Due Diligence Basics on AI Analysis

Market Name: AI Due Diligence

Intelligent Early Warning System for Companies and Organizations

Due Diligence Based on AI Analysis

A comprehensive overview and application of the advanced cognitive capabilities and data analysis capabilities of HRMS systems.

Great! Integrating AI due diligence into HRMS systems is a cutting-edge and highly valuable application. The attendance, payroll, and expense data available in the DGX HRMS system is a valuable resource for in-depth employee behavior analysis and risk insights.

Below, I'll build a complete AI due diligence solution for you.

Core Objective: AI Due Diligence System

Due Diligence, in this context, aims to leverage AI models (such as the DeepSeek API) to automatically and intelligently analyze employee behavior data, identify potential risks, unusual patterns, and compliance issues, and provide management with data-driven decision support.

Key Analysis Dimensions:

1. Compliance Risk: Examining whether employee behavior complies with company policies, laws, and regulations.
2. Financial Risk: Detecting potential fraud, waste, or abuse.
3. Operational Efficiency: Assessing employee productivity and cost-effectiveness.
4. Turning and Engagement Risk: Predicting the likelihood of high-performing employees leaving.

Data Integration and AI Analysis Framework

The following is a specific plan for leveraging existing data for analysis:

Provide a new data entry

New Core: Due Diligence Dimension Based on Interaction and Text

Traditional financial audits are "looking back," while your proposed approach is "looking forward" and "getting to the heart of the matter," aiming to assess employee engagement, integrity, cultural fit, and potential behavioral risks.

Dimension One: AI Analysis of Work Reports and Interactions

This component aims to assess work authenticity, productivity, management relationships, and potential warning signs by analyzing textual interactions between employees and supervisors.

Technical Implementation (DeepSeek API Example):

# Prompt Example: Analyzing a Single Work Report
prompt = f"""
You are a senior HR analyst. Please conduct an in-depth analysis of the following employee's work report:

[Employee Weekly Report]
{weekly_report_text}

[Manager's Feedback]
{manager_feedback_text}

[Employee Last Weekly Report]
{last_weekly_report_text}

Please provide analysis based on the following dimensions:

1. **Progress Alignment**: Did this week's work make reasonable progress based on last week's plan? Are there any inconsistencies or ambiguities?

2. **Outcome Specificity**: Was the description of the work results specific and measurable?

3. **Interaction Quality**: Based on the manager's feedback, analyze the effectiveness of the interaction between the two parties.
4. **Risk Identification**: List one or two of the greatest potential risk points (if any).

Please output in JSON format, including the "score" (percentile) and "analysis" fields.
""

Dimension 2: Social Conduct and Whistleblowing Investigation Analysis

Social Conduct and Whistleblowing Investigation Analysis

This section aims to structure informal, online information so it can serve as supporting evidence for investigations. However, care must be taken to comply with privacy and labor laws.

Technical Implementation (Disciplinary Analysis Example):

# Prompt Example: Analyzing a Single Report
prompt = f"""
You are a compliance investigator. Please analyze the following report:

[Report Content]
{whistleblowing_text}

[Related Data Context] (Optional)
- Related Employee Expense Records: {expense_data}
- Related Employee Attendance Records: {attendance_data}

Please complete the following tasks:

1. **Information Extraction**: List the core facts alleged in the report.

2. **Severity Assessment**: Classify the allegations as "high risk" (e.g., illegality, fraud), "medium risk" (e.g., policy violation), or "low risk" (e.g., interpersonal conflict).

3. **Preliminary Credibility Assessment**: Determine the credibility of the report (high, medium, or low) based on the specific details and context provided (if any).
4. **Investigation Recommendations**: Recommends next steps for investigation (e.g., "Review surveillance during the XX time period," "Interview XX relevant individuals").

Output format is JSON.
""

Integrated Due Diligence Report: Final AI Output

The ultimate value of AI isn't providing discrete insights, but rather generating a Integrated Due Diligence Report. This report can be generated for a single employee or a team.

Report structure example:

{
"employee_id": "12345",
"risk_score": 65,
"risk_level": "Medium",
"summary": "This employee's financial data is normal, but work reports show slow progress and negative interactions with their supervisor. There is a recent anonymous report that requires attention."

"details": {
"financial_analysis": {...},
"work_report_analysis": {
"consistency_score": 40,
"specificity_score": 30,
"manager_engagement_trend": "Declining",
"key_risk": "Failed to meet self-imposed milestones for two consecutive weeks and did not respond to supervisor's guidance feedback."

},
"conduct_analysis": {
"reported_incidents": 1,
"latest_incident": {
"severity": "Medium",
"credibility": "Medium",
"summary": "Reported for using company resources for personal matters.",
"recommended_action": "It is recommended to review the network and device usage logs with the IT department. "
}
}
},
"recommended_actions": [
"1. The supervisor should conduct a formal interview with the employee to understand the obstacles to project progress. ",
"2. The Compliance Department should initiate a preliminary investigation into the report. "
]
}

Technical Implementation Path (How to Do It)

Step 1: Data Preparation and Preprocessing

1. Data Cleansing and Standardization: Clean data from different DGX HRMS modules (CSV, database tables), unifying time formats, currency units, employee IDs, etc.
2. Feature Engineering: This is the most critical step. Convert raw data into features that the AI ​​model can understand.

• Attendance Features: Monthly number of latenesses and early departures, average daily work hours, frequency of weekend overtime, frequency of IP address changes when clocking in, etc.
• Reimbursement Features: Monthly total reimbursement amount, reimbursement frequency, average single reimbursement amount, proportion of each category for food, transportation, and accommodation, invoice number consistency, etc.
• Salary Features: Deviation from department average salary, historical salary increase range, correlation between bonuses and performance, etc.

3. Data Anonymization: For privacy reasons, hashed employee IDs can be used instead of real names during training and analysis.

Step 2: Model Selection and Analysis

For Quantitative Anomaly Detection (Best Path):

• Using a Machine Learning Library (e.g., Scikit-learn): Run the model directly on your server for low cost, high speed, and good privacy protection.
• Isolation Forest and One-Class Support Vector Machine (SVM): Well-suited for identifying a few "anomalies" from the majority of "normal" behavior.
• Logistic Regression/Decision Tree: Used for classification of labeled data (e.g., training a model to predict new cases given a known set of historical fraud cases).
• Process: Extract features using Python -> Use the trained model for predictions -> Output anomaly scores and labels.

**For Qualitative Analysis and Deep Insight (with API):

• Using the DeepSeek API: Used when understanding complex context and generating natural language reports are required.

• Scenario: Send all data (features, raw records) for an employee marked as "abnormal" to DeepSeek, requesting it to analyze risk points and generate an easy-to-understand investigation summary for HR.

• Sample prompt:

"You are an HR investigation expert. Please analyze the following employee's data, identify potential compliance risks, and provide investigation recommendations in a concise list format." [Employee ID]: 12345 [Abnormal Features]: Employees whose monthly expense total exceeds 98% of the department's total, frequently claiming meals during off-hours, and whose invoice numbers are multiple and consecutive. [Raw Data Snippet]: [Insert a few sample expense records here] Please analyze: Do these actions potentially constitute fraudulent expense claims? What should the investigator focus on next? ”

Step 3: System Integration and Deployment

1. Batch Processing Mode: Automatically run the anomaly detection model weekly or monthly, scanning all employee data and generating a "Risk Employee Ranking" report.

2. Real-Time API Mode: Within the HRMS system, when a manager submits a reimbursement request for approval, the model is invoked in real time to perform a risk score and provide a prompt on the approval screen (e.g., "This request has unusual characteristics; please proceed with caution").

3. Visual Dashboard: Create a management view displaying key risk indicators, trend charts, and detailed alert lists.

Why is this a better solution?

• Privacy and Cost: Core anomaly detection is performed locally, avoiding the frequent sending of sensitive data to an external API.

• Efficiency: Machine learning models process numerical features extremely quickly, making them suitable for large-scale screening.

• Accuracy: AI models can detect complex patterns and multivariate correlations that are imperceptible to the human eye.

• Interpretability: Using DeepSeek APIs provide human-friendly explanations for anomalies generated by "black box" models, closing the loop.

Action Plan Recommendations

1. Start Small: Select a risk scenario to pilot, such as "employee expense fraud detection."

2. Build a Team: This requires collaborative work between data engineers (to process the data), machine learning engineers (to build the model), and HR business experts (to define the rules and interpret the results).

3. Proof of Concept (PoC): Train a simple isolation forest model on historical data to see if it can successfully identify known anomalies.

4. Iterative Optimization: Gradually increase the complexity of the data source and model, ultimately integrating it into the DGX HRMS process.

With this approach, your HRMS system will transform from a system of record into a powerful risk insight and decision support system – the disruptive value of AI.