Lending Is Reaching An Inflection Point

More than 80% of financial services AI decision‑makers plan to increase investments in both predictive AI and generative AI, with most firms expecting double‑digit growth, according to Forrester’s data. The immediate focus for the majority of financial services leaders remains pragmatic: scaling origination, reducing friction caused by handoffs, and improving risk control. But the leaders aren’t those who only optimize for middle to back-office workflows; lenders who embed intelligence from the very first moment a customer expresses interest and intent all the way through to helping them achieve their goals will outpace those who don’t.

Efficiency And Risk Mitigation Remain The Primary Drivers

Despite growing interest in customer‑facing use cases, most lenders continue to focus efficiency gains and risk mitigation such as:

  • Identity verification and fraud prevention. By combining machine learning, graph analytics, behavioral biometrics, and liveness detection to counter deepfakes and synthetic identities. HSBC used graph ML to map the “network” of an individual or entity to uncover insights or relationships to other individuals, events, or entities that might indicate fraudulent behaviors.
  • Fraud and AML monitoring. By using multimodal analysis of documents, transactions, and unstructured data to detect evolving threats and reduce false positives. BCU Bank leveraged network, forensic, semantic, and preceptor detectors as well as relational links to uncover fraudulent documents (e.g., a repeated balance on a bank statement), preventing $5.6 million in losses in the first nine months of 2025.
  • Credit memo generation. By leveraging retrieval-augmented generation to extract key insights from submitted documents, financial statements, and internal taxonomies as well as third-party news, market data, and financial filings, lenders can use genAI to synthesize this data into a concise credit memo that provides a borrower overview, financial and risk analysis, covenants, and a recommendation.

Customer‑Facing GenAI Adoption Is Advancing, But Selectively

While interest in customer-facing and domain-specific applications is growing, most financial institutions continue to proceed cautiously due to regulatory scrutiny, integration complexity, and the need for high levels of accuracy and explainability. Forward-looking lenders are experimenting with and scaling AI across:

  • Personalized marketing campaigns. Using ML, deep learning, predictive AI, NLP, and genAI, lenders can map and generate marketing content with language that aligns with specific emotional triggers for an individual.
  • Customer help and support. Rocket Mortgage has built a customer-facing genAI assistant called Rocket AI Agent, with eight domain-specific agents invoked via a centralized orchestration layer to help with a number of tasks, such as providing answers to product questions like rates, options, and processes; guiding borrowers through preapproval forms; and helping borrowers schedule payments.
  • Customer engagement. Built on an agentic AI architecture, Lendi Group launched Guardian, a mobile-first, AI-powered companion that serves as the conversational front door to the group’s agentic platform. Under the hood, Guardian is a multiagent system powered by more than a dozen specialized agents that continuously supports customers at key moments by surfacing insights that matter, such as interest rate saving alerts, equity changes, and suburb price growth. 

Use AI To Unlock The Next Phase Of Value Creation

The reality is this: most lenders will struggle to unlock new growth vectors. Fixated on using AI as a tool for business-as-usual efficiency, not as a strategic lever, many lenders will face diminishing marginal gains. To unlock the next phase of value creation, lenders need to shift from leveraging AI as a tool for efficiency to leveraging it as a driver of growth, differentiated experiences, and competitive advantage. Over the next 12 months, we expect:

  • Conversational banking to become the cornerstone of customer engagement. The next iteration will replace menus with a single conversational bar, where borrowers are guided from initial intent through to loan management, with the overall goal of debt reduction and wealth creation. It’s a paradigm shift, not an incremental change or a UX upgrade.
  • Agentic AI will act on behalf of people and software, but it won’t be 100% autonomous. Early agentic AI lending implementations focus on middle- and back-office operations; however, truly transformative outcomes only emerge when agentic AI is embedded in the experience layer. What this looks like: an adaptive system that guides borrowers through their end-to-end journey by integrating data from two-way conversations (between borrowers, AI agents, and brokers) with the data fabric. These combined insights build borrower preferences and context knowledge, helping agents plan, select the right tools, self-reflect, and improve their decision-making.

Read my latest report, “The State Of AI In Lending, 2026 | Forrester” for a deep dive into these insights. Got a question? Book in a guidance session with me.