CONSUMER CREDIT RISK ASSESMENT, PREDICTION & MANAGEMENT SYSTEM

1. CONSUMER CREDIT RISK
ASSESMENT, PREDICTION & MANAGEMENT SYSTEM

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Contents
System Overview
System Objectives
Credit checking: a typical case
Automated Credit checking: Overview
Technology - System Architecture
Customer Model
Scoring Model
Credit Scoring: Properties - Key Input
Business Logic: Policies & Rules
Customer Viewer – Sample screen
Reporting capabilities

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A specialized Information System that combines cutting edge technologies, statistical models and business
knowledge in order to optimize and automate the manual credit checking within contract activation process.
Key characteristics of the system are:
System Overview
Single point of reference: a centralized system with up-to-date
Information regarding customer evaluation, usage & payment
Overall customer picture: physical customer metadata, statistical
scores, descriptive statistics, billing & payment history, product &
services information organized in a single enterprise-wide application
(‘Customer Viewer’ – web interface).
Business Logic Interface: a subsystem that allows policy and rule
management. Supports unlimited policies with unlimited rules over a
large set of parameters that ensure maximum flexibility in designing or
implementing business logic.
Performance Measurement infrastructure: advanced reporting and
analysis capabilities allow policy & rule performance evaluation and
optimization.
Business Intelligence infrastructure: a general, extensible data mart
that can be enriched with scores, traffic patterns thus enhanced into a
‘Marketing Database’
decision
decision
POS - USER
CUSTOMER
BILLING ACTIVATION
PAYMENT FRAUD
Raw data
request
Credit Checking Process
CREDIT
CHECKING LOGIC
USER DEFINED RULES
INTERFACES
SYNC PROCESSES
STATISTICAL MODELS
CREDIT CHECKING
SYSTEM
request

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System Objectives
Automate & optimize credit checking – contract activation process
Minimize & control bad dept
Understand bad payment behavior - Identify systematic behaviors-patterns, Fraudulent cases
Measure Risk – provide quantitative figures at customer level
Provide business flexibility: support unlimited, user-defined rules
Integrate related processes (external credit check, deposit schemes etc)
Exchange credit risk information with other companies, without exposing sensitive customer data
(within the same group of companies)
Design a robust, extensible database with enhanced data quality, ready to be extended a customer
analytics database or Marketing Database
Provide advanced reporting capabilities for monitoring & performance evaluation
Incorporate predictive modeling

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Credit checking: a typical case
Complex, time consuming process, manual processing
Subjective decision, depends heavily on the user (rep)
Non optimized (loss of customers with strict rules, bad dept generation with flex rules)
Incomplete or misleading data, not a complete, objective picture of the customer (treats the line instead
of the customer)
Activation Policies,
Business Rules,
Deposit schemes,
Fraud Cases
POS
USER
CUSTOMER
WORKFLOW
BALANCE
- STATUS
RECENT
REQUESTS
FRAUD
STATUS
BILLING
HISTORY
PAYMENT
BEHAVIOR
EXTERNAL
CREDIT
AGENCIES
COMMON
‘MARKET
BLACKLISTS’
INTERNAL
BLACKLIST
PENDING –
SUSPICIUS
REQUESTS
EXISTING CUSTOMERS ALL CUSTOMERS
application
raw data - transactional
Credit Risk raw data
decision
BILLING
ACTIVATION
PAYMENT
FRAUD
LEGACY SYSTEMS
Credit Checking Procedure
Increased cost (due to external credit Agencies)
Increased Fraud/ Bad dept cases - Limited business flexibility
Insufficient process monitoring & reporting

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Automated Credit checking: Overview
application
decision
POS
USER
CUSTOMER
CREDIT
CHECKING
SYSTEM
COMPLEX CREDIT
CHECKING LOGIC BILLING
ACTIVATION
PAYMENT
FRAUD
LEGACY SYSTEMS
decision
Raw
data
Logic
Business rules
request
Credit Checking Overview
Provide an overall, objective picture of the physical customer
Well-defined, flexible business logic from user to system
Release Human resources – minimize manual intervention
Integrate other processes or systems (such as Fraud, or CRM related)
Minimization of external credit agencies cost
Automated check: Minimization of activation time
Identification of Fraud patterns
Advanced monitoring & reporting (OLAP) along with Customer Risk Analysis
Use Credit Risk in Segmentation schemes - Loyalty – Campaign management processes

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Technology - System Architecture
BILLING
ACTIVATION
PAYMENT
FRAUD
Customer
Viewer
LEGACY SYSTEMS
Credit Scoring System
synch
processes
analytics
database
scoring
models
on-line
component
COM
libraries
Report
Manager
Rule
Manager &
Sys Admin
SERVER SIDEMIDDLEWARECLIENT SIDE
Cutting edge technology: .NET, 3-tier architecture, XML interfaces, Database server independent, web based
client, smart clients. Scalable, modular approach, Open Architecture (API)
Scoring Model: Core component that implements statistical models, built-in and/or external
Customer Viewer: web based client that enables customer management
Report manager: Windows based sub-system exposing OLAP functionality
COM libraries: Set of components that provide customer model & statistical data
On-Line component: A centralized component answering ‘user’ requests
Synch processes: The processes that enforce synchronization with production systems & data cleansing rules
Rule Manager: Windows based sub-system allowing policy & rule management (insert, modify, release, suspend)

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Customer Model
The structure is based on the Physical Customer (physical or legal entity)
Provides Overall & detailed (Partial) picture of the customer
Uses a Weight Factor model that ‘understands’ the importance of each account – in the context of the
physical customer- and readjusts the overall score and other stats
Physical Customer
Account #1
Contract – Line #1
Contract – Line #n
Billing, Payment
Tariff, Services
Tariff, Services
Demographics, customer
history, ratings, memberships
Account #n
Contract – Line #1
Contract – Line #n
Billing, Payment
Tariff, Services
Tariff, Services Scoring
Engine
Score, statistics,
weight factor
Score, statistics,
weight factor
Weight
Model
Overall credit score
PartialScore
PartialScore

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Scoring Model
Credit Scoring models are build on historical-transactional data, incorporating customer payment
behavior into a single number.
Can be used for comparative analysis of the customer base
Allow using ‘payment behavior’ as criterion in complex selection or assessment processes
Allows development global segmentation scheme based on payment behavior
Provides a framework for monitoring customer base & Activation process versus time
0%
4%
8%
12%
(%)Percent
Customer base distribution based on Credit Score
HIGH RISKLOW RISK
CREDIT RISK
NORMAL

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Credit Scoring: Properties
Expressed as percent (%) - easy to interpret
It is ‘event based’ which means that it can be extended to ‘near real time’
It is weighted,assigns different importance to different accounts of the same customer, or different events of
the same account.
It is dynamic, the system tends to ‘forget’ previous ‘bad’ history data given a recent ‘good’ behavior
It is configurable, receives several parameters (weights for reasons, ‘memory factor’ or event reasons)
Billing: monthly bill invoices
Payment detailed payment data (optional)
Events on customer behavior (ticklers) communication with customer care
along with reason and results
Account Event History: Suspension, Reactivation, Cancellation
Service activation & usage History: which services, for how long and with what usage.
Account life Statistics: tenure, special properties (tariff model, services)
Customer level Statistics: geo-demographics, socioeconomic, total billing & payment figures, typologies
Credit Scoring: Key Input

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Business Logic: Policies & Rules
A ‘policy’ is defined as a set of complex business rules that enable different treatment of
(potential) customers based on several characteristics. Policies can be
User Defined, based on business logic over a set of predefined parameters – UDP
Statistically Derived based on a statisticaldata mining models - SDP
UDP are easy to be implemented through the graphical user interface of the system (Policy Manager). Apply mostly
to existing customers (having at least a basic history within the company).
UDR, are arbitrarily developed based mostly on user experience, perception and business understanding
SDP can be used for new customers with no further (internal) information. SDP are usually Decision trees rule sets.
UDR & SDR can work in parallel with a complementary logic.
Policies & rules can be defined as conditional based of time period or flow management requests. For example
during a peak-period (e.g. Christmas) with massive promotions the system automatically activates a different set of
rules.

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Business Logic: Policies & Rules
Customer type (legal or physical)
Profession category
Age Class
Nationality/Area
Fiscal Code – data integrity
Credit Score
Recent request History
Pending Requests
Current request (Service or Product)
BalanceSource analysis
Billing history (averages, variation)
Amount Paid
Number of contracts- lines per status
Outlier detection flag
Traffic patterns
Blacklist Flag
Automatic Fraud Alert
Input Variables
Activate - unconditional
Activate – conditional, request deposit
Send for additional manual check
Send for external Credit Evaluation
Reject Application
Actions + Reasoning
If Score is greater than [70%]
and (customer (physical) has balance and average
invoice is between 100 and 200 Euro,
Or customer (physical) has an balance greater than
1000 Euro, and balance is delayed)
and requested service is (professional or corporate)
and customer seniority is less than 1 year
.…………………………………………………………….
……………………………………………………………….
……………………………………………………………….
then hold the application for extra internal credit checking and
request additional data from external credit agencies

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Reporting Capabilities
OLAP functionality with key dimensions:
User
Policy
Rule
Time
Decision (Action)
Tariff model
Current Status
Predefined Reporting
A set of named, parameterized reports that answer specific business needs along with search and grouping
functionality for effective management.
Dynamic Reporting
The dynamic reporting module will be based on advanced report generation modules that will allow the
authorized users to combine available dimensions, measures and filters in order to build specialized reports.
Drill down functionality, to support hierarchies
Drill through functionality, to generate lists of cases
Direct export to MS Excel, PDF

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Overall Architecture
DATA PROVIDERS
BILLING SYSTEM
Physical Customer Entity,
Customer profile,
account and contract
data, Products & Services,
Transaction
History (Billing,
Payment, Activation
Requests),
Tariff Models
CUSTOMER CARE-CRM
Contact History
(CRM Loyalty
activities programs,
Complaints-CTI)
MARKETING DATA
Product & Services
Promotions,
Campaigns, Surveys,
marketing Studies
EXTERNAL DATA
External Credit bureau
data, external databases
TRAFFIC DATA
CDR raw data (In-out),
Network structure
QoS data
ERP, ACTIVATION,
PROVISIONING
Products, Dealer,
Application data
OLAP & Reporting system
FRONT-END APPLICATIONS
Operational CRM-CC system
Customer Base segmentation
Campaign Management
Customer Viewer
Customer base KPIs monitor
POS network analyzer
Physical
Customer Data,
Account & Contact,
Customer Scores
Billing data
Payment behavior
Segmentation data
Utilization profile &
Traffic patterns
ETL
processes
DATA WAREHOUSE
CRM datamart
Sales datamart
Network datamart
MKT data mart
Statistical
Models
Customer
Intelligence
Database
Traffic
Processing
XML
interface
Policy Manager
Reporting Module
Customer Viewer
Sync
Processes
Customer KPI viewer
Segmentation System
CUSTOMER ANALYTICS
ACTIVATION PROCESS
Deposit Scheme
UPGRADE PROCESS
Customer eligibility
LOYALTY SYSTEM
Loyalty schemes
CAMPAIGN
Eligibility Check
CALL CENTER
Customer profile
BUSINESS PROCESSES
On-Line
component

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Customer analytics – the complete picture
A single component with business logic for every customer assessment-related
function of the enterprise. Typical applications include:
Activation Process: Is the customer eligible for a new line or service? Under what condition?
Loyalty – Point Scheme: Is the customer eligible for point redemption?
Terminal Upgrade: Is the customer eligible for terminal upgrade? What’s the exact offer based on
commercial policy?
Campaign management: Eligibility for campaign-specific offer?
Segmentation Schemes (Micro & Micro): What is the segment for a specific customer?
Customer
Viewer
Credit Scoring System
synch
processes
analytics
database
scoring
models
on-line
component
COM
libraries
Report
Manager
Rule
Manager &
Sys Admin
SERVER SIDEMIDDLEWARECLIENT SIDE Churn Prediction
Loyalty System
Terminal Upgrade
Segmentation &
Campaign
management
Customer Base KPI
Monitoring
Customer analytics – The marketing database

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22 Ethnikis Antistasis Avenue,
15232 Chalandri,
Athens, Greece
George.Krasadakis@datamine.gr
info@datamine.gr
http://www.datamine.gr
December 2003