Underwriting’s Dirty Secret: Excel Is Costing You Millions (AI Fixes It)

Why 90% of Underwriters Are Ditching Excel For AI’s X-Ray Vision

Underwriting hinges on pinpointing critical questions that determine a deal’s viability, not just checklists. While tools like Excel have been staples for modeling deals, growing portfolio complexity and fast-changing markets expose the limitations of static spreadsheets.

From Static Tools to Dynamic Insights

AI doesn’t replace existing processes, it enhances them. Unlike rigid spreadsheets, AI integrates real-time data from financial records, market trends, borrower histories, and economic indicators, transforming underwriting into a dynamic, predictive system. Key capabilities include:

• Scalable data analysis across diverse sources.

• Instant detection of patterns and inconsistencies.

• Automated tagging of rent rolls, income statements, and sponsor details.

• Uncovering hidden trends in complex datasets.

This shift from “still photo to live video” provides a holistic view of deals in the context of broader market risks.

Key AI Underwriting Metrics

1. Time to Decision

Speed in CRE lending is a competitive edge, but haste must align with precision. AI tracks the time from deal intake to credit committee readiness, enabling:

• Optimized deal flow and resource allocation.

• Enhanced client responsiveness and loyalty.

• Proactive risk mitigation by reducing exposure to market shifts.
  By shortening underwriting cycles, teams prioritize complex tasks while maintaining rigorous oversight.

2. Confidence Scoring

AI bridges gaps in incomplete data (e.g., missing tenant info in development loans) by generating confidence scores. Platforms like Blooma AI:

• Aggregate data from financials, property metrics, and market comparables.

• Stress-test scenarios (e.g., interest rate spikes, tenant defaults).

• Use predictive analytics to refine risk likelihood.

• Validate scores with human expertise.
  This metric quantifies uncertainty, fostering transparency in risk assessment.

3. Portfolio Risk Matrix

AI maps risk exposure across an entire portfolio by visualizing asset-level data (property type, location, borrower profiles). Features include:

• Interactive heatmaps highlighting concentrations (e.g., bridge loans in one metro).

• Scenario testing (e.g., rent declines, economic downturns).

• Real-time updates for dynamic risk monitoring.
  Example: Identifying 40% bridge loan exposure in a high-interest environment prompts diversification strategies.

4. Exception Rate

AI quantifies operational friction by tracking exceptions (e.g., missing docs, inconsistent DSCR). Insights include:

• Risk patterns (e.g., recurring issues with specific brokers).

• Process gaps (e.g., frequent manual overrides).

• Model calibration (e.g., refining AI training data).
  A 20% exception rate might reveal broker training needs or model inaccuracies, turning outliers into actionable data.

5. Risk Velocity Analysis

This metric tracks how quickly risks escalate (e.g., occupancy drops, DSCR declines). AI:

• Monitors trends in tenant health, LTV ratios, and market shifts.

• Flags anomalies (e.g., rapid vacancy spikes in retail portfolios).

• Projects future impacts (e.g., cash flow disruptions).
  Example: A 15% occupancy decline over six months triggers preemptive loan restructuring.

Strategic Impact

AI-powered underwriting shifts CRE from reactive to proactive risk management. By layering real-time data with predictive analytics, lenders gain:

• Deeper visibility into portfolio vulnerabilities.

• Faster, data-driven decisions without sacrificing rigor.

• Scalability to handle growing deal volumes.

While traditional tools focus on individual deals, AI integrates macro and micro insights, enabling lenders to navigate volatility and optimize returns. The future of underwriting lies in balancing human expertise with AI’s analytical power—transforming uncertainty into opportunity.

Meet Underwrite.In, Revolutionizing Insurance Underwriting With AI

We’re Underwrite.In, and we’re here to fix what’s broken in insurance underwriting. Let’s cut to the chase: traditional underwriting is stuck in the past. Manual data entry, siloed information, and sluggish decision-making aren’t just headaches, they’re costing your business time, revenue, and the chance to deliver exceptional customer experiences.

At Underwrite.In, we asked: What if underwriting could be faster, smarter, and more transparent? That’s why we built an AI-powered platform designed to tackle the industry’s biggest pain points head-on.

The Challenges We Solve

• Inefficient Data Handling: Manual processes drown teams in paperwork and errors.

• Slow Decisions: Weeks spent on risk assessments delay opportunities.

• Lack of Transparency: Scattered documents and unclear metrics erode confidence.

• Outdated Risk Analysis: Static methods miss critical trends and patterns.

Our Mission

We exist to empower underwriters. By automating routine tasks and unlocking data-driven insights, we help you make faster, smarter decisions—without sacrificing accuracy or compliance.

How We Do It?

Why Partner with Us?

Underwrite.In isn’t just a tool, it’s your ally in innovation. We integrate seamlessly into your workflows, giving your team superhuman efficiency while keeping compliance and transparency front and center.

Join us in reshaping the future of underwriting. Let’s turn data into your greatest asset and leave inefficiency in the dust.

Here’s to smarter risk, smarter decisions, and a smarter bottom line.

Thank you for reading

Underwrite.In & team