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http://www.datamine.gr
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
http://www.datamine.gr
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
http://www.datamine.gr
System Objectives
Automate & optimize credit checking – contract activation processMinimize & control bad deptUnderstand bad payment behavior - Identify systematic behaviors-patterns, Fraudulent casesMeasure Risk – provide quantitative figures at customer levelProvide business flexibility: support unlimited, user-defined rulesIntegrate 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 customeranalytics database or Marketing DatabaseProvide advanced reporting capabilities for monitoring & performance evaluationIncorporate predictive modeling
http://www.datamine.gr
Credit checking: a typical case
Complex, time consuming process, manual processingSubjective 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
EXTERNALCREDIT
AGENCIES
COMMON ‘MARKET
BLACKLISTS’
INTERNAL BLACKLIST
PENDING –SUSPICIUSREQUESTS
EXISTING CUSTOMERS ALL CUSTOMERS
application
raw data - transactionalCredit 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 flexibilityInsufficient process monitoring & reporting
http://www.datamine.gr
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
LogicBusiness rules
request
Credit Checking Overview
Provide an overall, objective picture of the physical customerWell-defined, flexible business logic from user to systemRelease Human resources – minimize manual interventionIntegrate other processes or systems (such as Fraud, or CRM related)Minimization of external credit agencies cost
Automated check: Minimization of activation timeIdentification of Fraud patternsAdvanced monitoring & reporting (OLAP) along with Customer Risk AnalysisUse Credit Risk in Segmentation schemes - Loyalty – Campaign management processes
http://www.datamine.gr
Technology - System Architecture
BILLING
ACTIVATION
PAYMENT
FRAUD
Customer Viewer
LEGACY SYSTEMS
Credit Scoring System
synchprocesses
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 externalCustomer Viewer: web based client that enables customer managementReport manager: Windows based sub-system exposing OLAP functionalityCOM libraries: Set of components that provide customer model & statistical dataOn-Line component: A centralized component answering ‘user’ requestsSynch 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)
http://www.datamine.gr
Customer ModelThe structure is based on the Physical Customer (physical or legal entity)Provides Overall & detailed (Partial) picture of the customerUses 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
Par
tial S
core
Par
tial S
core
http://www.datamine.gr
Scoring ModelCredit 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 baseAllow using ‘payment behavior’ as criterion in complex selection or assessment processesAllows development global segmentation scheme based on payment behavior
Provides a framework for monitoring customer base & Activation process versus time
0%
4%
8%
12%
(%)
Perc
ent
Customer base distribution based on Credit Score
HIGH RISKLOW RISK
CREDIT RISK
NORMAL
http://www.datamine.gr
Credit Scoring: Properties
Expressed as percent (%) - easy to interpretIt 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’ behaviorIt is configurable, receives several parameters (weights for reasons, ‘memory factor’ or event reasons)
Billing: monthly bill invoicesPayment detailed payment data (optional)Events on customer behavior (ticklers) communication with customer care along with reason and resultsAccount 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
http://www.datamine.gr
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 – UDPStatistically Derived based on a statistical\data 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.
http://www.datamine.gr
Business Logic: Policies & Rules
Customer type (legal or physical)Profession categoryAge ClassNationality/AreaFiscal Code – data integrityCredit ScoreRecent request HistoryPending RequestsCurrent request (Service or Product)Balance\Source analysisBilling history (averages, variation)Amount Paid Number of contracts- lines per statusOutlier detection flagTraffic patternsBlacklist FlagAutomatic Fraud Alert
Input VariablesActivate - unconditionalActivate – conditional, request depositSend for additional manual checkSend for external Credit EvaluationReject 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
http://www.datamine.gr
Reporting Capabilities
OLAP functionality with key dimensions:
UserPolicyRuleTimeDecision (Action)Tariff modelCurrent Status
Predefined ReportingA set of named, parameterized reports that answer specific business needs along with search and grouping functionality for effective management.Dynamic ReportingThe 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 hierarchiesDrill through functionality, to generate lists of casesDirect export to MS Excel, PDF
http://www.datamine.gr
Overall ArchitectureDATA PROVIDERS
BILLING SYSTEMPhysical Customer Entity,
Customer profile, account and contract
data, Products & Services, Transaction
History (Billing, Payment, Activation
Requests), Tariff Models
CUSTOMER CARE-CRMContact History(CRM \ Loyalty
activities \ programs, Complaints-CTI)
MARKETING DATAProduct & Services
Promotions, Campaigns, Surveys,
marketing Studies
EXTERNAL DATAExternal Credit bureau
data, external databases
TRAFFIC DATACDR raw data (In-out),
Network structureQoS 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 dataPayment behaviorSegmentation dataUtilization 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 PROCESSDeposit Scheme
UPGRADE PROCESSCustomer eligibility
LOYALTY SYSTEMLoyalty schemes
CAMPAIGNEligibility Check
CALL CENTERCustomer profile
BUSINESS PROCESSES
On-Line component
http://www.datamine.gr
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
synchprocesses
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
http://www.datamine.gr
22 Ethnikis Antistasis Avenue,15232 Chalandri, Athens, Greece
[email protected]@datamine.gr
http://www.datamine.gr
December 2003