PwC survey: Indian fintechs push AI despite 84% yet to see financial returns
PwC India’s survey of 31 fintech respondents finds firms investing in AI while preparing for tighter capital and consolidation. Embedded lending is viewed as a durable 2030 opportunity, but trust, fraud and security remain key hurdles for agentic payments and data-sharing adoption.
What happened
PwC India’s fintech survey finds aggressive AI investment despite limited measurable returns, with firms preparing for tighter capital and consolidation.
Key facts
- 84% of respondents have not seen measurable AI-driven financial results
- 71% are building for a 'Disciplined Consolidation' scenario
- 48% rank proprietary intelligence as their top strategic bet
- 30% cite customer-experience enhancement as the main AI-agent adoption driver
- 22% cite cost reduction and productivity gains
- 74% expect an enabling capital and regulatory climate by 2030
- 41% cite customer trust and adoption as the top barrier to agentic AI
- 33% cite fraud, security and risk management as a barrier
- 93% say customers would share financial data with AI agents for better recommendations
- 7% say customers would use AI agents if fraud risk increased slightly
- 77% identify embedded lending as a durable 2030 value pool
- 55% rank trust and transparency as most important for digital-native consumers
- 10% rank lowest cost as most important
- 31 survey respondents
Why this matters
Target partnerships or acquisitions in fraud prevention, identity, consented data-sharing and embedded lending, where infrastructure can unlock durable omnichannel payment growth.
What to watch
- RBI or Indian regulatory guidance on agentic payments, data-sharing consent, digital lending and AI accountability.
- Reported fraud, scam and chargeback rates for AI-assisted or delegated-payment journeys.
- Fintech funding rounds, down-rounds, M&A and vendor exits indicating accelerated consolidation.
- Merchant adoption of embedded lending and changes in approval rates, repeat purchase frequency and delinquency.
- Evidence of AI ROI in fintech earnings or operating metrics, especially cost-to-serve, fraud losses and conversion.
- Consumer uptake of delegated shopping or payment agents versus abandonment at identity and consent checkpoints.
- Prioritise AI use cases with directly auditable economics: fraud-loss prevention, chargeback reduction, collections, conversion uplift and support deflection.
- Require agentic-payment partners to provide clear customer consent flows, transaction limits, real-time alerts, dispute handling and liability allocation.
- Build retail-fintech data partnerships around consented transaction and loyalty data, with alternative underwriting models tested in limited merchant cohorts.
- Use embedded credit selectively for repeat, high-trust customers and merchants; monitor delinquency by channel, basket type and customer tenure.
- Review payment and commerce vendors for capital durability, regulatory readiness and ability to consolidate multiple AI, fraud and identity functions.