SYSTEM ONLINE MODEL v2.4.1 ENSEMBLE ML ACTIVE SYS / 2026-Q1
001 / AUTONOMOUS RISK PREVENTION

We build real‑time AI
risk‑prevention systems
for the property insurance market.

Built for carriers, reinsurers, MGAs, MGUs, underwriting firms, and brokers — helping the entire property insurance market detect, prevent, and respond to property risks before they escalate.

Property insurance is strained by rising claims, climate volatility, and operational bottlenecks. We're engineering AI that strengthens resilience, reduces loss, and empowers insurance firms to prevent before — not assess after.

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0% Claim Reduction
<30s Detection & Action
0% ML Accuracy (Ensemble)
FINBUL_ENGINE v2.4.1 — RISK MONITOR
$ finbul --mode=live --portfolio=carrier_a
Initialising ensemble models (RF / GBM / XGBoost / IsolationForest)...
✓ RandomForest loaded
✓ GradientBoost loaded
✓ XGBoost loaded
✓ IsolationForest loaded
Connecting IoT + underwriting + claims systems...
✓ 2,847 sensors online
 
— ALERT property_id=A4421
risk_score=0.87 class=WATER_LEAK
consensus=4/4 models
action: SHUTOFF_VALVE dispatched
elapsed=23s ↓ within SLA
 
$ _
0 Active Sensors
$571K Losses Prevented YTD
99.98% Platform Uptime
4 Regions (UK · EU · US · APAC)
Platform Features

Designed for
autonomous action.

Finbul is an autonomous risk‑prevention platform for carriers, reinsurers, MGAs, MGUs, underwriting firms, and brokers. Agentic AI, ensemble ML, and under‑30‑second interventions, wired into policy admin, claims, IoT, and underwriting systems.

F-001
Agentic AI Engine

Fully autonomous decision‑making without human‑in‑the‑loop by default. The engine perceives risk, reasons about severity, and dispatches physical interventions or workflow actions in real time.

PERCEPTION → DECISION → ACTION
F-002
Ensemble ML Core

Random Forest, Gradient Boosting, XGBoost, and Isolation Forest working together. Finbul requires consensus across the ensemble before triggering interventions, reducing single‑model blind spots.

RF · GBM · XGBOOST · IF
F-003
Sub‑30‑Second Response

Detection to autonomous action in under 30 seconds. Emergency shutdowns, IoT commands, and service routing execute when every second matters for loss prevention and underwriting visibility.

<30s MEAN TIME
F-004
Real‑Time IoT Mesh

Sensor‑level view of water, fire, environment, and building systems across portfolios. Continuous, holistic monitoring for prevention, claims, and underwriting risk selection.

MULTI‑SENSOR FUSION
F-005
Portfolio Intelligence

Portfolio‑level views for carriers, reinsurers, and underwriting firms. Identify concentration risk, emerging patterns, and live prevention performance across books of business.

BOOK‑LEVEL ANALYTICS
F-006
Enterprise Integration

Native connectors and APIs for policy admin, claims, IoT, and underwriting tools. Surface live risk scores, prevention history, and alerts directly inside existing underwriting and claims workflows.

POLICY · CLAIMS · IOT · UW
Built For

Every firm that
moves risk in property insurance.

Finbul is built for every type of organisation in the property insurance value chain — from carriers and reinsurers to MGAs, MGUs, underwriting firms, and brokers — that wants to move from reactive loss payment to proactive risk prevention and underwriting intelligence at scale.

01
Insurance Carriers

Transform from reactive claims payer to autonomous risk preventer. Improve combined ratios with direct loss reduction, operational efficiency, and differentiated prevention‑led products.

Loss Prevention
02
Reinsurers

Gain portfolio‑wide, real‑time prevention signals from cedants. Use live intervention data to refine exposure models, structure programmes, and reprice risk more dynamically.

Portfolio Intelligence
03
MGAs & MGUs

Embed autonomous prevention into your delegated authority operations. Differentiate your proposition to capacity providers and brokers with measurable loss savings and richer underwriting data.

Delegated Authority
04
Underwriting Firms & Brokers

Give underwriters and brokers live prevention context at quote and bind. Finbul integrates into underwriting workbenches and broker portals, surfacing risk scores, interventions, and portfolio context.

Underwriting Workflows
Performance Data

The numbers
behind prevention.

Simulated portfolio outcomes demonstrating the impact of autonomous prevention versus traditional reactive models across carriers, reinsurers, and underwriting firms.

Prevented Losses Over Time
Cumulative · 12‑month rolling · $000s
LIVE MODEL
Claim Frequency Reduction
Monthly claim rate · Prevention vs. baseline · %
ENSEMBLE ML
Detection Accuracy
94.7%
False Positive Rate
3.2%
Model Consensus Rate
89.1%
System Uptime
99.98%
Enterprise Grade

Built for carriers,
underwriters, and reinsurers.

Finbul is architected for regulated enterprises with global portfolios, regional data controls, and stringent underwriting and claims governance.

SOC 2‑Ready Architecture

Security controls and auditability designed to support SOC 2 Type II and equivalent frameworks from day one of deployment.

End‑to‑End Encryption

Data encrypted in transit (TLS) and at rest (e.g. AES‑256). Zero‑plaintext policy for production systems handling policy, claims, and underwriting data.

Cloud‑Native Redundancy

Multi‑region deployment with automatic failover and tested recovery procedures, supporting global client bases across UK, EU, US, and APAC.

Enterprise SLAs & DPA

Contractual SLAs, data processing addenda, and security exhibits aligned with carrier, reinsurer, and underwriting firm requirements.

SECURITY ARCHITECTURE / SYSTEM DIAGRAM
ENTERPRISE CLIENT LAYER
↓
TLS 1.3 · ZERO TRUST AUTH
↓
AGENTIC AI ENGINE · ENSEMBLE ML CORE
↓
IoT LAYER
CLAIMS API
POLICY & UW DB
↓
ENCRYPTED DATA STORES
↓
MULTI‑REGION CLOUD REDUNDANCY
● 24/7 Monitor ● Auto Failover ● SOC 2 Ready
Limited Availability

Be among the
first to deploy.

Finbul is available through an early‑access programme for carriers, reinsurers, underwriting firms, MGAs/MGUs, and brokers. Founding partners shape the product, benchmarks, and roadmap.

Validate Autonomous ROI

Quantify loss reduction, operational savings, and retention impact across your property portfolio. Establish prevention benchmarks for your lines of business.

Shape AI Behaviour

Configure thresholds, escalation paths, and intervention rules to match your risk appetite and underwriting authority structures.

Workflow Integrations

Connect Finbul to policy admin, claims, IoT, and underwriting systems so prevention intelligence appears where your teams already work.

Founding Partner Status

Preferential commercial terms, roadmap influence, and dedicated support. Your most pressing prevention and underwriting challenges become our next releases.

Request Access

LIMITED SLOTS · NO COMMITMENT REQUIRED TO APPLY

FAQ

Frequently asked
questions.

Predictive analytics tells you a risk might happen. Finbul acts on it — autonomously, within seconds. It is an action platform, not just a prediction layer: the agentic AI engine dispatches real‑world responses and feeds prevention signals back into underwriting, claims, and portfolio management.

Finbul integrates with underwriting workbenches and policy admin systems. Underwriters see live risk scores, recent prevention actions, and portfolio context at quote and bind, without leaving their existing tools.

Finbul supports a configurable autonomy spectrum. Low‑risk interventions can run fully autonomously in under 30 seconds. High‑impact actions can require human approval or multi‑step escalation. You define the guardrails; Finbul executes within those parameters.

Finbul uses encryption in transit (TLS) and at rest (e.g. AES‑256), least‑privilege access controls, network segmentation, and continuous monitoring. We operate to SOC 2‑ready standards and support regional hosting patterns for UK, EU, US, and APAC clients.

Our public legal hub includes the Privacy Policy, Terms of Service, Security Practices Overview, Cookie Policy, and a current Sub‑Processors List. A Data Processing Addendum (DPA) template is available for download, and a signed DPA can be executed on request for eligible customers.

Limited Access Slots

Secure your
partnership.

Join carriers, reinsurers, underwriting firms, MGAs/MGUs, and brokers across UK, EU, US, and APAC who are moving from reactive claims to autonomous prevention. Our team will respond within 24 hours.

PROGRAMME INCLUDES
Platform Access
Full access for 90 days, no long‑term commitment
Integration Support
Dedicated onboarding across policy, claims, IoT, and underwriting systems
ROI Measurement
Custom financial and operational impact framework for your portfolios
Founding Partner
Preferential licensing + roadmap input
Current Status

Accepting 1 February 2026 cohort applications. Limited slots available.

APPLICATION FORM

By submitting you agree to our Privacy Policy. We will use your details solely to respond to this enquiry. Eulerian Holdings Ltd. · Company No. 16551691.

7EULERIANSTRATEGIES · ABOUT US FOUNDED 2025 · UK
ABOUT / 7EULERIANSTRATEGIES

Engineering
the prevention
era.

We build real‑time AI risk‑prevention systems for the property insurance market — built for carriers, reinsurers, MGAs, MGUs, underwriting firms, and brokers to detect, prevent, and respond to property risks before they escalate.

Property insurance is strained by rising claims, climate volatility, and operational bottlenecks. We're engineering AI that strengthens resilience, reduces loss, and empowers insurance firms to prevent before — not assess after.

Kent & Kingston
University roots
Founded by engineering and research undergraduates united by a shared belief that property insurance deserves better tools and better outcomes.
Production‑Grade
Engineering culture
Large‑scale ML systems, production data pipelines, and real‑world insurance industry exposure — from day one.
UK · EU · US · APAC
Global ambition
Building a platform for the world's largest insurance markets, from a team that cares about the craft as much as the impact.
Our Story

Why we founded
7EulerianStrategies.

7EulerianStrategies was founded by engineering and research undergraduates from Kent and Kingston University, united by a shared belief that property insurance deserves better tools, better intelligence, and better outcomes.

Our team grew out of academic research labs and hands‑on engineering environments. Between us, we've shipped large‑scale machine‑learning systems, built production‑grade data pipelines, and worked inside companies that collaborate directly with insurance firms and risk‑focused organisations.

Those experiences exposed the same pattern everywhere: traditional insurance workflows are overloaded, reactive, and underserved by modern technology.

We founded 7EulerianStrategies to change that. We're building Finbul — a real‑time, agentic AI platform designed to prevent property risk before it becomes loss.

Our mission is simple: combine rigorous engineering with deep industry understanding to create AI that genuinely serves the people solving real‑world problems in underwriting, claims, and risk prevention.

For us, it's not just about writing code or deploying models. It's about providing value, elevating a traditional industry, and enjoying the journey of innovation — together, as a team that cares about the craft as much as the impact.

WHAT WE STAND FOR
  • Engineering rigour
    Production‑grade systems, not prototypes. We ship code that holds under real portfolio conditions, not just demo environments.
  • Industry depth
    We've worked alongside insurance professionals and risk organisations. We understand the constraints, the governance, and what success actually looks like.
  • Prevent before, not assess after
    The industry is built on indemnification. We're building the infrastructure to shift it towards prevention — where every second saved is a loss avoided.
  • Team over hierarchy
    We move fast because we trust each other. Decisions are made by people closest to the problem, and the work is better for it.
  • Craft meets impact
    We genuinely love what we build. That care — for the elegance of the system, the accuracy of the model, the clarity of the interface — shows up in every deployment.
The Team

Built by engineers
who understand insurance.

Every member of our team has direct experience building ML systems, working inside regulated industries, or collaborating with risk‑focused organisations. That context shapes everything we ship.

7E
Research & ML Engineering
Academic Research · Production ML

Coming from academic research labs, our ML engineers combine the rigour of peer‑reviewed methodology with the pragmatism of production systems that have to work at portfolio scale, every second of every day.

Ensemble Methods Anomaly Detection Time‑Series
7E
Platform & Data Engineering
Systems Architecture · Data Pipelines

Our platform engineers have shipped large‑scale data pipelines and real‑time processing systems. They're the reason Finbul can go from IoT signal to intervention action in under 30 seconds — reliably, at scale.

Real‑Time Systems IoT Infrastructure Cloud Architecture
7E
Insurance & Risk Domain
Industry Knowledge · Product Strategy

Having worked with companies that operate directly inside insurance and risk organisations, our domain team bridges the gap between what AI can do and what underwriting, claims, and risk professionals actually need.

Underwriting Workflows Claims Prevention Portfolio Analytics
Connect With Us Follow our progress at 7EulerianStrategies
Our Mission

Rigorous engineering.
Deep industry
understanding.

We exist to create AI that genuinely serves the people solving real‑world problems in underwriting, claims, and risk prevention. Not another dashboard — an intelligence that acts, learns, and makes the system stronger every time it fires.

HOW WE'RE DOING IT
  • Agentic AI that closes the loop
    Not just predictions — real‑world actions dispatched autonomously in under 30 seconds, from risk signal to physical intervention.
  • Ensemble models, not single bets
    Four ML models working in consensus. Fewer false positives, stronger recall, and explainability that enterprise risk teams trust.
  • Built into existing workflows
    We don't ask underwriters to change tools. Finbul surfaces intelligence inside the systems they already use every day.
  • Enterprise‑grade from the start
    SOC 2‑ready, end‑to‑end encrypted, multi‑region hosted. The security posture carriers and reinsurers require before they'll trust any platform with production data.
Work With Us

Ready to prevent before
instead of assess after?

Whether you're a carrier, reinsurer, MGA, or underwriting firm — if you're serious about shifting from reactive loss payment to autonomous prevention, we want to hear from you. Founding partner slots are limited.

Enquire Now Follow on LinkedIn

NO COMMITMENT REQUIRED · RESPONSE WITHIN 24 HOURS

P(H|D) ∝ P(D|H)·P(H) E[L] = Σ pᵢ·Lᵢ v(x) = xᵅ if x≥0 d′ = (μₛ−μₙ)/σ H(X) = −Σp log p
LIVE RESEARCH HUB VOLUME IV · Q1 2026 7EULERIANSTRATEGIES · INSIGHTS
002 / RISK INTELLIGENCE INSIGHTS

Behavioural
science meets
property risk.

Academic research, practitioner analysis, and evidence-based perspectives on how human cognition shapes property insurance decisions — from underwriting judgment to claims settlement and climate perception.

24 Articles Published
4 Research Streams
Q1 2026 Latest Edition
Behavioural Risk
Stream 01 · 6 articles
Heuristics, biases, and cognitive shortcuts that distort property risk perception in policyholders and risk managers.
Underwriting Psychology
Stream 02 · 6 articles
Dual-process theory, framing, and groupthink in underwriting judgment and risk selection.
Claims Decision-Making
Stream 03 · 6 articles
Loss aversion, the endowment effect, and prospect theory applied to settlement negotiation and reserve setting.
Climate & Perception
Stream 04 · 6 articles
Temporal discounting, dread risk, and social amplification in climate-exposed property markets.
FEATURED / MOST RECENT
P(H|D) = P(D|H)·P(H) / P(D)
Featured Behavioural Risk Mar 2026
Mar 2026 · 10 min read

The Availability Heuristic and Systematic Property Risk Misjudgment: Implications for Underwriting and Prevention

When a major flood event dominates news coverage for three weeks, residential property owners in unaffected regions temporarily inflate their personal flood risk estimates by a measurable margin — then revert to systematic under-estimation within months. This paper examines the availability heuristic's role in property risk perception, its quantifiable effect on insurance purchasing behaviour, and the structural interventions that risk-prevention platforms can deploy to counteract it.

7EulerianStrategies Research 10 MIN 1,842 views
SHOWING 8 OF 23 ARTICLES
Interactive Research Tools

The mathematics of
risk cognition.

Four foundational models from psychology and decision science, rendered interactively. Adjust parameters to observe how cognitive and mathematical risk frameworks respond.

01 · Bayesian Updating
P(H|D) = P(D|H) · P(H) / P(D)
Prior P(H) 0.30
Likelihood 0.75
False +ve rate 0.15
Posterior P(H|D): —
Updating prior loss beliefs with new sensor evidence. Critical for understanding how real-time IoT data should shift underwriter priors on expected claim frequency.
02 · Prospect Theory Value Function
v(x) = xᵅ (gains) · −λ(−x)ᵝ (losses)
Loss aversion λ 2.25
Gain curve α 0.88
Loss aversion ratio: 2.25× gains
Kahneman & Tversky's (1979) value function explains why policyholders feel a £5,000 loss far more acutely than a £5,000 premium saving — with direct implications for claims negotiation.
03 · Expected Loss Model
E[L] = Σᵢ pᵢ · Lᵢ · (1 − mᵢ)
Freq. (water) 8%
Freq. (fire) 3%
Mitigation m 35%
Expected annual loss: —
Computes portfolio expected loss across risk categories with autonomous prevention mitigation applied. The mᵢ term represents Finbul's intervention effectiveness per peril class.
04 · Signal Detection Theory
d′ = (μ_signal − μ_noise) / σ · β = threshold
Sensitivity d′ 2.0
Threshold β 0.0
Hit rate: — · False +ve: —
Models the tradeoff between false alarms and missed detections in autonomous risk alerting. Critical for calibrating intervention thresholds across carrier risk appetites.
Category · Date

Article Title

Author · Read time
Legal Hub

Policies for
enterprise clients.

This hub summarises how 7EulerianStrategies and Finbul operate from a legal, privacy, and security perspective. All documents are effective from 1 February 2026.