Scoring Business Lending Leads with AI - RuleSixPack #4

Prioritization focuses resources on the right things at the right time.

Instead of first-in, first-out, this CU now prioritizes based on who’s most likely to close — thanks to AI-powered lead scoring.

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The Challenge

Regional Credit Union (approx. 180 employees) was facing a critical issue: No simple, structured way to prioritize loan leads. Employees might spend valuable time processing and evaluating loan leads that weren't at the top of the pile, but they wouldn't know until a significant evaluation was invested.

How We Solved It

Using Salesforce’s Financial Services Cloud, Einstein Lead Scoring, and Sales Cloud, we designed a custom solution using Einstein Lead Scoring on historical close data as a predictor of application relevance. In practice, the CU saw a higher conversion rate and better lender productivity, significantly improving our client’s outcomes and efficiency.

What This Replaced

Previously, this process was entirely human-powered and fraught with randomness. It relied on FIFO queues and gut-feel sorting, often leading to delays, errors, or inconsistent results. By automating and enhancing this workflow, the team unlocked more conversion, more capacity and better accuracy.

The Result

The estimated value created from this change: $5M in additional loans funded.

• 500 qualified leads/year

• 5% higher conversion = 25 new loans

• Avg. loan: $200K

≈ $5M in added lending, ~$250K/year revenue impact

Could This Be You?

If you're struggling with no structured way to prioritize loan leads, it's likely that similar tools and design patterns could dramatically simplify your workflows too. Let’s talk about how to bring that efficiency to your business.

Be sure to review the rest of the RuleSixPack of Use Cases!

Contact Rule Six Consulting Services