Contributors:
Amol Ghemud Published: February 18, 2026
Summary
When someone asks ChatGPT, “What’s the best savings account in India?” or Perplexity, “Which mutual fund app should I use?”, AI doesn’t just search and rank. It synthesizes, evaluates, and recommends. Each AI platform uses different criteria. ChatGPT prioritizes market dominance and ecosystem fit, weighing Gartner Magic Quadrant rankings and Reddit sentiment heavily. Perplexity prioritizes real-time data and cost efficiency, surfacing new brands with fresh content. Claude weighs technical architecture and compliance, preferring brands that reference RBI and SEBI alignment. Google Gemini draws from existing Google Search rankings more than other platforms. All platforms use five core signals: authority and brand reputation; content structure and extractability; third-party validation (G2, Reddit, industry publications); accuracy and compliance (critical for YMYL financial content); and recency (current-year references and updated data). Understanding how each AI recommends financial products is the first step to ensuring your fintech brand is recommended.
In This Article
Share On:
How ChatGPT Decides Which Financial Products to Recommend
ChatGPT prioritizes market dominance and historical reliability when recommending financial products. Established brands with strong web presence win out over newer fintech players.
The model frequently cites Gartner Magic Quadrant and Forrester Wave reports in its recommendations. These institutional rankings act as confidence signals that the AI trusts.
Ecosystem synergy matters too. ChatGPT tends to recommend products that fit within larger platform ecosystems. If you’re a standalone app competing against a bank’s integrated suite, you’re fighting an uphill battle in ChatGPT’s recommendations.
ChatGPT was trained on web data, books, and WebText2, which is Reddit-sourced content. This means brand sentiment on discussion platforms directly influences recommendations. A product with positive Reddit momentum gets lifted in ChatGPT’s rankings.
For fintech brands specifically, ChatGPT tends to recommend established names with strong review presence on Google, Trustpilot, and similar platforms. New competitors face an adoption cliff that’s hard to overcome.
There’s a training data bias problem worth noting. GPT was trained on data that includes social norms and cultural assumptions from the internet. A Center for Financial Inclusion study found that ChatGPT provided different financial planning advice to men and women. That’s concerning because it means AI recommendations can reflect broader societal biases.
ChatGPT has 800 million weekly active users. That scale means the platform’s recommendations influence a significant market volume. When it recommends HDFC Bank over a newer neobank, that recommendation reaches hundreds of millions of people.
How Perplexity Recommends Financial Products Differently
Perplexity takes a fundamentally different approach by searching the internet in real time. Unlike ChatGPT’s training-data approach, Perplexity pulls current information and cites sources alongside recommendations.
Recency is Perplexity’s dominant signal. Recently published content with current data gets priority. That creates an opportunity for newer fintech brands. If your content is fresh, accurate, and well-cited, you can appear alongside much larger competitors.
Perplexity consistently recommends open-source, cost-efficient alternatives. This is unique among AI platforms. If you’re a fintech competing on price or transparency, Perplexity is your best bet for visibility.
Research on Perplexity’s positioning shows it operates with a libertarian capitalist stance. It’s more supportive of market-driven solutions and competitive choice than ChatGPT or Google Gemini.
The platform processed 780 million queries in May 2025. That’s significant growth. For newer fintech brands, Perplexity is where you can actually compete against household names because the playing field resets with every real-time search.
We’ve seen this firsthand with fintech clients. When a financial services firm implemented structured content and fresh data pages, Perplexity started surfacing them in competitive queries where they’d been invisible on ChatGPT.
One case study stands out: a fintech client using Perplexity Enterprise Pro reduced earnings report analysis from 48 hours to 2 minutes. That’s the kind of capability that drives adoption among institutional users.
How Claude and Google Gemini Approach Financial Recommendations
Claude focuses on technical architecture and security postures when evaluating financial products. The AI shows a preference for platforms with granular code control and high-compliance industry fit.
For fintech brands, Claude’s emphasis on compliance is a meaningful advantage. If your content references RBI and SEBI alignment, cites compliance frameworks, and demonstrates technical governance, Claude is more likely to cite you prominently.
Google Gemini operates differently. It relies more heavily on existing Google Search rankings than other AI platforms. Gemini favors content that already ranks well in traditional Google search results.
This creates a straightforward calculus for fintech marketers. If you have strong SEO and rank well in Google, Gemini will likely cite you in recommendations. But if your SEO is weak, you’re invisible on this platform too.
ChatGPT, Perplexity, Claude, and Google Gemini together account for 83% of AI search volume as of October 2025. That concentration means your AI recommendation strategy needs to address each platform separately.
The Five Signals AI Uses to Recommend Financial Products
Signal 1: authority and brand reputation
This includes your web presence, reviews, and industry recognition. Established brands win here. New fintech players need to quickly build third-party validation.
Signal 2: content structure and extractability
AI systems prefer pages with clear FAQ schema, structured answer blocks, and entity markup. Well-formatted content ranks higher than poorly organized content, even if the underlying information is identical.
Signal 3: third-party validation
Reddit mentions, G2 reviews, and industry publication citations all count. When your product gets mentioned positively on established platforms, AI systems notice and weigh your brand higher.
Signal 4: accuracy and compliance
This is where fintech wins big. RBI and SEBI references, correct interest rates, accurate terms, and updated regulatory information all signal that your brand is trustworthy. AI systems prioritize accuracy here because financial content is YMYL (Your Money, Your Life).
Signal 5: recency
Current year references, recently updated pages, and fresh data all matter. Perplexity weighs this heavily. ChatGPT is less so, but still present.
For fintech specifically, compliance signals carry outsized weight. Financial content is YMYL, which means search engines and AI systems apply stricter standards. A page that’s current, compliant, and accurate gets amplified compared to a page that’s technically similar but slightly outdated.
Research from Ahrefs shows brand mentions correlate more with AI visibility than backlinks do. That’s a shift from traditional SEO logic. For AI visibility, being talked about matters more than being linked to.
The Bias Problem: When AI gets Financial Recommendations Wrong
Gender bias exists in AI financial recommendations. The Center for Financial Inclusion documented that ChatGPT gives different financial advice to men versus women. That’s a problem because it means women might be receiving suboptimal recommendations without realizing it.
Political-economic bias is present across platforms as well. ChatGPT leans liberal. Perplexity leans libertarian-capitalist. Google Gemini is more centrist. These aren’t neutral differences. They shape which products get recommended and why.
Training data bias is the root cause. AI recommendations reflect the biases in web content, Reddit discussions, and review platforms. If a product dominates discussions on Western platforms but lacks a presence in India, AI systems will view it as more credible.
There’s also an accuracy risk. AI sometimes cites incorrect interest rates, wrong product terms, or outdated regulatory information. In financial services, that’s not just a usability problem. It’s a regulatory problem.
But here’s the virtuous cycle: fintech brands that publish accurate, RBI and SEBI-compliant content actually help AI give better recommendations. When you ensure that AI accurately represents your product, you build trust. That trust compounds over time.
The regulatory angle matters here. India’s RBI is increasingly focused on AI in financial services. That means fintech brands need to ensure AI represents them accurately. It’s not just a marketing concern. It’s a compliance concern.
How to Make AI Recommend your Fintech Product
Start with comprehensive, structured product pages. Use the FAQ schema and clear comparison data. AI systems need to extract information easily. Make their job simple.
Publish authoritative content that references RBI and SEBI compliance. Include current interest rates and verified product terms. This builds authority and trust signals.
Maintain an active presence on third-party platforms. G2 reviews, Reddit discussions, and mentions in industry publications all count. You can’t control what people say, but you can show up where people are talking.
Keep content updated with 2025 and 2026 dates. Current data matters. Perplexity especially weights freshness, but all platforms favor recent information over stale content.
Optimize for each AI platform’s specific biases. Build market dominance signals for ChatGPT. Focus on recency for Perplexity. Emphasize compliance for Claude. Optimize your SEO for Gemini.
Monitor how AI actually describes your product across platforms. Set up alerts. Check what ChatGPT, Perplexity, and Claude say about your brand. If something’s wrong, fix it immediately.
This is what GEO (Generative Engine Optimization) addresses. It’s not about ranking anymore. It’s about being recommended. The distribution model is shifting. You need to shift with it.
Start optimizing for AI recommendations today
The distribution shift is real. AI is becoming the new search layer. ChatGPT has 800 million weekly active users. Perplexity processed 780 million queries in May 2025. If you’re not optimized for AI recommendation, you’re invisible to the next wave of customers.
At upGrowth, we’ve helped 150+ fintech brands navigate this shift. We understand that AI doesn’t see products the way humans do. Structure your data. Update your content. Build compliance signals. Get recommended.
The brands that win are the ones that understand how each platform evaluates financial products and optimize accordingly.
1. How does ChatGPT choose which financial products to recommend?
ChatGPT prioritizes established brands with strong online presence, positive reviews, and authority signals such as Gartner rankings. Ecosystem fit matters too. Products that integrate with larger platforms get boosted in recommendations.
2. Does Perplexity recommend different fintech brands than ChatGPT?
Absolutely. Perplexity surfaces newer brands with recent, well-cited content. If you’re a newer fintech with current data and structured pages, Perplexity is where you can compete against established players.
3. Can I influence which financial products AI recommends?
Yes, but not through traditional marketing. You need structured content, third-party validation, accuracy signals, and compliance references. Build authority over time. Monitor what AI says about you. Correct inaccuracies immediately.
4. Are AI financial recommendations biased?
Yes. ChatGPT shows gender bias in financial advice. Different platforms have different political-economic leans. Training data biases are baked in. You can’t eliminate this, but you can ensure your brand has accurate, compliant information online so that AI represents you fairly.
5. Does AI check if financial product information is accurate?
Not systematically. AI relies on recency, structural signals, and consensus. If incorrect information shows up repeatedly across sources, AI might amplify it. This is why having accurate, SEBI-compliant data on your own pages matters so much.
6. How does RBI and SEBI compliance affect AI recommendations?
Compliance references are trust signals. When you mention RBI or SEBI alignment, you’re signaling that your content is regulated and trustworthy. AI systems weigh this heavily because financial content is YMYL.
7. What is GEO, and how does it help fintech brands get recommended by AI?
GEO is Generative Engine Optimization. Instead of optimizing for traditional search rankings, you optimize to be recommended by AI systems. It’s about structure, authority, compliance, recency, and third-party validation. The distribution channel has shifted. GEO is how you win in that new channel.
Amol has helped catalyse business growth with his strategic & data-driven methodologies. With a decade of experience in the field of marketing, he has donned multiple hats, from channel optimization, data analytics and creative brand positioning to growth engineering and sales.
Growth Strategy and Planning
