Turn a risk estimate into the offer that creates the most value.
Evaluate candidate amounts, terms and prices against your policy limits, and recommend the offer with the highest expected contribution that a borrower is likely to accept.
Request: $12,000 over 36 months
The Credit Offer Engine prices and structures each loan offer for the most expected value your policy allows, not just approves or declines it. It searches every amount, term and price you permit, estimates acceptance probability and expected loss for each candidate, and recommends the one offer that maximizes expected contribution: the value you would expect after accounting for both acceptance and default risk.
The Credit Risk Model estimates risk. The Credit Offer Engine converts that risk into the amount, term and price expected to create the most value within your constraints.
The risk estimate can come from a Carrington Labs model or from a validated model you supply.
Your constraints define the search space: the engine only recommends inside them
Cost of funds
Loss assumptions (LGD and EAD)
Price and term ranges you allow
Acceptance calibration data
Policy guardrails
Borrower risk estimate
Requested amount and term
Credit Offer Engine
Offer-level optimization within policy
Recommended offer
Expected loss
Expected contribution
Calibration is per lending product, because exposure profiles differ, and so do the term and price ranges that are legal and sensible to offer.
For consumer lending, offer optimization runs across cash advance, personal loans, BNPL, credit cards and lines of credit, auto, overdrafts and debt consolidation, with amount, term and price ranges calibrated to each product's own exposure and repayment pattern.
For business lending, the engine applies the same optimization across invoice financing, term loans, lines of credit, business credit cards, equipment finance, merchant cash advance and working capital, with ranges and limits calibrated separately, since exposure and repayment patterns differ from consumer lending.
Adjust the ranges and policy limits. The engine re-evaluates candidate offers and returns the one with the highest expected contribution that still satisfies every constraint.
For illustrative purposes only: values are produced by a simplified demonstration model, not a live pricing engine.
Want to see the price and term trade-off play out on your own portfolio's numbers?
Recommended offers are returned by API or batch into your origination workflow. Your policy limits define the search space, and final approval remains yours.
Decision engine
Scorecard
Origination platform
LMS
CRM
Portfolio analytics
You set the constraints (requested amount, term, price range and risk limits) and can override or reject any recommendation before it reaches the borrower.
Amount, term and price are chosen together against expected contribution, rather than approving a requested offer as submitted.
Maximum default probability, minimum margin, minimum acceptance probability and regulatory rate and term bounds are inputs, not suggestions.
The default probability can come from a Carrington Labs model or a validated model you already use.
The recommended offer, the borrower's preferred offer and alternatives, each with the economics attached.
The Credit Risk Model is trained on your data. The Offer Engine is configured to your ranges, limits and constraints instead, then recalibrated as acceptance and margin outcomes shift.
01
We encode your amount, term, rate and fee ranges alongside your policy limits.
02
Either a Carrington Labs model or a validated model you supply.
03
Acceptance behavior and contribution assumptions are fitted to your portfolio.
04
Offer recommendations by API at decision time or by batch for pre-approved campaigns.
05
Acceptance and margin outcomes are monitored, and assumptions recalibrated as behavior shifts.
Explainability, lender control and data handling built into every deployment.
The Credit Offer Engine returns no score and makes no decision on the applicant. It selects a price point from the amount, term and price ranges you set, and when a candidate offer is ruled out, it names the constraint responsible.
You define the ranges and limits the engine may search. It cannot recommend an offer outside them, and final approval remains yours.
The engine runs on the constraint set you configure (amount, term, price ranges, cost of funds) plus a PD estimate. No applicant transaction data or PII passes through it.
When limits conflict, it reports the closest feasible offer and names the conflicting pair, giving your team a clear audit trail. Final approval decisions remain yours throughout.
API at decision time or batch for campaigns, into your origination platform and CRM.
Re-run historical applications under your current policy and compare expected contribution.
Leave your details below and our team will schedule a call to discuss your business and how Carrington Labs can help.
