01
Signal grammar.
Behavioural patterns are represented at signal level, so detection can operate across exchanges rather than isolated messages.
Hidden pattern intelligence
Signal-level detection of harmful patterns in digital conversations.
Iris is a deterministic signal-processing engine with confidence scoring. Built for institutions that need auditable answers, not generative guesses.
- 8
- Patent families
- 46
- Verticals
- 17
- Source jurisdictions
- 1,000+
- Reference authorities
Coercion, exploitation and fraud unfold over time.
Iris detects the cumulative pattern.
The gap
Why current tools miss the pattern.
Conversation analytics, transaction monitoring and case review tools share one structural blind spot: they look at events one at a time. Harmful behaviour often appears only when exchanges are read as a pattern across messages, transactions, channels and time.
Iris detects cumulative patterns that single-event analytics miss.
The engine
A signal-level architecture for auditable detection.
Iris is a deterministic signal-processing engine with calibrated confidence scoring. It surfaces patterns for humans to act on, without generating content or classifying people.
02
Scoring layer.
Each output carries calibrated confidence. The same input produces the same answer, so results are reproducible.
03
Evidence trail.
Every detection can be traced back to the source signals and the regulatory obligation it helps evidence.
The output
Six behavioural dimensions.
Every detection maps to one or more behavioural dimensions, creating a consistent language for patterns across financial services, safety, abuse and exploitation contexts.
01
Control intensity
Monitoring, permission-seeking, decision dominance, information gatekeeping.
02
Manipulation sophistication
Reality distortion, narrative construction, emotional weaponisation.
03
Exploitation severity
Financial drain, resource depletion, identity theft, data harvesting.
04
Escalation trajectory
Changepoint detection, cycling patterns, severity rate-of-change.
05
Network manipulation
Isolation, ally recruitment, support-network disruption.
06
Evasion sophistication
Coded language, channel-switching, evidence destruction.
Iris ASM
The authorities selection process.
Iris Authorities Selection Methodology is the criteria-based process by which Iris identifies, assesses and classifies every reference authority that could bear on a behavioural-signal detection layer.
Builds the universe.
Candidate authorities are identified per vertical, top-down from primary regulators with enforcement authority, then broadened by onward citation.
Classifies the authority.
Each authority is assessed against a deterministic rule set, with the outcome recorded against named criteria.
Reproducible.
Every classification is reproducible by a third party applying the same methodology to the same inputs.
- 17
- Source jurisdictions
- 11
- Deployment-scope jurisdictions
- 70+
- Regulatory instruments
- 1,000+
- Reference authorities
Regulation
Built to evidence named obligations.
Iris positions pattern detection against the regimes institutions already need to evidence, without disclosing implementation detail.
| Regime | What Iris evidences | Anchor |
|---|---|---|
| FCA Consumer Duty | Detection of vulnerability outcomes including the four FG21/1 drivers; auditable evidence of the firm acting in good faith. | PS22/9, FG21/1 |
| FCA financial crime | Pattern detection across communications and transactions; APP-fraud safe-account narratives; investment scams. | FCG, JMLSG, PSR APP rules |
| Online Safety Act | Cumulative-pattern detection in user-to-user services; risk-assessment evidence at the platform level. | OSA Part 3, Ofcom codes |
| Data protection | Lawful-by-design architecture; controller-retained data; auditable purpose limitation. | UK GDPR, DUA Act 2026 |
| EU AI Act | Determinism, traceability, post-market monitoring; evidence appropriate to the risk classification of the deployment. | EU AI Act, Annex III |
| Modern slavery | Pattern detection in recruitment, debt bondage, and document control. | Modern Slavery Act 2015 s.54 |
| Online safety equivalents | US, EU, and Australia online-safety duties addressed under the same engine. | KOSA (US), DSA (EU), eSafety (AU) |
Coverage
Forty-six verticals across two books.
Two books: a financial services book, and a domain book covering safety, abuse and exploitation contexts.
Financial services
Retail banking vulnerability, motor finance, mortgage and property, APP fraud, pension scams, market manipulation, romance fraud (FS overlay), open banking and economic abuse.
Domain book
Housing and cuckooing, modern slavery and human trafficking, coercive control, elder financial abuse, image-based sexual abuse, gambling harm, tech-facilitated abuse, harassment and hate crime.
Detail at typology level accessible under NDA.
Where it fits
Institutional and frontline deployment.
Institutional deployment.
Banks, platforms, and regulated firms.
Iris integrates with organisations that need to detect coercive, fraudulent, exploitative or manipulative patterns at scale, with audit-grade evidence and full data control retained by the institution.
Frontline deployment.
Advocacy, oversight, and research teams.
Iris also supports organisations working on harm directly. The same engine, applied diagnostically, helps surface patterns in evidence that would otherwise be invisible to a single reviewer.
Patent and IP
Patent pending across multiple families.
Iris is patent pending across multiple families covering signal-processing approaches to behavioural pattern detection in digital conversations.
Founder
Caroline Wells
Caroline has 25 years in financial services - banking, wealth and investment management, insurance - and a background in digital communications. Her work sits where compliance, technology and consumer harm meet. Caroline is founder and sole inventor of the Iris portfolio.
Caroline contributes to UK consultations and working groups on online safety, multi-agency intelligence sharing and AI governance, pressing for a more deployment-focused AI agenda for safeguarding. She studied at the Cardiff School of Journalism and holds professional qualifications from LSE, the CII and the CISI.
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