Automated LTV, LTC, DSCR, and ARV analysis. AI calculates every metric your underwriters need and assigns risk scores based on your historical loan performance.
Every calculation private lenders need, automated
Calculates loan amount as percentage of property value. Flags deals above 75% LTV as high risk for fix & flip loans.
Measures loan amount against total project cost (purchase + rehab). Critical for fix & flip deals with renovation budgets.
Compares After Repair Value to purchase price. Identifies if flip has sufficient profit margin to justify the risk.
For rental loans, calculates if monthly rent covers mortgage payment (PITI). DSCR <1.0 = negative cash flow, auto-decline.
Weighs years in real estate investing, number of past flips completed, and credit score to assess borrower capability.
Reviews bank balances to verify borrower has reserves for unexpected costs. Flags insufficient liquidity as major risk.
Analyzes location factors: days on market in area, comparable sales velocity, neighborhood appreciation trends.
Combines all factors into single 0-100 risk score. Machine learning weighs each metric based on your historical loan outcomes.
How AI decides approve, conditional, or decline
Any ONE of these triggers immediate decline:
Two or more medium-risk flags:
Requires manual underwriter review with additional conditions (higher rate, more reserves, personal guarantee, etc.)
All of these must be true:
Straight to closing, no underwriter review needed
Better decisions, faster approvals, lower defaults
Every loan judged by same criteria. No more "John approves looser than Sarah." Same LTV gets same risk score every time, regardless of who underwrites.
AI highlights problems underwriters might miss—borrower has liquidity but DSCR is negative, ARV seems inflated compared to comps, LTC budget doesn't include permit costs.
ML models train on your past loans. If deals with >72% LTV historically defaulted more, AI weighs that factor heavier. Gets smarter with every funded loan.
Risk scores tie directly to pricing tiers. Show borrowers: "70% LTV = 9.5% rate. Your deal is 73% LTV = 10.5% rate." Data-driven, defensible pricing.
How credit teams use the risk engine day to day
It is an AI underwriting layer that calculates all key metrics your team cares about—LTV, LTC, DSCR, ARV ratios, borrower experience, liquidity, and market risk—and rolls them into a single composite risk score. The engine then proposes approve, conditional, or decline recommendations based on your credit policy.
Yes. All rules are configurable: you define your own LTV, LTC, DSCR, liquidity, and experience cutoffs. Underwriters can always override an AI recommendation with a reason code, and those overrides feed back into the model so it aligns more and more with how your team actually makes decisions.
It supports them. Mentyx Risk Scoring takes care of the calculations, flags, and consistency, while your underwriters focus on edge cases, structure, and borrower relationships. The result: fewer missed red flags, faster clear approvals, and a clear audit trail of how every decision was made.
Watch AI calculate LTV, LTC, DSCR, and assign risk scores in real-time