AI

AX Partner: Bridging Business and Technology

  • AI

  • AI Development

AI Development

Turning strategy into action, ideas into systems
We accurately understand business problems, analyze data characteristics, and design and build optimal machine learning models.
MLOps Pipeline
MLOps 파이프라인

Key Services

Category
Predictive Model
Recommendation Model
Anomaly Detection Model
Classification Model
Definition
A regression model that predicts future values based on historical data
A model that recommends the most relevant products and content based on user behavior data
A model that detects abnormal data that deviates from normal patterns
A model that classifies data into predefined categories
Key Features
  • Continuous numerical prediction
  • Time series analysis
  • Multivariate analysis support
  • Personalized recommendations
  • Collaborative filtering
  • Content-based filtering
  • No labeled data required
  • Real-time detection
  • Low false positive rate
  • Binary/multiclass classification
  • Probability scores
  • Imbalance data handling
How It Works
  • 1. Train on historical data
  • 2. Identify relationships between variables
  • 3. Derive a regression equation
  • 4. Predicting Future Values
  • 1. Collecting User Behavior Data
  • 2. Calculating Similarity
  • 3. Generating Recommendation Candidates
  • 4. Optimizing Rankings
  • 1. Learning Normal Patterns
  • 2. Setting Anomaly Thresholds
  • 3. Comparing Real-Time Data
  • 4. Determining Anomalies
  • 1. Training on Labeled Data
  • 2. Setting Decision Boundaries
  • 3. Classifying New Data
  • 4. Presenting Results with Confidence Levels
Use Cases
  • Insurance Claim Forecasting
  • Loan Delinquency Rate Forecasting
  • Stock Price and Exchange Rate Forecasting
  • Demand Forecasting (Inventory Management)
  • Insurance Product Recommendations
  • Loan Product Proposals
  • Investment Portfolio Recommendations
  • Cross-Selling Targeting
  • Insurance Fraud Detection
  • Anomaly Detection in Financial Transactions
  • Predictive Maintenance for Equipment Failures
  • Network Intrusion Detection
  • Credit Rating Classification
  • Insurance Underwriting Approval/Rejection
  • Disease Diagnosis (Positive/Negative)
  • Customer Churn Prediction
Key Benefits
  • Improved decision-making accuracy
  • Proactive risk prediction
  • Optimized resource allocation
  • 20–30% increase in conversion rates
  • Higher customer satisfaction
  • Increased cross-sell revenue
  • Proactive loss prevention
  • 70% improvement in audit efficiency
  • Enhanced compliance
  • Automation rate of over 80%
  • 90% reduction in review time
  • Consistent decision-making criteria
Data Requirements
Continuous numerical data covering at least 6 to 12 months
User behavior logs and interaction data
Primarily normal data with a small amount of abnormal data
A balanced distribution of labeled training data across classes is required.