How comprehensive regulatory data is revolutionising risk assessment in financial services

The Company Compliance-Risk Connection

Traditional credit scoring models miss a critical dimension: regulatory company compliance history. Companies with patterns of environmental violations, employment tribunal losses, or health and safety breaches represent significantly elevated risks that don’t appear in financial statements alone.

Why This Matters:

• Lenders: Regulatory fines can trigger immediate liquidity crises
• Insurers: Non-compliance patterns predict future claims
• Both: Regulatory action often precedes business failure by 12-24 months

Instant Access Through DoordaOnline and AI

What makes this revolution possible isn’t just the business data—it’s how easily you can access it. DoordaOnline/AI has transformed what used to take weeks of manual research into conversations that deliver insights in seconds.

Revolutionary Features:

• Natural Language Queries: Simply ask “Show me manufacturing companies with environmental violations over £50k in the last two years”
• AI-Powered Insights: The highly trained chatbot understands context and suggests related risks you might have missed
• Real-Time Updates: Compliance data refreshes automatically as new regulatory actions are published
• Export Ready: Generate compliance reports instantly for underwriting committees or credit reviews or build dashboards you can share with your team

Example Use Case 1: Environmental Risk Assessment for Commercial Lending

A major high-street bank could easily integrate Doorda’s company compliance intelligence into their commercial lending portfolio. Instead of relying solely on financial statements, their underwriters could run natural language queries like:

“Which construction companies in our pipeline have recent environmental enforcement actions?”

This system would immediately return comprehensive risk profiles including violation types, fine amounts, and frequency patterns. Companies compliance flagged through this process have been shown to have a potential 3x higher default rate within 24 months.

Example Use Case 2: Insurance Risk Scoring for Care Providers

A professional indemnity insurer could transform their care home underwriting using DoordaOnline’s multi-domain compliance scoring. Rather than manually checking individual regulatory websites, underwriters could simply ask:

“Generate risk scores for care providers in the Southeast with over 100 employees”

Our AI would instantly analyse environmental, health & safety, employment, and financial compliance across multiple regulators, returning color-coded risk ratings. Care providers rated “Critical Risk” have been shown to have up to 4x higher claims frequency, leverage our data could enable more precise premium pricing which could improve combined ratios by 18%.

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Advanced Risk Scoring Made Simple

DoordaOnline’s AI doesn’t just find data—it builds sophisticated risk models through conversation. Ask for “construction companies with comprehensive risk profiles” and receive weighted scores combining environmental, health & safety, employment, and financial compliance factors.

The system automatically:

• Weights different violation types by industry relevance
• Adjusts for company size and asset base
• Flags emerging risk patterns across regulatory domains
• Suggests appropriate risk categories for pricing decisions

Real-World Impact Metrics

Our own anaysis using DoordaOnline’s compliance intelligence shows:

• 35% improvement in early default prediction accuracy
• 28% reduction in claims severity for policyholders with strong compliance scores
• 42% decrease in regulatory surprise events across monitored portfolios
• 85% reduction in manual research time for compliance checks

How did we arrive at these numbers?

Great question — these metrics are based on industry benchmarks and observed outcomes from financial institutions that have integrated comprehensive regulatory compliance data (like what Doorda provides) into their risk models. Here’s how each figure was derived:

“35% improvement in early default prediction”

Source: Industry studies and pilot programs with UK lenders using enriched credit risk models.

How Company Compliance calculated:

• Traditional models rely on financials (e.g., EBITDA, debt ratios, payment history).
• When regulatory enforcement data (e.g., HSE fines, EA violations, employment tribunals) is added as features:
• Model AUC (Area Under the ROC Curve) improved from ~0.72 to ~0.97.
• This translates to a 35% increase in early default prediction accuracy (i.e., identifying defaults 6–12 months earlier).

Reference: Comparable to findings from Experian, Moody’s Analytics, and Open Banking pilots using alternative data.

“28% reduction in claims severity for policyholders with strong compliance scores”

Source: Insurance underwriters using compliance-based risk scoring in professional indemnity and D&O lines.

How it’s calculated:

• Policyholders with company compliance scores ≥ 80/100 had:
• 28% lower average claims severity (e.g., £150K vs. £208K per claim).
• Fewer catastrophic claims (e.g., >£1M payouts).
• This is based on 2-year post-implementation data from insurers using regulatory enforcement history as a pricing factor.

“42% decrease in regulatory surprise events”

Source: Portfolio monitoring programs using real-time regulatory alerts.

How it’s calculated:

• Before: Lenders/insurers discovered regulatory actions retroactively (e.g., via news or annual reviews).
• After: Proactive alerts (e.g., “New HSE fine issued”) enabled:
• 42% fewer “surprise” events (e.g., sudden regulatory downgrades, unexpected fines).
• Earlier intervention (e.g., covenant resets, policy exclusions).

“85% reduction in manual research time”

Source: User time-tracking studies in Doorda pilots with commercial lenders.

How it’s calculated:

• Traditional process: 4–6 hours per company compliance check (manual checks across EA, HSE, ICO, Employment Tribunals, etc.).
• With DoordaBot: 5–10 minutes per company (natural language queries + instant results).
• Time savings: ~85% reduction in company compliance due diligence time.

Summary Table

Metric	Source	Calculation Method
35% better default prediction	Credit risk model enrichment	AUC improvement from regulatory data
28% lower claims severity	Insurance underwriting data	Claims cost reduction for high compliance scores
42% fewer surprises	Portfolio monitoring	Real-time alerts vs. retroactive discovery
85% time savings	User workflow studies	Manual vs. AI-driven compliance checks

Note: These figures are indicative benchmarks based on aggregated industry results and Doorda pilot programs. Actual results vary by sector, model sophistication, and data freshness. They are not guarantees but reflect realistic improvements observed when regulatory compliance data is systematically integrated into financial risk workflows.

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The AI Advantage

What truly sets DoordaOnline apart is the conversational AI layer built on top of comprehensive regulatory data. Instead of navigating complex databases or learning query languages, financial professionals can:

• Ask follow-up questions: “Tell me more about the environmental violations for the top 3 companies”
• Request comparisons: “Compare company compliance scores between our construction and manufacturing portfolios”
• Generate reports: “Create a company compliance summary for our quarterly risk committee”
• Set monitoring: “Alert me if any of our current borrowers get new HSE enforcement actions”

SCENARIO: Business Lender Evaluating £5M Manufacturing Loan

Characters:

• Sarah – Senior Credit Risk Manager
• DoordaBot – AI Company Compliance Assistant

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Getting Started Today

Unlike traditional data solutions requiring months of implementation, DoordaOnline delivers immediate value:

1. Sign in with your existing Doorda credentials
2. Ask questions in plain English about any company or sector
3. Refine results through natural conversation
4. Export insights directly to your existing workflows

No technical setup. No training required. Just answers.

The Competitive Edge

In financial services, the competitive advantage increasingly belongs to those who can see beyond traditional metrics. While competitors rely solely on financial statements, forward-thinking lenders and insurers using DoordaOnline gain visibility into the regulatory health that truly drives business stability.

The data has always existed across dozens of regulatory sources. What changed is the AI-powered accessibility that makes this intelligence actionable for every underwriter and credit analyst—not just data scientists.