A new loan application arrives. Within minutes — not days — this AI system pulls credit bureau data, runs a risk scoring model, makes an approve/review/decline decision, generates the decision letter, and logs everything to your CRM with a full audit trail.
70%
faster decisions
vs. manual review
<5 min
processing time
per application
Full
audit trail
every decision logged
CRM
auto-updated
zero manual entry
Typical build: 4–6 week sprint · Fixed price · Zero delivery risk
Trigger
API / form submit
Avg runtime
<5 minutes
Error handling
Human queue fallback
Manual underwriting queues mean borrowers wait 2–5 business days for a credit decision. At high volume, backlogs extend further — costing conversions and creating competitive disadvantage.
When underwriters make judgement calls manually, identical applications can receive different decisions depending on the reviewer. This inconsistency creates fair lending risk and compliance exposure.
Manual underwriting often leaves no structured audit record of what data was considered and why a decision was made. This is a significant liability during regulatory fair lending examinations.
This is the actual workflow Kovil AI builds and deploys — not a diagram. Here's what runs inside every node.
Applications arrive via a REST API endpoint (for lender integrations) or a branded web form. n8n validates required fields, assigns a unique application ID, and creates a record in HubSpot. The application payload is normalised into a standard schema before the pipeline continues.
Using the applicant's SSN and consent token, n8n calls the Experian or Equifax API to pull a full credit report: FICO score, payment history, debt-to-income components, open tradelines, derogatory marks, and public records. The call is logged with timestamp and bureau response code for the compliance audit trail.
A Python-based risk scoring model — trained on historical lending outcomes — combines the credit bureau data with the application fields (income, employment type, LTV, DTI) to produce a risk score from 0–100. GPT-4o then writes a plain-English underwriting rationale explaining the key factors driving the score, suitable for inclusion in the decision letter.
Business rules defined by your credit policy map risk scores to decision outcomes: scores below 30 auto-approve (with conditions if required), scores 30–65 route to manual underwriter review, scores above 65 auto-decline. Thresholds are configurable per loan product and are version-controlled for audit purposes.
GPT-4o produces a formatted decision letter using the AI rationale, decision outcome, and any required regulatory language (adverse action notices for declines under ECOA). The letter is generated as a PDF, personalised with the applicant's name and loan details, and stored in the loan management system.
The complete decision package is logged to the HubSpot deal record: AI risk score, decision outcome, GPT-4o rationale, bureau response, processing timestamp, model version, and a link to the decision letter PDF. This creates the regulatory-grade audit trail required for fair lending compliance reviews.
AI risk rationale + decision letter
Writes a plain-English underwriting rationale from the risk model output. Generates ECOA-compliant decision letters with adverse action notices for declines.
ML risk scoring model
Trained risk scoring model that combines credit bureau data with application fields — income, LTV, DTI, employment type — to produce a 0–100 risk score.
Workflow orchestration
Manages the full pipeline: API intake, credit bureau calls, model scoring, decision routing, letter generation, and CRM logging with error handling.
Credit data
Pulls full credit reports in real-time: FICO score, tradelines, derogatory marks, DTI components, and payment history. FCRA-compliant with consent token handling.
CRM + audit trail
Stores the complete decision package against every deal record. Provides the regulatory-grade audit trail for fair lending examinations.
API endpoints
Python FastAPI service that hosts the risk scoring model endpoint. Receives normalised application data and returns a risk score with feature attribution.
Kovil AI scopes, builds, tests and deploys this workflow end-to-end. You don't touch n8n or the risk model until it's live and processing real applications.
The system is built to support human underwriting decisions, not replace them. For auto-decline scenarios, the decision letter includes an adverse action notice as required by ECOA. All decisions include a written AI rationale and are logged with model version and inputs — creating the documentation trail needed for fair lending examinations.
Yes. During the scoping phase, we work with your credit team to define the risk score thresholds, decision rules per product, and any policy overlays (e.g. minimum FICO floors, maximum DTI caps). These are configured as versioned business rules — not hard-coded — so your credit team can adjust them without engineering involvement.
The standard build integrates with Experian or Equifax via their developer APIs. TransUnion integration is available as an add-on. Tri-merge bureau pulls can also be configured for mortgage products that require all three bureaus.
Any application where the AI confidence is below a configurable threshold, where bureau data is frozen or disputed, or where manual policy overlays apply is automatically routed to a human underwriter queue in HubSpot with all data pre-populated for review.
Book a 30-minute discovery call. We'll scope the risk model, credit bureau integrations, and compliance requirements for your lending products — fixed price, zero delivery risk.
Typical sprint: 4–6 weeks · Fixed-price · Fully managed delivery · Post-launch support included