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How is Decision Intel different from ChatGPT or a general AI assistant?

ChatGPT gives one opinion from one model: ungoverned, untraceable, unaudited. Decision Intel is the reasoning audit platform. Most tools audit your data; we audit your reasoning — and catch the fatal blind spots in strategic memos before the committee does. It measures the noise in your reasoning the same way Kahneman did in the insurance underwriter study, simulates an AI boardroom of CEO, CFO, and board personas, runs what-if interventions against a 143-case public reference library, and compounds every confirmed outcome back into a calibrated Decision Quality Index your audit committee can defend. Not a chatbot; a reasoning audit, checkable from memo to outcome.

Which cognitive biases caused London Capital & Finance's collapse?

5 bias patterns are documented in this case, led by Cognitive Misering. The full set: Cognitive Misering, Status Quo Bias, Authority Bias, Selective Perception, Confirmation Bias. Each is mapped to Decision Intel's 22-bias R²F taxonomy with stable IDs and academic citations.

What is Decision Intel's reasoning audit?

Decision Intel runs a 12-node Recognition-Rigor Framework (R²F) pipeline on strategic memos, detecting cognitive biases from a 22-bias canonical taxonomy and producing a Decision Quality Index (DQI) score. The audit produces a hashed, tamper-evident Decision Provenance Record (DPR) suitable for audit-committee review.

All case studies

Catastrophic failureFinancial Services2019

London Capital & Finance

London Capital & Finance Fraud

Estimated impact: £237M investor losses

F14

Decision Quality Index (simulated)

Critical · 5 biases detected, 2 toxic combinations

LCF raised £237M from 11,600 investors through misleading ISA bonds promising 8% returns. Funds were lent to connected parties for high-risk ventures. The FCA failed to act on warnings for years, and the Financial Services Compensation Scheme initially refused coverage.

Decision context

Whether the FCA should have intervened earlier when it received complaints about LCF's marketing of unregulated mini-bonds through regulated ISA wrappers.

Decision anatomy

Monetary stakes

High

Dissent suppressed

Time pressure

None

Unanimous consensus

Decision participants

Dissent encouraged

External advisors

Iterative process

Red = risk factor present · Green = protective factor present

Deep hindsight-stripped analysis coming soon. This case is part of our ongoing backfill to reconstruct what the platform would have flagged at decision time. The summary and bias profile below are drawn from primary sources.

Biases present in the decision

★Cognitive Misering

critical

Status Quo Bias

medium

Authority Bias

high

Selective Perception

medium

Confirmation Bias

high

★ Primary driver · Severity estimated from bias type and decision outcome

3D Decision Knowledge Graph

London Capital & Finance · 5 biases, 2 toxic combinations

Drag to rotate · Scroll to zoom · Click to explore

Decision

Critical

High

Medium

Toxic

Bias Intensity Profile

Severity × confidence across 5 detected biases

Toxic combinations

!Status Quo Lock

Loss aversion combined with status quo bias makes even clearly beneficial change feel impossible.

!Blind Sprint

Overconfidence combined with planning fallacy creates unstoppable momentum toward failure.

Reference class base rates

Across all 143 curated case studies in our library:

89%

base failure rate

-0%

partial / mixed

11%

base success rate

■ Failure■ Partial■ Success

Lessons learned

Cognitive misering in regulatory oversight means complaints are processed bureaucratically rather than analytically
The regulatory perimeter gap between ISA wrappers (regulated) and mini-bonds (unregulated) was exploited
Status quo bias in the FCA led to treating each complaint in isolation rather than identifying a systemic pattern

Source: Dame Elizabeth Gloster Independent Investigation into the FCA's Regulation of LCF (2020) (FCA Enforcement)

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We caught these patterns in London Capital & Finance's own record — before the outcome.

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Workflows that fire on decisions like London Capital & Finance’s

The same Recognition-Rigor Framework that documents this case audits memos in the same shape — before the outcome forces the lesson.

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All case studies

Catastrophic failureFinancial Services2019

London Capital & Finance

London Capital & Finance Fraud

Estimated impact: £237M investor losses

F14

Decision Quality Index (simulated)

Critical · 5 biases detected, 2 toxic combinations

Decision context

Whether the FCA should have intervened earlier when it received complaints about LCF's marketing of unregulated mini-bonds through regulated ISA wrappers.

Decision anatomy

Monetary stakes

High

Dissent suppressed

Time pressure

None

Unanimous consensus

Decision participants

Dissent encouraged

External advisors

Iterative process

Red = risk factor present · Green = protective factor present

Biases present in the decision

★Cognitive Misering

critical

Status Quo Bias

medium

Authority Bias

high

Selective Perception

medium

Confirmation Bias

high

★ Primary driver · Severity estimated from bias type and decision outcome

3D Decision Knowledge Graph

London Capital & Finance · 5 biases, 2 toxic combinations

Drag to rotate · Scroll to zoom · Click to explore

Decision

Critical

High

Medium

Toxic

Bias Intensity Profile

Severity × confidence across 5 detected biases

Toxic combinations

!Status Quo Lock

Loss aversion combined with status quo bias makes even clearly beneficial change feel impossible.

!Blind Sprint

Overconfidence combined with planning fallacy creates unstoppable momentum toward failure.

Reference class base rates

Across all 143 curated case studies in our library:

89%

base failure rate

-0%

partial / mixed

11%

base success rate

■ Failure■ Partial■ Success

Lessons learned

Cognitive misering in regulatory oversight means complaints are processed bureaucratically rather than analytically
The regulatory perimeter gap between ISA wrappers (regulated) and mini-bonds (unregulated) was exploited
Status quo bias in the FCA led to treating each complaint in isolation rather than identifying a systemic pattern

Source: Dame Elizabeth Gloster Independent Investigation into the FCA's Regulation of LCF (2020) (FCA Enforcement)

Pilot slots available