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Hyper-Personalized Financial Advisory

LLM-powered intelligent financial guidance at scale, without the headcount.

Personalized financial advice has historically been the privilege of high-net-worth individuals. Mass-market customers receive generic product recommendations that do not reflect their actual financial situation, goals, or risk tolerance. Financial institutions struggle to cost-effectively serve millions of customers with individualized guidance — resulting in low engagement, high churn, and missed cross-sell opportunities.

QUAPT develops conversational financial advisory agents grounded in each customer's real financial data — account history, spending patterns, income trajectory, life events, and stated goals. The AI agent synthesizes this context with market intelligence, product eligibility rules, and regulatory guardrails to deliver personalized, actionable financial guidance. The system escalates complex situations to human advisors with a full context handoff — creating a seamless human-AI advisory model.

  • Secure integration with customer financial data via open banking APIs
  • Goal-based financial planning and scenario modelling engine
  • Product recommendation engine with eligibility and suitability checks
  • Conversational AI interface (chat, voice, in-app)
  • Life event detection and proactive outreach triggers
  • Regulatory suitability assessment and documentation
  • Seamless human advisor escalation with context transfer

3.4x

Increase in Customer Engagement

45%

Improvement in Product Adoption

62%

Reduction in Churn

5x

Scale vs Human Advisors

  • Democratized access to quality financial guidance across all customer segments
  • Significant uplift in product cross-sell and upsell revenue
  • Improved customer retention through proactive, personalized engagement
  • Reduced cost-to-serve while increasing service quality and frequency
  • Scalable advisory capacity without proportional headcount increases
  • Stronger regulatory compliance through consistent suitability documentation