Atlantis continuously scans and enriches your existing borrower data, filling in the gaps and running loan scenarios based on your product lineup. This isn’t a credit trigger or a cold list—it’s working off the borrowers you already have relationships with.

Example:
Milo Thatch, a borrower who closed a 30-year fixed mortgage with you 2 years ago, is identified as a candidate for a cash-out refinance based on current home equity trends.

Atlantis surfaces Milo as an opportunity, tagging it with:

• Readiness Score: 85% (based on LTV, rate environment, and payment history)

• Confidence Score: 65% (lower due to limited recent income data)

The system cross-references this scenario against your products and flags a Cash-Out Refi option that fits your pricing and guidelines. This isn't AI guesswork; it's compliance-grade scenario logic.

Milo's opportunity is delivered directly to his assigned loan officer with everything they need to take action—no digging, no guesswork. The alert includes:

A recommended loan scenario (e.g., a cash-out refi with estimated terms)

A breakdown of Readiness and Confidence Scores

Borrower insights based on current data

Suggested next steps for engagement

Instead of a vague notification, the LO gets a tailored opportunity backed by real numbers and context—ready to pick up the phone with purpose.

Because Atlantis flagged the Confidence Score as moderate, the LO knows to approach Milo with a consultative angle:

“Milo, we’ve been reviewing your account and there may be an opportunity for you to tap into your home’s equity—though I’d like to learn a bit more about your current income to see if it’s a strong fit.”

After a quick conversation, Milo provides updated income info. The LO enters this into their existing system, and Atlantis re-runs the scenario. The Confidence Score jumps to 90%.

With updated data and a scenario that aligns with both borrower need and lender product, the LO proceeds confidently, knowing this isn’t a cold lead, it’s a high-probability deal ready to move.