AI Pattern Detection - Disclosurely AI Features

Detect patterns across reports, identify emerging trends, recognize related incidents, network analysis, and automated pattern alerts for whistleblowing.

Pattern Detection

AI-powered pattern detection analyzes your organization's whistleblowing reports to identify trends, recurring issues, related incidents, and systemic problems that might not be obvious from individual cases. This powerful feature helps investigators spot serial offenders, emerging risks, and organizational vulnerabilities before they escalate.

What is AI Pattern Detection?

Disclosurely's Pattern Detection engine continuously analyzes all cases in your organization to identify meaningful patterns, connections, and trends. By examining similarities across allegations, subjects, departments, timeframes, and outcomes, the AI reveals insights that would take months of manual analysis to uncover.

Key Capabilities:

Similar Case Detection: Find reports with related allegations, subjects, or circumstances
Trend Identification: Spot emerging issues before they become widespread problems
Serial Offender Recognition: Identify individuals with multiple complaints over time
Department Risk Mapping: Highlight departments with recurring misconduct
Network Analysis: Discover connections between people, departments, and incidents
Temporal Pattern Analysis: Detect cyclical or time-based patterns in misconduct
Automated Alerts: Receive notifications when patterns reach significance thresholds

How AI Pattern Detection Works

Intelligent Analysis Engine

The AI examines multiple dimensions simultaneously to identify patterns:

Data Collection: Analyzes all historical and current cases (with proper access controls)
Feature Extraction: Identifies key characteristics of each case (type, people, department, severity, etc.)
Similarity Matching: Compares cases across multiple dimensions using advanced algorithms
Pattern Recognition: Identifies statistically significant patterns and trends
Confidence Scoring: Assigns confidence levels to detected patterns (High, Medium, Low)
Alert Generation: Notifies investigators when significant patterns emerge
Continuous Learning: Refines pattern detection as more cases are processed

Pattern Analysis Dimensions

The AI analyzes patterns across 15+ dimensions:

People-Based Patterns:

Multiple reports about the same individual (serial offender)
Repeated complainants (may indicate retaliation or ongoing issue)
Common witnesses across cases
Supervisory chain patterns
Department or team patterns

Allegation-Based Patterns:

Similar misconduct types
Escalating severity of related allegations
Common policy violations
Related compliance issues
Similar evidence types

Location-Based Patterns:

Department or team clustering
Geographic location patterns
Remote vs. on-site patterns
Cross-departmental patterns

Temporal Patterns:

Time-of-year seasonality
Day-of-week patterns
Incident timing clusters
Investigation duration patterns
Cyclical reporting patterns

Outcome Patterns:

Similar investigation outcomes
Common recommended actions
Policy change triggers
Substantiation rates by type
Resolution effectiveness

Key Features

1. Similar Case Detection

Automatically identify cases related to the current investigation.

When Viewing a Case:

AI displays "Similar Cases" panel
Shows cases ranked by similarity (0-100% match)
Explains why cases are similar
Links to related investigations
Suggests pattern investigation approach

Example:

SIMILAR CASES DETECTED

Current Case: WB-2024-0342 (Harassment by Manager Sarah J.)
Similarity Threshold: 65%+

CASE #1: WB-2023-0189 (Similarity: 87%)
Type: Workplace Harassment
Subject: Sarah J. (same person)
Date: 6 months ago
Outcome: Verbal warning, management training
Key Similarity: Same subject, similar harassment allegations

CASE #2: WB-2023-0421 (Similarity: 72%)
Type: Hostile Work Environment
Subject: Sarah J. (same person)
Date: 1 year ago
Outcome: Coaching provided
Key Similarity: Same subject, related behavioral issues

CASE #3: WB-2024-0198 (Similarity: 68%)
Type: Management Misconduct
Subject: Different manager in same department
Date: 2 months ago
Outcome: In progress
Key Similarity: Same department, similar management issues

PATTERN ALERT: Serial Offender
Subject Sarah J. has 3 reports in 18 months. Previous interventions
(training, coaching) have not resolved behavior. Consider escalation.

2. Trend Analysis

Identify emerging issues before they become widespread problems.

Trend Types Detected:

Volume Trends:

Increasing reports of specific misconduct types
Department-specific report volume changes
Seasonal variations in reporting
Post-intervention trend changes

Severity Trends:

Escalating seriousness of similar allegations
Risk score trends over time
Investigation complexity increasing
Impact severity patterns

Behavioral Trends:

New types of misconduct emerging
Policy violation frequency changes
Compliance issue increases
Cultural indicators

Effectiveness Trends:

Investigation outcome patterns
Intervention effectiveness
Policy change impact
Training effectiveness indicators

Access Trends:

Dashboard > Analytics > Trends
View trend charts and graphs
Filter by timeframe, department, type
Export trend reports
Set trend-based alerts

Example Trend Alert:

TREND ALERT: Emerging Pattern Detected

TREND: Discrimination Complaints Increasing in Engineering Department

Data Points:
• Q1 2024: 2 reports
• Q2 2024: 5 reports (150% increase)
• Q3 2024: 8 reports (60% increase)
• Q4 2024 (partial): 6 reports (projected 12 for full quarter)

Pattern Details:
• All reports from Engineering department
• 70% involve age or disability discrimination
• Multiple subjects (not single serial offender)
• Common themes: Promotion decisions, project assignments
• Substantiation rate: 60% (above organizational average of 40%)

Risk Assessment:
• Risk Level: HIGH
• Potential systemic discrimination issue
• Legal exposure significant
• Reputational risk if pattern continues
• May indicate cultural or policy problem

Recommended Actions:
1. Conduct department-wide culture assessment
2. Review recent promotion and assignment decisions for bias
3. Enhance manager training on discrimination prevention
4. Consider external expert review
5. Implement enhanced monitoring for Engineering department
6. Review hiring and promotion policies

3. Serial Offender Identification

Automatically flag individuals with multiple complaints.

Serial Offender Criteria:

Multiple reports about same person (configurable threshold: 2+, 3+, etc.)
Timeframe analysis (e.g., 3 reports in 12 months vs. 3 reports in 5 years)
Severity consideration (multiple serious allegations vs. minor issues)
Outcome patterns (repeat after interventions vs. first-time substantiated)
Escalation tracking (are allegations becoming more serious?)

Alert Trigger: When a new report is filed about someone with previous complaints, the AI immediately alerts investigators:

SERIAL OFFENDER ALERT

New Report: WB-2024-0456
Subject: Michael T., Senior Manager

PREVIOUS REPORTS:
• WB-2023-0234 (8 months ago): Bullying - SUBSTANTIATED
  Outcome: Written warning, coaching
• WB-2023-0712 (4 months ago): Retaliation - SUBSTANTIATED
  Outcome: Final written warning, management training
• WB-2024-0456 (NEW): Harassment - Under Investigation

PATTERN ASSESSMENT:
• 3 reports in 10 months
• 100% substantiation rate on closed cases
• Progressive discipline applied
• Behavior worsening despite interventions
• Multiple complainants (no repeat reporters)

RISK LEVEL: CRITICAL
• Serial offender pattern confirmed
• Previous interventions ineffective
• Legal liability high
• Termination recommended if substantiated

RECOMMENDED IMMEDIATE ACTIONS:
1. Expedite investigation (treat as urgent)
2. Consider interim suspension
3. Engage legal counsel
4. Review all previous cases for pattern
5. Prepare for potential termination
6. Monitor for additional complaints from others

4. Department Risk Mapping

Visualize which departments have recurring compliance issues.

Department Analysis Metrics:

Report volume by department (per 100 employees)
Substantiation rates by department
Severity of issues by department
Types of misconduct by department
Investigation durations by department
Policy violations by department

Visual Risk Heat Map:

DEPARTMENT RISK HEAT MAP (Last 12 Months)

Department          Reports  Risk Score  Primary Issues
------------------------------------------------------------
Engineering         24       HIGH ██████  Discrimination, Harassment
Sales               18       MEDIUM ████  Fraud, Expense Abuse
Operations          12       MEDIUM ████  Safety Violations
Finance             8        LOW ██      Confidentiality Breaches
HR                  6        LOW ██      Policy Violations
Marketing           4        LOW █       Minor Issues

Drill-Down Analysis:

Click any department for detailed breakdown
View specific cases within department
Identify common subjects or patterns
Compare to organizational benchmarks
Track improvement over time

Actionable Insights:

DEPARTMENT INSIGHT: Engineering Department

Risk Level: HIGH (24 reports, risk score 78/100)

Key Findings:
• 70% higher report rate than organizational average
• Discrimination complaints 3x higher than other departments
• 2 serial offenders identified
• Manager training completion: 65% (below 90% target)
• Recent policy changes not yet effective

Root Cause Hypothesis:
• Rapid growth (50% headcount increase in 2 years)
• Many new managers promoted without training
• Competitive culture may contribute to discrimination
• Work-from-home transition challenges

Recommended Actions:
1. Mandatory manager training for all Engineering leaders
2. Culture assessment by external consultant
3. Review promotion and compensation practices
4. Enhance new hire onboarding for culture fit
5. Implement 360-degree feedback for managers
6. Monthly compliance check-ins with Engineering leadership

5. Network Analysis

Discover hidden connections between people, departments, and incidents.

Network Analysis Reveals:

Common supervisors across multiple cases
Interconnected groups of complainants
Departments with cross-functional issues
Reporting patterns (e.g., reports often go through specific channels)
Witness networks and credibility patterns

Visual Network Graph: Shows connections between:

Subjects of multiple complaints
Departments with related issues
Common witnesses
Shared supervisors
Related case clusters

Example Network Pattern:

NETWORK PATTERN DETECTED

Central Figure: David L., Executive Vice President

Connected Cases: 8 reports in 12 months

Network Analysis:
• David L. is direct or indirect supervisor in 8 recent cases
• 5 of 8 cases involve his direct reports as subjects
• 3 of 8 cases involve whistleblowers reporting to him
• 2 cases name him as witness to misconduct
• 60% of cases in his division vs. 20% in other divisions

Pattern Assessment:
This network pattern suggests either:
1. Toxic culture in David L.'s division
2. David L. is aware of but not addressing misconduct
3. David L. may be contributing to problematic environment
4. His division has particularly strong speak-up culture (positive)

Recommended Investigation:
• Review David L.'s knowledge of and response to prior reports
• Interview current and former employees in his division
• Assess divisional culture independently
• Review his performance management of problem employees
• Consider whether he should recuse from future investigations

6. Temporal Pattern Analysis

Identify time-based patterns in misconduct and reporting.

Time Patterns Detected:

Seasonal Patterns:

Year-end expense fraud increases (bonus season)
Q4 discrimination complaints (promotion cycle)
Summer harassment reports (office social events)

Cyclical Patterns:

Weekly patterns (Monday reports higher - weekend reflection)
Monthly patterns (end-of-month financial misconduct)
Annual patterns (performance review season)

Event-Triggered Patterns:

Post-reorganization complaints spike
After layoff announcements
Following policy changes
Training session aftermath (increased awareness)

Example Temporal Alert:

TEMPORAL PATTERN ALERT

Pattern: Expense Fraud Reports Peak in January

Historical Data (Last 3 Years):
• January average: 12 expense fraud reports/month
• Other months average: 3 expense fraud reports/month
• Pattern: 4x increase every January
• Timing: Reports submitted in January for December expenses

Hypothesis:
• Year-end bonus and vacation spending leads to cash flow issues
• Personal expenses mistakenly (or intentionally) claimed as business
• Rushed year-end expense processing reduces scrutiny
• Year-end audit preparation prompts discovery of fraud

Preventive Actions:
• Increase expense audit sampling in December
• Send fraud prevention reminder in November
• Enhanced approval scrutiny for December expenses
• Finance team training on fraud red flags before year-end
• Consider delaying expense reimbursement for high-risk claims

Expected Impact:
• 50% reduction in January fraud reports
• Earlier detection of year-end fraud attempts
• Deterrent effect from increased scrutiny

7. Automated Pattern Alerts

Receive real-time notifications when significant patterns emerge.

Alert Types:

Threshold Alerts:

Serial offender (2nd, 3rd, or more report about individual)
Department risk threshold exceeded
Trend velocity alert (rapid increase in issue)
Severity escalation (similar cases becoming more serious)

Anomaly Alerts:

Unusual spike in reports
New type of misconduct appearing
Department dramatically above baseline
Investigation durations lengthening

Compliance Alerts:

Pattern suggests regulatory violation
Systemic issue requiring reporting
Policy inadequacy identified
Training effectiveness concerns

Configure Alerts:

Dashboard > Settings > Pattern Alerts
Set thresholds for each alert type
Choose notification method (email, dashboard, SMS)
Assign alert recipients by role
Set alert frequency (immediate, daily digest, weekly)

Example Alert Configuration:

Pattern Alert Configuration

Serial Offender Alert:
✅ Enabled
Threshold: 2 or more substantiated reports in 12 months
Recipients: Compliance Officer, HR Director
Notification: Immediate email + dashboard alert

Trend Alert:
✅ Enabled
Threshold: 50% increase in specific issue type over previous quarter
Recipients: Compliance Team
Notification: Weekly digest

Department Risk Alert:
✅ Enabled
Threshold: Department risk score exceeds 70/100
Recipients: Department Head, HR Business Partner, Compliance Officer
Notification: Immediate email

Network Pattern Alert:
✅ Enabled
Threshold: 5+ cases connected to single individual (as subject, supervisor, or witness)
Recipients: Chief Compliance Officer
Notification: Immediate email + dashboard alert

Privacy and Security

Data Access Controls

Pattern Detection Respects Permissions:

AI only analyzes cases the user has permission to view
Cross-organizational patterns not visible (multi-tenant isolation)
Confidential information protected in pattern reports
Anonymous reporter identities never included in patterns

Privacy by Design:

Pattern analysis uses pseudonymized data where possible
Personal data minimized in pattern reports
Purpose limitation: Patterns used only for compliance and risk management
Data subjects can request pattern data deletion (subject to legal retention)
See GDPR Compliance for full details

Bias Prevention

Avoiding Algorithmic Bias:

AI does not profile based on protected characteristics
Patterns based on behavior and incidents, not demographics
Regular bias audits of pattern detection algorithm
Human oversight required for pattern-based decisions
Investigators can contest or override pattern assessments

Configuration and Setup

Enabling Pattern Detection

Prerequisites:

Enterprise or Pro plan
At least 20 historical cases (for meaningful pattern detection)
AI features enabled
Proper data retention policies configured

Setup Steps:

Enable Pattern Detection
- Dashboard > Settings > AI Features
- Toggle "Pattern Detection" to ON
- Agree to terms of use
Configure Detection Sensitivity
- High: Detects more patterns (may include false positives)
- Medium: Balanced approach (recommended)
- Low: Only strong, clear patterns
Set Alert Thresholds
- Define what constitutes "serial offender"
- Set department risk score thresholds
- Configure trend velocity alerts
- Set network pattern complexity thresholds
Assign Alert Recipients
- Compliance officer
- HR leadership
- Legal team
- Department heads (for their departments)
Configure Privacy Settings
- Anonymize subjects in department-level patterns
- Set visibility restrictions by role
- Configure data retention for pattern reports

Best Practices

Do's: ✅ Investigate Patterns: Don't ignore pattern alerts - they indicate real risk ✅ Combine with Human Judgment: Patterns inform, not determine, decisions ✅ Act on Systemic Issues: Use patterns to identify root causes and fix them ✅ Monitor Effectiveness: Track whether interventions reduce patterns ✅ Share Appropriately: Brief leadership on significant patterns ✅ Update Regularly: Review pattern settings quarterly

Don'ts: ❌ Don't Profile Individuals: Use patterns to assess behavior, not predict misconduct ❌ Don't Ignore Context: Patterns need investigation, not automatic action ❌ Don't Violate Privacy: Respect confidentiality even when patterns detected ❌ Don't Dismiss Outliers: Individual cases matter even without patterns ❌ Don't Over-React: Correlation doesn't always mean causation

Integration with Other AI Features

Pattern Detection + Risk Assessment

AI Risk Assessment uses pattern data to adjust risk scores:

Cases involving serial offenders: +20% risk score
Cases in high-risk departments: +10% risk score
Cases matching concerning trends: +15% risk score
Pattern-based prioritization recommendations

Pattern Detection + Case Helper

AI Case Helper leverages patterns in its recommendations:

"This subject has 2 prior substantiated complaints"
"Similar cases resolved with X outcome"
"Department has pattern requiring systemic intervention"
"Consider whether this is isolated or part of larger issue"

Pattern Detection + Content Generation

AI Content Generation incorporates pattern insights:

Investigation reports mention relevant patterns
Policy recommendations address systemic issues
Executive summaries highlight trend concerns
Communications reference pattern context where appropriate

Reporting and Analytics

Pattern Analytics Dashboard

Access Pattern Insights:

Dashboard > Analytics > Patterns
View all detected patterns
Filter by pattern type, confidence, date range
Drill down into specific patterns
Export pattern reports

Key Metrics:

Total patterns detected (by type)
Pattern confidence distribution
Serial offenders identified
High-risk departments
Trending issues
Pattern resolution rate

Executive Pattern Reports

Generate comprehensive pattern reports for leadership:

Board-Level Pattern Report:

High-level organizational risk patterns
Year-over-year trend analysis
Department comparison
Effectiveness of interventions
Emerging risks
Recommendations

Export Formats:

PDF (formatted for presentation)
PowerPoint (slides for board deck)
CSV (data for further analysis)
Interactive dashboard (live view)

Pricing and Availability

Available On:

✅ Enterprise Plan: Full pattern detection with unlimited cases and advanced features
✅ Pro Plan: Pattern detection for up to 500 cases
❌ Basic Plan: Not available

Upgrade: Contact sales to enable Pattern Detection.

AI Case Helper - Get AI-powered recommendations using pattern insights
AI Risk Assessment - Automated risk scoring enhanced by pattern data
Compliance Overview - Use patterns to improve organizational compliance
Case Assignment - Assign pattern-related cases to appropriate investigators
Analytics and Reporting - Visualize patterns and trends across your organization