AI Employee Turnover Prediction System Development

Development of an AI System for Employee Turnover Prediction

Predicting voluntary employee resignation is an HR task with direct financial impact. Replacing one employee costs 50-200% of their annual salary (recruiting, onboarding, productivity loss). An AI system identifies employees at high risk 1-3 months before resignation, when retention is still possible.

Ethical and Legal Constraints

Before starting—key constraints:

152-ФЗ / GDPR:

• Explicit employee consent to process personal data for predictive analysis
• Don't use data for automatic HR decisions (only as support)
• Right to explanation and contestation of prediction

Ethical boundaries:

• Don't use personal correspondence data
• Don't apply covert biometric monitoring
• Transparency: employees should know the system exists (not necessarily details)

Without these constraints, the system creates toxic culture and violates law.

Data for the Model

HR system (~ 80% of predictive power):

hr_features = {
    # Career track
    'months_since_last_promotion': months,
    'promotions_count_3y': count,
    'salary_vs_market': salary / market_benchmark,
    'performance_rating_last': rating_1_to_5,
    'performance_trend': rating_last - rating_prev,

    # Engagement
    'training_hours_annual': hours,
    'projects_participated': count,
    'internal_transfers_requested': count,

    # Working conditions
    'average_work_hours_weekly': hours,
    'remote_work_days_weekly': days,
    'manager_tenure': months_with_current_manager,
    'team_size': headcount,

    # Demographics (with caution and fairness audit)
    'tenure_months': total_months_at_company,
    'department': department_encoded
}

Engagement surveys: eNPS (Employee Net Promoter Score), pulse surveys: "Would you recommend this employer?", "Satisfied with manager?", "Do you see career prospects?"

Access control system:

• Abnormally long work hours outside office
• Changes in arrival/departure patterns

With permission and in aggregated form:

• Frequency of HR system use for benefits review (may indicate market comparison)
• Vacation requests: many days at once = possibly taking pause before leaving

Model and Target

Target definition: voluntary resignation within next 90 days.

Imbalance: typically 5-15% turnover per year = 1.5-4% per quarter. SMOTE or class_weight for balancing.

Algorithm: LightGBM with SHAP for explanations. Each employee in high-risk category gets top-3 risk factors—concrete reasons for HR manager.

Segment-level Analysis

Beyond individual scores, analysis by segments:

Cohort analysis: Which hire cohorts turn over faster? If cohort 2022-Q3 has 2× attrition—what happened at their hire/onboarding?

Department risk radar: Departments with systematically high resignation risk → systemic problem (poor management, uncompetitive pay, boring tasks).

Manager effectiveness: Employees under specific manager resign 3× more than average → HR flag.

Retention Actions

Action Matrix:

Effectiveness measured through A/B: Randomize retention interventions across high-risk group, measure retention rate vs. control.

HR Dashboard

• Workforce risk heat map by department
• Top-10 individual risks with factors
• Trend: how overall company resignation risk changes
• Forecast: expected number of resignations in next 90 days (for recruiting planning)

HRIS integration: SAP SuccessFactors, Workday, 1C:HRM—API for HR data retrieval and risk score recording.

Timeline: basic model on HR data from HRIS—4-5 weeks. Full system with SHAP explanations, dashboard and HRIS integration—3-4 months.