Know when to offer each borrower their next product.
Upload a spreadsheet of your existing borrowers from your CRM. AI reviews each relationship and recommends the next product to offer, when to reach out, and through which touchpoint, so your lenders call when a borrower is ready and drive top-line growth through cross-sell, upsell, and expansion revenue from the customers you already have.
- Loan officers and relationship managers
- Branch managers
- Consumer and commercial lending leaders
- Marketing and product teams
Reach out when the borrower is ready, not when the campaign runs.
Your existing borrowers are your best prospects for the next product, but the moment to reach out is buried in the CRM: a car loan with six payments left, a mortgage that has built up equity, a business line that is nearly maxed out. Lenders rarely have time to go through their book account by account, so the moment passes and the customer goes elsewhere.
With MultiModel, your team uploads a CRM export and lists the products they want to promote. The assistant reviews every borrower and returns a prioritized list: the next product to offer, why now, the best touchpoint, and talking points for the conversation.
- Find the borrowers who are ready for a new product or service
- Time outreach to the moments that matter, like payoff, maturity, or a rate reset
- Give every lender a prioritized list with talking points for each call
Copilot Chat
Internal
Upload a spreadsheet of borrowers from your CRM, choose the products to promote, and add notes on the offers. The assistant reviews each borrower and recommends the next product, when to reach out, and through which touchpoint, with the account signals behind each recommendation.
Recommend products for existing borrowersInternal
Q3 borrower recommendations
All Products
HELOC promotion through Dec 31, 12-month CD special, premium checking, treasury management for business borrowers.
I reviewed 1,248 borrowers and found 214 with a strong next-product fit. Here are the top five, ranked by fit and timing.
| Borrower | Recommend | Reach out |
|---|---|---|
| R. Alvarez | HELOC | This week, by phone |
| Pinecrest Dental LLC | Treasury management | Next review, in person |
| J. and M. Osei | 12-month CD | Now, by email |
| T. Brennan | Premium checking | At next deposit, in app |
| Harbor Freight Movers Inc. | Treasury management | This month, by phone |
Signals behind the top picks
- R. Alvarez: mortgage balance down to 52% of the home’s value, two credit card balances over $9,000, and a HELOC promotion that ends Dec 31
- Pinecrest Dental LLC: 40+ outgoing ACH payments a month made one at a time, and average balances up 35% this year
- J. and M. Osei: $61,000 sitting in savings at 0.40% and a maturing auto loan freeing up cash
Next step
The full list of 214 is ready to download as a spreadsheet, sorted by branch and loan officer.
From your documents to a sourced draft.
The assistant uses retrieval-augmented generation (RAG): it finds the relevant passages in the content you approve, then answers from them with citations your team can check.
Upload your CRM export
Choose the portfolio, upload a spreadsheet of borrowers, and list the products and offers you want to promote.
Get a recommendation for each borrower
In minutes, the assistant recommends the next product, when to reach out, and through which touchpoint, with the account signals behind each recommendation.
Review and reach out
Your lenders review the prioritized list, start with the strongest opportunities, and log the outreach in your CRM.
What your team asks, and what comes back.
Auto loan nearing payoff
The request. A borrower has six payments left on an auto loan, has never missed a payment, and holds only a basic checking account.
What the assistant returns. A recommendation to offer a high-yield savings account to keep the monthly payment working for them, and a pre-approval for their next vehicle, with a branch call 60 days before payoff.
Why it matters. The relationship grows at the point it would otherwise end, instead of the customer drifting away once the loan is paid off.
Mortgage with built-up equity
The request. A homeowner is eight years into a 30-year mortgage, the estimated loan-to-value has fallen below 60%, and they have no home equity product.
What the assistant returns. A recommendation to offer the current HELOC promotion at their annual escrow review, with talking points on renovation and debt consolidation uses.
Why it matters. The lender raises the HELOC at a natural touchpoint, before the customer shops the offer at another lender.
Small business line near its limit
The request. A business borrower's line of credit utilization has risen from 40% to 85% over three quarters, while deposits have grown steadily.
What the assistant returns. A recommendation for a line increase review ahead of renewal, plus treasury management and merchant services, with a relationship manager meeting as the touchpoint.
Why it matters. Expansion revenue comes from a growing customer, and the relationship manager arrives with a reason to meet and the numbers to back it.
Measurable time back, with the controls already in place.
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Talk to us about CRM Upsell Analysis
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AI for CRM upsell analysis: FAQ
Straight answers for CIOs, CISOs, and compliance officers evaluating AI.
Talk to usWhat does the spreadsheet need to include?
Any CRM export in CSV or Excel works. The most useful columns are the products each customer holds, balances, rates, origination and maturity dates, payment history, and contact preferences. The assistant works with the columns you have, and notes where a recommendation would be stronger with more data.
Does the AI contact customers?
No. For each borrower, the assistant recommends a product, a time to reach out, and a touchpoint, with talking points. Your lenders review each recommendation and decide whether and how to reach out.
How does it decide when to reach out?
It looks for signals in the account data, such as a loan nearing payoff, rising home equity, an upcoming rate reset or maturity, high line utilization, or growing deposits. Each recommendation lists the signals it relied on, so your lenders can check them before they call.
Which products can it recommend?
Only the products and offers you list, such as a HELOC promotion, a CD special, or treasury management services. Your team can edit the structured AI assistant to add products, change the portfolio options, or match your sales process. See AI assistants for how assistants are built and shared.
How do opt-outs and compliance review work?
Customers marked do-not-contact or opted out in your spreadsheet are left off the outreach list. Your compliance team can review the assistant's instructions and the products it is allowed to recommend, and every prompt and answer is kept in the audit log.
Is customer data sent to OpenAI or other model providers?
Your spreadsheet is processed inside your own Azure or AWS environment and is never copied to a third-party service. Account numbers, SSNs, and other PII are redacted on the employee's device before a prompt is sent, and prompts are excluded from model training across all AI providers. See data loss prevention.
See next-product recommendations for your own borrowers.
Bring a sample CRM export with names removed. We'll show you a recommended product, timing, and touchpoint for each borrower, generated inside your own environment.