster Tools, November 2025 to March 2026 measurement window.
Traffic from LLM-driven sources to the site grew by 139% across the same period, as visits arriving from ChatGPT, Perplexity, Gemini, and Copilot consolidated into a measurable acquisition channel that did not exist for the brand twelve months earlier (Source: GA4).
The brand moved from effectively absent in AI-generated answers to consistently cited at scale – a direct result of rebuilding the authority signals those surfaces read, in the order they read them.
The three layers of AI authority for BFSI
Across the BFSI engagements we audit, three layers consistently determine whether a brand becomes AI-citable.
The first is topical depth at the product page level. Every core product needs a single ranking page with structured schema markup, clear question-format headings, and content depth that answers user queries directly. In banking, that means savings and current accounts, fixed and recurring deposits, FASTag, and debit and credit cards. In lending and finance, personal loans, home loans, loan-against-property, gold loans, and EMI products. In securities and capital markets, demat and trading accounts, mutual funds, SIPs, IPO access, and bonds. In insurance, term life, health, motor, and ULIP products – each with its own page built to answer the specific questions a buyer asks, rather than a single category page that forces the user to navigate further.
The second is multiformat signal across the ecosystem. AI models weight YouTube explainers, structured Reddit and Quora answers, podcast transcripts, and editorial citations as authority signals – and in BFSI these formats carry real weight because consumers research financial products socially before they commit. A bank’s deposit-rate explainer, a broker’s walkthrough of opening a demat account, an insurer’s claims-process video, an NBFC’s loan-eligibility guide: each is a citable signal. Brands that exist only on their own website are at a structural disadvantage to those with full-spectrum presence across the surfaces where buyers actually ask questions.
The third is third-party validation. Analyst and rating-agency coverage, named PR placements in tier-one financial publications, reviews on independent comparison and aggregator platforms, and citations from financial advisors, distributors, and creator-economy voices – all strengthen the trust signals AI models read. The validation that matters varies by vertical: rating-agency mentions and regulatory standing for banks and NBFCs, advisor and fund-platform coverage for securities, and claims-settlement ratios and independent review aggregators for insurance. In a category where trust is the product, these external signals carry disproportionate weight in deciding which brand a model cites.
Why AI authority, once built, holds
There is a genuine reason this work rewards brands that build deliberately. AI models update their citation patterns based on accumulated authority signals, so the credibility a brand establishes compounds: each additional structured page, each third-party citation, each well-formed answer adds to a base that the models read as consistency over time. That accumulated authority is durable precisely because it cannot be bought back quickly through paid effort – it is earned at the foundation layer and it holds.
For Indian BFSI specifically, where consumer trust is the entire product, being absent from the answer is not a marketing problem. It is a category positioning problem – solved at the foundation layer, not the campaign layer.
When a consumer asks an AI assistant where to put their money, the answer is assembled from authority signals that are visible, measurable, and buildable. A BFSI brand that understands those signals – and builds for them deliberately – earns its place in that answer on the strength of the work itself.
The test isn’t Google anymore. It’s the answer that comes before Google.
A self-audit: is your brand built to be cited?
Run your own brand against the three layers. Each question can be answered honestly in a few minutes, and the answers map directly to where the foundation work begins.
Layer one – topical depth
- Does every core product – across banking, lending, securities, and insurance – have its own dedicated page, rather than sharing a single category page?
- Do those pages carry structured schema markup, applied consistently across the product set?
- Is each page written in the language a buyer actually uses to ask – in clear question-format headings – rather than internal product naming?
Layer two – multiformat signal
- Does your brand’s authority exist beyond your own website – in video explainers, structured answers on Reddit and Quora, podcasts, and editorial coverage?
- For each major product, is there at least one strong asset on the surface where buyers research it – a deposit-rate explainer, a demat-account walkthrough, a claims-process video, a loan-eligibility guide?
Layer three – third-party validation
- Is your brand referenced by sources outside your control – rating-agency or analyst coverage, named PR in tier-one financial publications, and independent comparison or review platforms?
- Do the validation sources that matter most in your vertical cite you – rating standing for banks and NBFCs, advisor and fund-platform coverage for securities, claims-settlement ratios and review aggregators for insurance?
Every question answered with a confident yes is a signal already working in your favour. Every no is not a gap to fear – it is a specific, buildable piece of the foundation, and it is the most useful place to start.
About Lyxel&Flamingo Search Intelligence
Lyxel&Flamingo Search Intelligence is the agency’s integrated practice for organic visibility across every surface where consumers find brands – traditional search (SEO), AI-powered answer engines (GEO), and hyperlocal discovery (Google Business Profile and store-level search). The practice works with leading BFSI, consumer, and enterprise brands across India to build the foundational technical and editorial authority signals that determine brand presence in Google Search, Google AI Overview, ChatGPT, Perplexity, Bing AI, Gemini, and Maps-driven local search.
Lyxel&Flamingo is one of India’s largest independent creative, technology, and digital growth agencies, with offices across Gurgaon, Mumbai, Bangalore, and Dubai, and a presence in the UK. The agency works with over 350 brands across BFSI, FMCG, retail, consumer durables, beauty, and emerging consumer categories, managing more than $250 million in annual media, and holds top-tier platform partnerships with Google, Meta, Amazon, Flipkart, Myntra, and Shopify.
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