AI Overviews Influence Financial Decisions in India: What the DICA Pattern Means for BFSI RegTech Procurement

The DICA model describes buyers who do deep research online but finish their purchase with human help.

Google and McKinsey & Company defined this pattern in their whitepaper, “Decoding the DICA consumer: Redefining insurance distribution for India’s discerning buyers.” Presented at Global Fintech Fest 2026, the report draws on data from Axis Max Life Insurance, HDFC ERGO General Insurance, and PolicyBazaar. The underlying Google and Ipsos survey of 811 in-market Indian adults (July 2025) highlights this trend. The study found that 41% made a purchase decision based on information from AI Overviews when evaluating insurance, cards, loans, mortgages, or investment accounts.

In Indian insurance, digital channels directly yield under 6% of life premiums and under 15% of health premiums, per the IRDAI Handbook on Indian Insurance Statistics 2024-25[2]. Yet digital research touches over 20 million life and health policies sold each year, representing over 40% of the 50 million total policies sold nationwide.

By comparison, over 5 million policies sell fully online without human help. Digitally influenced policy volume is four times larger. Over 7 million of these represent measurable digital influence, where consumers researched online and submitted contact details before buying offline with an advisor.

Financial search queries in India are growing longer and more conversational, supported by surging mobile app usage and strong non-metro growth.

Search habits are shifting from short keywords to complex queries. Ipsos and Google data shows that 18% of insurance searches are long queries. These detailed questions are growing faster than short head terms.

Mobile engagement is expanding rapidly. Platform data from SensorTower[3] shows that monthly active users on Indian insurance mobile apps grew almost sixfold from FY22 to FY26. This shift builds on India’s digital base of 950 million internet users, 800 million video viewers, 430 million UPI users, and over 100 million unique investors, per IAMAI and Kantar[4], IBEF, and Value Research.

Digital influence is also high outside Tier-1 metros. The Google and McKinsey report shows the top 20 non-metro cities have digital influence rates twice the national average, led by salaried workers aged 25 to 45.

Digitally researched buyers bring higher revenue, but poor handoffs between digital tools and human agents create friction.

The DICA whitepaper outlines three main points of friction:

• •Assisted conversations start from zero because agents cannot see what buyers researched online.
• •Human help arrives late, after initial buyer interest has decayed.
• •Buyers receive conflicting terms and pricing between web pages and human agents.

When resolved, digitally influenced buyers bring higher commercial value. Data from the IRDAI Handbook 2024-25 shows that in health insurance, digitally influenced buyers generate roughly 1.4 times higher ticket sizes and 1.2 times higher rider attachment. Agent interviews from August 2026 confirm that digitally sourced buyers arrive better prepared, ask sharper questions, and seek validation rather than basic education.

Enterprise software buyers in banks, NBFCs, and fintechs follow a path that closely mirrors consumer DICA habits.

Enterprise technology leaders rarely buy critical software from unsolicited pitches. Chief Risk Officers, Heads of Compliance, CISOs, and Product Leads follow the DICA model instead. They use search engines and AI summaries to discover vendors, benchmark capabilities, and check compliance.

Once teams form a shortlist, procurement shifts to assisted evaluation. Enterprise deals require hands-on proof-of-concept tests, security audits, and RFP defenses. Digital discovery builds the shortlist, while technical validation closes the contract.

A research boundary applies here. The Google and Ipsos survey covered 811 retail consumers buying personal financial products. No public survey measures how many enterprise BFSI risk officers use AI Overviews to pick vendors. Applying DICA to B2B procurement is an analytical analogy based on shared search mechanics.

Generative Engine Optimization requires direct, factual answers and precise regulatory details so AI models cite the page.

Modern search engines generate direct answers at the top of results pages. Under Section 5.1 of KYCKART’s search strategy framework[5], Generative Engine Optimization (GEO) requires an answer-first content format. This standard places direct, factual 40-to-80-word answers in the first two to three sentences of each section for language models to extract.

AI search tools value regulatory precision over marketing copy. They favor statutory citations, clear data tables, and explicit operational limits over promotional claims. While exact search weighting formulas remain private, factual guides reliably earn higher citation visibility.

Reports from exchange4media[7] and Adlift confirm that traffic is concentrating on trusted source domains. In B2B RegTech procurement, vendor sites that fail to answer core regulatory questions get bypassed by AI engines in favor of clear technical documentation.

The Reserve Bank of India requires banks to maintain strict board oversight, continuous monitoring, and full audit rights over outsourced software vendors.

Technology vendor selection in Indian banking must meet formal regulatory rules. Under the Reserve Bank of India’s outsourcing directions for commercial banks[6] issued on 28 November 2025, Chapter IV sets mandatory rules for IT services. Regulated banks must keep board-level risk controls, retain audit access, and run ongoing checks on software vendors.

Banks cannot pass regulatory accountability to third parties. When evaluating RegTech tools, risk officers check specific statutory capabilities:

• •Verifying remote onboarding against central bank rules, as covered in our guide on video KYC versus Aadhaar e-KYC.
• •Ensuring onboarding systems keep audit trails, as explained in our review of customer onboarding software for banks and NBFCs.
• •Confirming lending apps follow borrower protection rules, as detailed in our guide on digital lending KYC verification and our study on consumer trust in digital lending.

RegTech vendors that openly publish these compliance details help bank buying teams complete required vendor checks with confidence.

For BFSI risk and compliance teams, the rise of AI search means vendor evaluation must shift from passive sales reviews to active verification.

Read together, the fourfold lead of digitally influenced sales over pure web transactions and the rapid growth of complex queries show that buyers research deeply before talking to sales teams. This suggests that internal teams enter vendor calls with clear expectations shaped by AI Overviews and online documentation. The practical implication is that compliance officers must evaluate vendors more systematically.

To adapt to this changing procurement environment, compliance and risk leads can take three practical steps: