How AI Is Rearchitecting Lending

2026-04-23 · Source: Featured Blogs - Forrester · Field: Finance & Economics — FinTech & Digital Financial Services, Banking & Financial Services, Insurance & Risk Management · Depth: Intermediate, short

Summary

Financial services (FS) AI decision-makers are significantly increasing investments in predictive and generative AI, with over 80% planning growth, primarily targeting double-digit expansion. The immediate focus for lenders remains on scaling loan origination, reducing operational friction, and enhancing risk control. While efficiency and risk mitigation drive most AI adoption, including identity verification, fraud prevention, and credit memo generation, customer-facing genAI applications are also advancing selectively. Forward-looking lenders are experimenting with AI for personalized marketing campaigns, customer help and support (e.g., Rocket Mortgage's genAI assistant), and customer engagement (e.g., Lendi Group's Guardian). The sector anticipates conversational banking becoming central to customer engagement and agentic AI transforming experiences by integrating data from conversations and data fabrics.

Key takeaway

For CTOs and VPs of Engineering evaluating AI strategy, your focus should extend beyond back-office optimization to embedding intelligence across the entire customer journey. Prioritize investments in agentic AI and conversational banking to drive differentiated customer experiences and unlock new growth vectors, rather than merely seeking incremental efficiency gains. This strategic shift will be crucial for competitive advantage over the next 12 months.

Key insights

Lenders are increasing AI investments, focusing on efficiency and risk while selectively advancing customer-facing genAI.

Principles

• Embed intelligence from initial customer interest through goal achievement.
• Combine ML, graph analytics, and biometrics for fraud prevention.
• Agentic AI transforms experiences when embedded in the experience layer.

Method

Lenders use retrieval-augmented generation to synthesize credit memos from diverse data sources, and multimodal analysis for fraud monitoring. Agentic AI systems integrate two-way conversations with data fabrics to build borrower context.

In practice

• Use graph ML to map individual/entity networks for fraud detection.
• Implement genAI assistants for product questions and form guidance.
• Deploy agentic AI companions for continuous customer support.

Topics

• Predictive AI
• Generative AI
• Risk Mitigation
• Agentic AI
• Conversational Banking

Best for: CTO, VP of Engineering/Data, Executive, Director of AI/ML, AI Product Manager, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by Featured Blogs - Forrester.