2. • Established in November, 2007
• 100+ employees
• Customers in Nordics, Latvia, Russia and
the USA
• Provide systems integration services
(CRM, Decision Support Systems)
• Develops original products
• (Micromiles, Debessmana)
Who we are
3. • Defining needs
• Collecting data
• Generating and evaluating options
• Selecting the best possible
• Applying and using
• Getting feedback and following up
Decisions Making Process Is …
4. Data Mining is
• the computational process of discovering
patterns in large data sets
• Knowledge Discovery in Databases
What is Data Mining?
6. Cross Industry Standard Process for Data Mining (CRISP)
Business Understanding
• Business Objectives
• Success Criteria
• Project plan
• Deliveries
Data Understanding
• Initial Data Collection
• Data Description
• Data Exploration
Data Preparation
• Data cleaning
• Sampling
• Normalization
• Feature Selection
Modeling
• Select modeling techniques
• Build/train model
• Prediction
Evaluation
• Model validation
• Review results
• Success criteria evaluation
Deployment
• Results visualization
• Report creation
7. Business Understanding
Fraud detection analysis for insurance claims
(car insurance)
Business Objectives
The goal of this analysis is to create a tool which helps to
identify fraudulent claims in auto insurance (KASKO)
Deliveries
• Possible fraud prediction
• Descriptive analysis
14. • Automated data
preprocessing (normalizing,
cleaning)
• Workflow type modeling
• Build several models in
parallel
Modeling
Classification modeling using Oracle Data Miner
15. Models comparison and validation (confusion matrix)
Classification modeling evaluation
Models Actual values Predicted Values
Accuracy
Value Y N
SVM
Y 66% 34%
69%
N 29% 71%
DT
Y 66% 34%
66%
N 33% 67%
GLM
Y 70% 30%
70%
N 30% 70%
Where
Y – Fraud cases
N – Normal cases
