Slide 13
Slide 13 text
13
Building
predictive
(score) models
• Sophisticated data mining tool with modern
algorithms
• Predictive modeling with use of text and data
mining for use for analytical purposes and build
statistical & policy models (credit, collection,
insurance, churn etc.)
Integration
Administration of
reports, models
flows, users,
dashboards
Data and model
management
Scoring
(calculation)
Deploy score
models
Monitoring
Reporting
• Complete model flows managed by Risk
Management resources (without IT resources)
• Administrate models flows through environments:
Development, Test, acceptance and production
• Real time (1 by 1)
• Near real time (small batches)
• Batch (Big Data)
• Automatic monitoring of processes and critical
parameters at the same time.
•Basel reports, WoE, capital allocation, Pillar II&III
•Scorecard monitoring reports
•Portfolio reporting (delinquency, roll rate etc)
• Webservice/SOAP/
• OLEDB to databases
• Access control (different users different access)
• Version control (roll back, compare)
• Change log
• Automatic model and report documentation
• Manage complete workflows (inc db connections, data
preparation, policy rules, scoring).
• Off-load tasks to the Enterprise server
Scoring
Server
Model
Builder
Monitoring and
alerting Server
Model
Management
Decisioning
Platform
Reporter
Functionality Description Tool Purpose/problem
Credit risk
Fraud detection
Asset & Liability Man.
Compliance
Market Risk
Fraud risk
Customer Acquisition
Customer retention
Role-based security
Operational risk