Yaacov Martin is the CEO of Jifiti.
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AI is transforming every corner of finance, and the financial services sector is estimated to spend an impressive $97 billion on AI by 2027. As technologies such as agentic AI agents reshape banking and the customer experience, one factor is emerging as the new competitive edge: discoverability. Already, 44% of consumers trust AI agents in financial services, signaling a shift in consumer behavior.
AI agents are moving beyond personalized financial advice and fraud detection. Not only are use cases arising where they surface loan options for consumers, but they’ll ultimately be completing applications for them and automating fund disbursement. In the very near future, AI agents will likely handle everything from filling out forms to verifying identities and initiating automated underwriting.
For banks, the question is no longer whether to become AI-driven, but how quickly. As AI-optimized underwriting and digital-first lenders reshape the market, financial institutions that invest now will keep their place at the center of the credit ecosystem. Those that delay AI adoption risk losing visibility altogether, as younger, tech-native borrowers bypass traditional channels in favor of smarter, automated alternatives.
Discoverability Is the New Front Door
Using an AI engine to both search and apply for a loan is the next major leap in customer experience, with the global AI agents in the financial services market projected to be worth $4.28 billion by 2032. And while the opportunity is colossal for banks and FIs, this brings a new issue to the forefront: invisibility.
AI engines don’t discover and rank loans by quality; they’re ranked by readability. This is known as answer engine optimization (AEO). If a loan product isn’t structured for easy ingestion, it doesn’t get considered.
For instance, if a lender’s APR and eligibility criteria are buried in a PDF, an AI engine won’t surface the loan, regardless of its competitiveness. Banks must ensure exposed offer metadata: loan products need to be clearly described in structured formats—product type, APR, terms, and eligibility criteria. Structured metadata ensures AI agents can accurately index, compare, and act on loan products. Without it, even excellent loan offers may remain invisible.
But the issue of discoverability goes even deeper. AEO helps AI agents surface loans, but besides putting the data in the right format, banks also need the right infrastructure to allow AI agents to provide the customer with an AI-sourced loan offer.
For example, a customer could input their loan criteria into an AI agent search engine, which instantly displays all the relevant loan offers and the option to auto-apply. With one click, the customer receives a conditional loan approval, powered entirely by machine-readable data and API-driven workflows.
Banks without API-driven lending tech, digitized user journeys, non-siloed data, and automated onboarding and decisioning won’t even be in the running. In this environment, being the better lender is irrelevant if you're not discoverable.
But this is easier said than done. A PYMNTS report found that 75% of banks struggle with implementing new digital solutions due to their legacy infrastructure. And “59% of bankers see their legacy systems as a major business challenge, describing them as a “spaghetti” of interconnected but antiquated technologies.”
Fairness, and the New Compliance Frontier
If discoverability is the front door to agentic lending, fairness is the new compliance frontier. AI engines don’t just risk excluding products not optimized for AI discoverability; they threaten to exclude entire categories of lenders who don't meet their technical standards. But here the issue isn’t visibility; it’s equity.
Today’s agentic lending introduces a modern variation on biased lending: consumers may be steered toward lenders with the right infrastructure—APIs, clean data, automated workflows—rather than the best financial product.
Without transparency into how AI-powered platforms rank or surface loan offers, consumers risk being steered toward higher-cost or less suitable loans simply because those lenders had the right infrastructure, not the right product. This creates a new compliance blind spot for regulators. Regulators may soon ask, “Is your bank’s outdated infrastructure effectively blocking access to your best products?”
For decades, regulatory scrutiny has focused on discriminatory practices in lending decisions. But as agentic lending takes hold, the regulatory lens will widen. Banks that fail to modernize may not just lose market share; they may be seen as contributing to systemic bias.
Banks Can Still Compete—If They Modernize
On the surface, agentic lending seems tailor-made for fintechs, whose tech stacks are built for speed and flexibility. But the advantage isn’t exclusive. Banks just need to update their operating models.
Emerging AI agents are being designed to locate suitable products, complete applications, submit KYC documents, and trigger automated underwriting. Banks that haven’t digitized their end-to-end workflows risk being bypassed, even if they offer competitive rates. They need a coordinated system, or orchestration platform, that connects all the critical pieces of the lending process, automates workflows, and ensures each step is machine-readable and API-accessible.
An orchestration layer that offers this infrastructure typically integrates all critical as well as third-party functionality, including ID verification, KYC/KYB, anti-fraud, open banking, credit risk checks and automated decisioning.
Fintechs are already API-native, but many banks have some catching up to do with their fragmented tech stacks. Without orchestration, all these essential integrations remain siloed, and AI agents will need end-to-end continuity to ultimately provide an end-to-end loan application experience. The orchestration layer isn’t just helpful—it’s the bridge that lets legacy banks compete in the agentic lending ecosystem without tearing down their entire infrastructure.
Banks that modernize their infrastructure and automate their workflows can reclaim control of the lending funnel, ensuring AI platforms surface their products and that customers gain AI-driven access to the best and most suitable options available, not only the ones easiest to surface.
