Source: Wall Street Journal - 04/08/2025
In today’s tech-saturated financial landscape, where every new platform flaunts the letters “AI” like a badge of honor, taking a radically different approach means leading with security and compliance first.
As many financial institutions rush to modernize to stay competitive in an ever-evolving landscape, many are adopting AI tools before addressing a more foundational question: Can this system even be trusted with my clients’ sensitive data?
Most “AI-ready” startups rush to market by stitching together off-the-shelf, open-source codebases and third-party APIs. This mosaic approach might accelerate time to market, but it often leaves core processes fragmented, unmonitored and vulnerable.
In lending, where systems touch everything from borrower financials to bank statements, that disjointed approach poses a critical risk. The concern isn’t hypothetical as AI models are trained not just on public datasets, but also on anything they can ingest—often including your sensitive data, especially if terms of service quietly allow it. And once that sensitive data is trained in the model, it can’t be unlearned or erased.
Platform structure is everything, especially when it comes to lending. Before AI can be safely deployed, the systems it is built on—including document ingestion, borrower communication, portals, servicing—have to be secure, self-contained and purpose-built.
Some platforms are prioritizing privacy when it comes to AI models. For example, LenderAI, developed by iBusiness Funding, was built over four years ago with a very different approach: no plug-ins, no data-sharing partners and no shortcuts.
Every critical capability—from optical character recognition (OCR) and document generation to borrower chat and loan servicing—was natively built in-house. The result is a platform where no third-party ever touches borrower or bank data without explicit permission.
In an age where AI can absorb and retain anything, this privacy by design protects sensitive data, providing control over what is used and how.
That’s why LenderAI doesn’t embed AI by default, but rather offers a standalone module, Lendsey AI, which can be safely integrated and deployed only in its secured environment, not scraping client data or relying on external tools. Lendsey AI’s system is trained exclusively on iBusiness Funding’s own data and operations—which processes over $100 million in small business administration (SBA) and conventional loans per month—making it one of the few AI systems in lending not reliant on scraping or learning from inputs.
This “opt-in-intelligence” model should be the norm for institutions that want to explore AI without compromising control—especially within the highly regulated finance industry.
To support that shift, iBusiness Funding has also adopted the Banking AI Compliance Standard (BAICS). BAICS outlines 35 specific AI-related risks, each paired with recommended controls, giving banks and institutions a practical framework for safe AI adoption rooted in real operational experience. Based on extensive banking feedback and security expertise, BAICS is designed to evolve with the fast-changing world of generative AI for banks to understand responsible adoption.
The lesson behind both LenderAI and BAICS is simple: Responsible adoption starts far earlier than most expect, with the systems, agreements and architecture beneath it.
The next wave of AI in finance and lending won’t be defined by who has the most features. It will be defined by who builds the most trustworthy systems.
If you’re evaluating a platform, it’s imperative to ask:
Because once AI has your data, it may never let go—and no model is better than the platform it is built on. At a time when every startup is adding to the AI hype with magic promises, it’s important to choose a platform backed by trust.
Learn more about building trust with AI here.
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