How Banks Can Leverage AI to Find and Vet Embedded Finance Partners Faster

With hundreds of fintechs in the market, banks have an abundance of options. But vetting best-fit partners can be a slow, resource-intensive process.

To move faster without sacrificing rigor, banks need a scalable approach to partner discovery that aligns with their strategic goals and risk tolerance. AI enables teams to replace manual deck review with structured summaries and criteria-aligned fit assessments, helping them focus their diligence on the fintechs with the greatest potential for long-term value.

The challenge of finding the right fintech partners

Finding the right fintech partners requires banks to evaluate a wide range of factors, from business models and funding stability to compliance posture, vendor management requirements, ongoing monitoring expectations, target customers and technical readiness.

Fintechs, in turn, are working to understand how banks assess these criteria and what signals matter most in partnership decisions, since clearer expectations reduce resubmissions and shorten time to decision.

Even with clear evaluation frameworks, misalignment often surfaces deeper into partnership discussions. For example, fintech passes the business pitch, then fails on reporting granularity or policy alignment. A fintech’s growth strategy may outpace a bank’s risk tolerance, or its reporting capabilities may fall short of long-term oversight needs, turning months of due diligence into a dead end. 

As the number of potential partners increases, the challenge becomes less about diligence and more about efficiency. Banks need to review more opportunities earlier in the funnel, without slowing teams down or diverting resources from the most promising prospects.

This is where AI can support early fintech triage and early-stage evaluation. By applying AI to how potential partners are surfaced, compared and prioritized, banks can create a more focused pipeline for deeper due diligence.

4 step framework for transparent, AI-enhanced fintech partner evaluation

To build trust, banks must address how they vet fintech partners. Applying AI across intake and activation introduces consistency and structure that expands the pool of qualified fintechs without replacing human judgment.

1. Determine your requirements‍

Before evaluating fintechs, clarify what matters most to your institution. That includes:

• Non-negotiables:
  
  • Regulatory and compliance requirements
  • Data access and reporting needs, such as transaction-level reporting cadence, exception reporting and the ability to support ongoing monitoring
  • Operational and risk controls
    

• ‍Preferences:
  
  • Industry or customer segment focus
  • Revenue model alignment
  • Long-term strategic fit

Banks are being asked to review more fintech opportunities than their partner and risk teams can realistically process.

The World Economic Forum estimates the global embedded finance market will reach $7.2 trillion by 2030, opening new growth and revenue opportunities for community banks beyond traditional lending. For banks, that growth is already translating into more fintech inquiries, faster decision cycles and greater scrutiny across partner, risk and compliance teams.

Some are looking to drive deposit growth and balance stability through new customer acquisition channels. Others want to launch new products, diversify revenue streams or support partners in emerging industries. Each goal brings a different set of requirements and a different definition of what makes a fintech partnership successful.

How AI helps:
‍By automating rubric creation, standardizing intake, and flagging gaps, AI ensures fintech partners are reviewed through a uniform, objective process. This improves consistency, speeds up comparisons and makes decisions more defensible across teams.

2. Gather information from fintech prospects 

Once requirements are set, banks collect information from fintech applicants. 

This includes details about:

• Business model and product scope
• Funding stage and indicators of financial stability, like runway estimate, customer concentration and unit economics signals where available
• Leadership team and organizational maturity
• Target customers and go-to-market approach

Manually reviewing this information across a large pool of applicants is time-consuming and prone to inconsistency. Different teams often ask overlapping or conflicting follow-up questions, creating delays and frustration on both sides of the process.

How AI helps: 
‍AI helps by extracting consistent data from decks and questionnaires, summarizing it in a standardized format and generating tailored follow-up questions to address gaps. Higher quality inputs reduce follow-up cycles and limit gaps that only surface later in the process. For banks, this creates a more complete picture early on. For fintechs, it sets clearer expectations about what matters.

3. Review potential partners against your requirements 

After the information is collected, banks must narrow the field and validate fit. This stage determines whether a potential partnership should move forward.

Banks typically: 

• Review matched submissions aligned to their intake checklist and preferences
• Conduct conversations with shortlisted fintechs to confirm product needs and timelines
• Request follow-up information to clarify open questions

Internal review follows, bringing together business, technical, risk and compliance stakeholders to assess readiness against the bank’s standards. When requirements are met, due diligence is completed and approvals move forward, creating a predictable progression from initial interest to activation.

How AI helps: 
AI streamlines the process by auto-scoring fit against a standard rubric, flagging conflicts with non-negotiables and generating stakeholder-ready summaries to ensure consistent, organized evaluations. This ensures each step builds logically on the last and decisions remain grounded in the bank’s original criteria.

4. Set up integrations and launch

Once a partnership is approved, technical integration begins. At this stage, transparency means continuous visibility into fintech activity — not just a one-time assessment — as it integrates with core systems.

Banks typically focus on:

• Using APIs to connect fintech activity directly to the bank’s core systems
• Enabling real-time visibility into transactions and account activity
• Supporting reconciliation and reporting from day one

A robust embedded finance platform also includes a bank operating system, compliance tooling and reporting dashboards that give teams shared visibility. With direct-to-core integration and real-time reconciliation, banks retain control over functions such as account creation and transaction monitoring.

How AI helps:
AI can enhance this visibility even after launch. Tools such as anomaly detection can surface irregular activity, while automated reporting supports continuous oversight without relying solely on manual reviews.

AI is redefining transparency in bank–fintech partnerships

AI’s most meaningful impact in embedded finance is the clarity it brings to complex bank–fintech relationships. Platforms purpose-built for visibility enable banks and fintechs to move forward with confidence.

Treasury Prime’s AI Marketplace is built with this goal in mind. By applying AI to partner discovery and evaluation in a structured, transparent way, it sets a new standard for how embedded finance partnerships are built.

Ready to build more transparent, high-performing partnerships? Connect with our team to see how Treasury Prime’s AI Marketplace can help your team evaluate partners faster, reduce friction and move forward with confidence.