Banks & lenders
Automated, explainable underwriting decisions.
Credit Risk Scoring Software
Credit Risk Scoring Software for Smarter Lending
Approve good borrowers faster and price risk accurately: AI credit models, automated borrower risk assessment, loan-approval decision engines, and alternative data, with explainable decisions and LOS integration.
AI ScoringExplainable AIAlternative DataLOS Integration
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What credit risk scoring software does
Traditional scorecards miss good borrowers and misprice risk. Credit risk scoring software uses machine-learning models and alternative data to assess borrower risk automatically, predict default, and drive loan-approval decisions in real time, so lending is faster, fairer, and better-priced.
With explainable AI, real-time scoring, and integration to loan origination systems, it gives lenders defensible decisions that satisfy regulators while risk dashboards show portfolio exposure, turning underwriting from a bottleneck into a decision engine.
What it is
Credit decisions, automated and explainable
A credit risk platform scores borrower risk with machine-learning models, evaluates creditworthiness from traditional and alternative data, predicts default, and drives automated loan-approval decisions.
Connected to loan origination systems, it gives credit and risk teams real-time, explainable decisions with the audit trail regulators expect.
What we build into your credit scoring platform
Each capability turns borrower data into a fast, defensible lending decision.
AI-Powered Credit Scoring
Machine-learning models that score credit risk beyond scorecards.
Automated Borrower Risk Assessment
Assess borrower risk automatically from all available data.
Loan-Approval Decision Engines
Real-time decision engines that approve, decline, or refer.
Machine-Learning Predictions
Predictive models for default and repayment behavior.
Alternative Data Analysis
Use alternative data to score thin-file and new borrowers.
Creditworthiness Evaluation
Evaluate creditworthiness consistently across applicants.
Default Risk Prediction
Predict default probability to price and provision risk.
Loan Origination Integration
Integrate with LOS so scores drive live origination.
Explainable AI & Risk Dashboards
Explainable (XAI) decisions plus portfolio risk dashboards.
Where it runs
Who uses credit risk scoring software
Lenders and credit teams making faster, fairer, better-priced decisions.
Fintech lenders
Alternative-data scoring for thin-file borrowers.
Credit risk teams
Default prediction and portfolio risk views.
Underwriting
Real-time decisions instead of manual review.
Compliance
Explainable, auditable credit decisions.
Loan origination
Scores wired directly into the LOS.
Why it matters
Lend faster, price risk right
- Approve good borrowers faster with automated scoring
- Reach thin-file borrowers with alternative data
- Price and provision risk with default prediction
- Keep decisions explainable and audit-ready
- See portfolio risk on live dashboards
- Wire scores straight into loan origination
Every decision is risk and revenue
A declined good borrower is lost revenue; an approved bad one is a loss. Better scoring improves both sides of the decision.
Real-time
Automated approval decisions
Explainable
Every score with clear reasons
Predictive
Default risk before you lend
Compliance & connectivity
Built for defensible lending
ExplainabilityFair LendingModel RiskAlternative DataLOSAudit Trails
Lending pricing
How does a risk-based pricing engine set the loan rate?
A risk-based pricing engine sets the loan rate by placing each approved applicant in a risk band, applying your rate card and adjusting for term, amount and collateral. We build it as versioned rules beside the scoring model, so every quoted rate is traceable.
Score and band
The model's score places the applicant in a risk band. Band edges come from your credit policy, held as configuration, so risk teams can change them without a code release.
Base rate from the rate card
Each band maps to a base rate, or a margin over your funding index, for that product. Rate cards are versioned and dated, so every quote shows which version priced it.
Adjustments
Term, loan amount, collateral and loan-to-value, relationship pricing and promotions adjust the base rate, each as a named rule with its own limits.
Floors, caps and exceptions
The engine enforces minimum and maximum rates. A loan officer can request a pricing exception, which routes for approval, and the reason is stored with the decision.
Risk-based pricing notices
For consumer credit, the engine flags borrowers priced on less favorable terms because of their credit report, by direct comparison or the credit score proxy method, so the right notice or credit score disclosure goes out.
Pricing outcome monitoring
Dashboards compare pricing by band, channel and product and show exception rates, so risk and compliance teams review margin and fair lending together.
Facts and sources
Which rules shape risk-based loan pricing?
Short facts we design the pricing engine around, each linked to its primary source.
A lender that uses a consumer report to grant consumer credit on material terms materially less favorable than the most favorable terms available to a substantial proportion of its consumers must give that consumer a risk-based pricing notice.
Source: 12 CFR 1022.72(a) (eCFR)Under the credit score proxy method, a lender that prices on credit scores may set a cutoff score where about 40 percent of its borrowers score higher and 60 percent lower, and send the risk-based pricing notice to each borrower below the cutoff.
Source: 12 CFR 1022.72(b)(1) (eCFR)A risk-based pricing notice is not required in listed exceptions, including when the consumer receives an adverse action notice or a credit score disclosure that meets the rule.
Source: 12 CFR 1022.74 (eCFR)
Frequently Asked Questions
Yes. Alongside bureau and application data, it can incorporate alternative data to score thin-file and new-to-credit borrowers who traditional scorecards reject, expanding approvals without adding risk.
Yes. We build in explainable AI so every score comes with the factors behind it: essential for adverse-action notices, fair-lending review, and model governance.
Yes. Scores and decisions wire directly into your LOS so underwriting happens in the origination flow. We scope the specific LOS and data sources during discovery.
We support bias monitoring, model validation, and governance so decisions are defensible. The goal is faster lending that still stands up to fair-lending and model-risk scrutiny.
It draws on bureau data from Experian, Equifax, or TransUnion, application data, and alternative sources such as open-banking transaction feeds, cash-flow, and rental or telco history. It is provider-agnostic, so we wire in the bureaus and data vendors you already use and can add alternative data to widen approvals for thin-file borrowers.
Yes. We can deploy models we build with you or host models your risk team already owns, and produce the reason codes needed for adverse-action notices under FCRA and ECOA. Every model runs under governance aligned with SR 11-7 - backtesting, population-stability monitoring, and drift alerts - so a scorecard that decays in production is flagged for revalidation.
Yes. We load your current rate sheets as the first rate card version, replay loans you have already priced through the engine and switch over only when both give the same rate and the same notice decision.
Ready to modernize credit decisions?
Tell us about your lending products, data, and risk appetite. We'll return a plan for a credit risk scoring platform with explainable AI and LOS integration.
contact@agnotic.com
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