Underwrite faster. Explain everything.
AI-powered risk intelligence infrastructure that runs inside your perimeter. Analysts get automated preliminary analysis in minutes. Regulators get full decision attribution.
The Problem
Your best analysts spend most of their time not analyzing.
Senior underwriters spend 70% of their time on data collection and document reconciliation. The expensive judgment sits idle while logistics eat the clock.
The Aggregation Tax
Senior underwriters spend 60–70% of their time on data collection, document reconciliation, and preliminary screening. The value pyramid is inverted: expensive judgment consumed by low-leverage logistics.
Underwriting Variance
Two analysts reviewing the same deal reach different conclusions based on workload, experience, and implicit assumptions. This variance creates portfolio risk and audit exposure that compounds across the book.
The Privacy Constraint
Sensitive borrower data can't be sent to third-party AI services without regulatory risk. Building proprietary ML infrastructure requires data science talent most lending institutions don't have and shouldn't need.
How It Works
Ingest. Score. Decide.
Upload the data room. Rekord AI parses, scores, and produces a decision-ready case file with full factor attribution. Everything runs inside your infrastructure.
Ingest.
Drop the data room. Get a briefing.
Upload financial statements, contracts, credit files, and operational reports — hundreds of documents at once. Automated parsing extracts entities, normalizes schemas, and builds a queryable knowledge base. No manual tagging.
Score.
Every score explains itself.
Customizable risk scorecards generate factor-level attribution for every assessment. You define the weights, thresholds, and risk appetite. Cash flow coverage contributes −15 points; industry growth trend adds +8. Analysts and regulators see the same math.
Decide.
Analyst decides. AI prepared the case.
Recommendations appear alongside the analyst's existing workflow as decision support. The analyst reviews, adjusts, and approves. Every override feeds the learning loop; the system adapts to your institution's risk philosophy over time.
Architecture
Your infrastructure. Your models. Your data.
Self-hosted, on-premise or private cloud. Connects to your data sources and APIs, but all processing stays inside your perimeter. Every scoring decision is anchored to Rekord Kloud for an independently verifiable audit trail.
Self Hosted Deployment
| Requirement | How Rekord AI delivers |
|---|---|
| Data sovereignty | Runs on-premise or in your private cloud VPC. Connects to external data sources, services, and APIs as needed, but all processing stays inside your perimeter. |
| Regulatory compliance | Supports SOX, GDPR, and Basel documentation requirements. Factor-level attribution gives lenders the explainability evidence they need for model governance and regulatory obligations. |
| Audit trail | All scoring decisions anchored via Rekord Kloud — timestamped, independently verifiable, tamper-detectable. |
Learning Architecture
| Capability | Mechanism |
|---|---|
| Semi-supervised learning | Models observe analyst decisions (approvals, declines, overrides) and update risk profiles to match institutional philosophy. |
| Bias detection | Automated audits flag potential disparate impact across protected classes before models reach production. |
| Human-in-the-loop | Configurable thresholds route high-risk decisions to senior underwriters. AI never makes final credit judgments. |
Use Cases
In Production.
Three underwriting verticals, live. Each one measured against analyst workflow before and after deployment.
M&A Due Diligence & Bankability Scoring
Challenge
Data rooms contain hundreds of documents. Manual review takes 2–3 weeks per deal and creates timing risk in competitive processes.
What Rekord AI does
Ingests the full data room, generates an executive summary, identifies red flags, and produces a 0–100 bankability score with factor-level attribution.
Commercial Underwriting
Challenge
Multi-dimensional borrower profiles (financials, industry dynamics, collateral, covenants) create analysis bottlenecks that slow origination.
What Rekord AI does
Generates preliminary credit memo frameworks with scorecard assessments across financial health, collateral coverage, and covenant breach probability. Analysts receive a pre-built case file.
Consumer Lending
Challenge
Thousands of monthly applications, each requiring credit analysis, income verification, and fraud screening. Manual review creates bottlenecks and inconsistent decisioning.
What Rekord AI does
Query-driven risk assessment by customer identifier. Instant creditworthiness scoring, fraud indicators, and recommended decisions with supporting rationale. Feedback loops improve accuracy continuously.
One API. Any underwriting vertical.
Documents in, scored intelligence out.
from rekord import RekordAI
client = RekordAI(api_key="rk_live_...")
# Ingest a data room
room = client.rooms.create(
name= "acquisition_target_q1",
documents= ["./financials/", "./contracts/"]
)
# Score the deal
score = client.score(
room_id=room.id,
model= "bankability_v2"
)Comparison
Why Rekord AI.
Generic AI tools can't touch sensitive borrower data. In-house builds take 12–18 months and require specialized hires. Rekord AI deploys inside your perimeter in weeks with explainability built in.
| Capability | Generic AI Tools | In-House Build | Rekord AI |
|---|---|---|---|
| Data privacy | Data sent to third-party servers. | Full control, but you build everything. | Self-hosted. Connects to external sources; processing stays inside your perimeter. |
| Explainability | Black box or limited attribution. | Depends on team capability. | Factor-level attribution on every score. |
| Time to value | Days, but compliance blocks deployment. | 12–18 months, specialized hires. | Pilot in 30–60 days. Production in 90. |
| Learning | Static models. Manual retraining. | Continuous, if you staff it. | Self-reinforcing. Learns from analyst decisions. |
| Audit trail | Logs, not proofs. | Custom implementation required. | Cryptographic proof anchored via Rekord Kloud. |
Deployment
Pilot to production in 90 days.
Start with one underwriting vertical. Measure AI accuracy against analyst decisions. Scale when the numbers prove out.
Weeks 1–4
Shadow mode
Deploy AI scoring alongside existing underwriters on one vertical. Measure accuracy against human decisions. No workflow changes.
Integrate AI recommendations into analyst workflows. Train teams. Activate feedback loops.
Weeks 5–8
Dashboard rollout
Weeks 9–12
Production
Full deployment. Continuous learning active. Expand to additional verticals and business units.