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Data Mining for CRM and Risk Managementkdlabs AGwww.kdlabs.comDr. Jörg-Uwe Kietz
© January 2003, kd labs ag, analytical CRM and data mining
Overview
Knowledge Discovery @ kdlabs Knowledge Discovery Services (KDS) for CRM
Some cases:– Credit Risk Scoring (to get the good customer)– Customer Life-cycle Analysis for Cross-Selling (to earn more from them)– Customer & Employee Satisfaction (to keep them)
Knowledge Discovery Applications (KDA)– Detecting Money Laundering Activities
Summary
© January 2003, kd labs ag, analytical CRM and data mining
About kdlabs
kdlabs AG was founded in July 2000 to deliver services and to develop applications in the area of Knowledge Discovery Services (KD) and Knowledge Discovery Application (KDA).
kdlabs core competence is KD and KDA. In addition, kdlabs staff has extensive experience in complementary fields, such as Marketing and Marketing Research, CRM and e-CRM, Data Warehousing and Application Integration.
While kdlabs is vendor-independent, it is part of a strong partner network when it comes to the implementation of complete KDA- and CRM-solutions.
© January 2003, kd labs ag, analytical CRM and data mining
incr
ease
pro
fitab
ility
optim
ise
risk
Marketing & CRM Applications• customer acquisition• cross- and up-selling• churn prediction & retention• customer satisfaction modelling• employee satisfaction modelling
Website Applications• website behaviour analysis• website development• dynamic personalisation
Credit Risk Applications• credit risk scoring• credit risk monitoring
Fraud Detection Applications• fraud detection• money laundering detection
Basic Applications(e.g. data quality assessment, profitability analysis, customer segmentation)
Focus on application fields
© January 2003, kd labs ag, analytical CRM and data mining
Overview
Knowledge Discovery @ kdlabs Knowledge Discovery Services (KDS) for CRM
Some cases:– Credit Risk Scoring (to get the good customer)– Customer Life-cycle Analysis for Cross-Selling (to earn more from them)– Customer & Employee Satisfaction (to keep them)
Knowledge Discovery Applications (KDA)– Detecting Money Laundering Activities
Summary
© January 2003, kd labs ag, analytical CRM and data mining
Evolution of customer relation over time
Val
ue o
f cus
tom
er re
latio
n
Three simple business goals of CRM
CustomerAcquisition
Acquire the „right“ customers with high potential
valueCustomer
Development
Cross- and up-sell by offering
the right products at the right time
CustomerRetention
Retain profitable customers and increase their
long-term value
KD for CRM
© January 2003, kd labs ag, analytical CRM and data mining
The CRM Project
Return
Inve
stm
ents „Big Bang“
Need for a managed evolution
„Flop“
„No Go“
© January 2003, kd labs ag, analytical CRM and data mining
Overview
Knowledge Discovery @ kdlabs Knowledge Discovery Services (KDS) for CRM
Some cases:– Credit Risk Scoring (to get the good customer)– Customer Life-cycle Analysis for Cross-Selling (to earn more from them)– Customer & Employee Satisfaction (to keep them)
Knowledge Discovery Applications (KDA)– Detecting Money Laundering Activities
Summary
© January 2003, kd labs ag, analytical CRM and data mining
Credit-Risk Scoring
Not only banks have a credit-risk, everyone who accepts to deliver services or goods before he receives payment has one.
Models predicting credit-risk can be build from previous cases. These models have to be applied on-line at the point-of-sales They can be build off-line, but should be rebuild periodically kdlabs build a predictive model for an advertisement supplier
predicting non-payers with accuracy of 79% on the test set This model is integrated into the customers point-of-sales
application.
© January 2003, kd labs ag, analytical CRM and data mining
How does it work
Customer,Customer history,Background data
AnalysisData
Offline Credit-Risk Modeling
Online Credit-Risk Prediction
CurrentTransaction
Data
Mining
Data P
reprocesssing ScoredTransaction
EnrichedTransaction
© January 2003, kd labs ag, analytical CRM and data mining
Point-of-Sales credit-risk-scoring
The model acquired by KD is used to provide immediate feedback into the sales process
© January 2003, kd labs ag, analytical CRM and data mining
Overview
Knowledge Discovery @ kdlabs Knowledge Discovery Services (KDS) for CRM
Some cases:– Credit Risk Scoring (to get the good customer)– Customer Life-cycle Analysis for Cross-Selling (to earn more from them)– Customer & Employee Satisfaction (to keep them)
Knowledge Discovery Applications (KDA)– Detecting Money Laundering Activities
Summary
© January 2003, kd labs ag, analytical CRM and data mining
Customer Life-cycle Analysis
The Business Problem:– Life insurances are profitable products of insurance-companies– Only a small percentage of all customers own one Cross-sell life-insurances to customers
The Approach:– Transform the “state-description”
Customer C (a ….) owns product P (with …) since X– into a life-cycle description
A Customer C started with P1 at X1, X2 years later she bought P2, …– “Data Mine” typical states of customers when they bought a life-insurance– Rate customers based on their similarity with such prototypical states– Advertise a life insurance to the N highest rated customers
Resulted in one of the most successful campaigns
© January 2003, kd labs ag, analytical CRM and data mining
Buying Age Distribution of Insurances
0%
5%
10%
15%
20%
25%
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100
Age
Cus
tom
ers
Car
Home
Life
© January 2003, kd labs ag, analytical CRM and data mining
Overview
Knowledge Discovery @ kdlabs Knowledge Discovery Services (KDS) for CRM
Some cases:– Credit Risk Scoring (to get the good customer)– Customer Life-cycle Analysis for Cross-Selling (to earn more from them)– Customer & Employee Satisfaction (to keep them)
Knowledge Discovery Applications (KDA)– Detecting Money Laundering Activities
Summary
© January 2003, kd labs ag, analytical CRM and data mining
Customer & Employee Satisfaction
Business Goals• Employee and customer satisfaction are prerequisites for loyal
behavior and customer relation, and by that influence profitability.• Only detailed knowledge about the influence-factors of customer and
employee satisfaction enable effective actions to increase satisfaction, loyalty and therefore profitability.
Therefore:• New Management Methods like Total Quality Management (TQM) or
Balanced Scorecard (BSC) contain the assessment of customer and employee satisfaction.
© January 2003, kd labs ag, analytical CRM and data mining
Customer & Employee Satisfaction
Data Preparation• clean Values• outlier detection• missing values• ...
Causal modeling• factor analysis• business needs
Data Completion
Impact Analysis• Linear Regression• LISREL• PLS• ...
Segmentation• by region• by business process• by division• ...
Result Presentation• Report• Workshop•
The Knowledge Discovery Process Before KD starts
© January 2003, kd labs ag, analytical CRM and data mining
kdimpact Portfolio
© January 2003, kd labs ag, analytical CRM and data mining
kdimpact graph and charts
© January 2003, kd labs ag, analytical CRM and data mining
Overview
Knowledge Discovery @ kdlabs Knowledge Discovery Services (KDS) for CRM
Some cases:– Credit Risk Scoring (to get the good customer)– Customer Life-cycle Analysis for Cross-Selling (to earn more from them)– Customer & Employee Satisfaction (to keep them)
Knowledge Discovery Applications (KDA)– Detecting Money Laundering Activities
Summary
© January 2003, kd labs ag, analytical CRM and data mining
Detecting Money Laundering Activities
The Business Problem Size of worldwide money laundering per year US$ 590-1‘500 billion Over 95% of delinquency sum still undiscovered Criminal potential obvious since September 11, 2001; top-priority for
countering the financing of terrorism Significant damage of reputation and high fines for involved financial
institutions and managers FATF (financial action task force) demands for stronger regulations in
affiliated countries Governments strengthen anti-money laundering laws and regulations Effective Money Laundering detection by bank‘s helps to protect the
secrecy of banking
© January 2003, kd labs ag, analytical CRM and data mining
Examples of what has to be detected transactions from/to uncooperative countries or exposed persons unusual high cash deposits high level of activity on accounts that are generally little used withdrawal of assets shortly after they were credited to the account many payments from different persons to one account repeated credits just under the limit fast flow of a high volume of money through an account
and many more ... e.g. have a look at: – FIU‘s in action: 100 cases from the Egmont Group– Yearly report of the Swiss MROS
Detecting Money Laundering Activities
© January 2003, kd labs ag, analytical CRM and data mining
Data analysis
1 2patterns
3
Sel
f-his
tory
Pee
r gro
ups
Link
Ana
lysi
s
rules
expe
rts,
regu
latio
ns
names
Bla
cklis
ts,
PE
P‘s
, etc
.
Overview
bank´stransactions& customers
data
!Alertdelivery
Admin Client
User Interfaces
WorkflowClient
datarepository
externaldata
© January 2003, kd labs ag, analytical CRM and data mining
bank´stransactions& customers
data
Data analysis
1 2namesrules patterns
3
time
serie
s
outli
ers
etc.
Blac
klis
ts,
PEP
‘s, e
tc.
expe
rts,
regu
latio
ns
!Alertdelivery
Admin Client
User Interfaces
WorkflowClient
datarepository
externaldata
Data collection and data integration
data retrieval from any operational sources and transformation into consistent database
basic data sources: customer profile (CIF and related data), customer view (linked CIF’s), accounts, transactions and many other behavior oriented data
automatic and robust loading process with supportive error correction business-related description of data structure for the ease of use
simultaneously in other parts of the system statistical and quality measures for focused tuning of all technical
processes
© January 2003, kd labs ag, analytical CRM and data mining
bank´stransactions& customers
data
Data analysis
1 2namesrules patterns
3
time
serie
s
outli
ers
etc.
Blac
klis
ts,
PEP
‘s, e
tc.
expe
rts,
regu
latio
ns
!Alertdelivery
Admin Client
User Interfaces
WorkflowClient
datarepository
externaldata
Data analysis: three core detection techniques
Data analysis
21patterns
3names
Bla
cklis
ts,
PE
P‘s
, etc
.rules
expe
rts,
regu
latio
ns
Sel
f-his
tory
Pee
r gro
ups
Link
Ana
lysi
s
specific rulesand thresholdslaw, regulations,domain expertise
TvT Complianceinternal experts
unusual patternsand profileshistorical comparison,peer comparison, linkanalysis, etc.
suspicious namesand actorsprimary sources
specialized tools
OFAC
internal lists
Eurospider
Logica
Factiva
World-Check
Etc.
© January 2003, kd labs ag, analytical CRM and data mining
bank´stransactions& customers
data
Data analysis
1 2namesrules patterns
3
time
serie
s
outli
ers
etc.
Blac
klis
ts,
PEP
‘s, e
tc.
expe
rts,
regu
latio
ns
!Alertdelivery
Admin Client
User Interfaces
WorkflowClient
datarepository
externaldata
Data analysis: detecting unusual patterns / profiles
Pattern discovery 1: self history• e.g. unusual activity in an account history based on
multidimensional time series analysis and comparison time series analysis and comparison
Pattern discovery 2: peer groups• e.g. unusual behavior compared to peer group based on
natural clusters and/or pre-defined segments clustering, segmentation and outlier detection
Pattern discovery 3: link analysis• e.g. similarities in different accounts based on connected/linked
transactions that are not otherwise expected to occur Pattern detection and matching
© January 2003, kd labs ag, analytical CRM and data mining
Overview
Knowledge Discovery @ kdlabs Knowledge Discovery Services (KDS) for CRM
Some cases:– Credit Risk Scoring (to get the good customer)– Customer Life-cycle Analysis for Cross-Selling (to earn more from them)– Customer & Employee Satisfaction (to keep them)
Knowledge Discovery Applications (KDA)– Detecting Money Laundering Activities
Summary
© January 2003, kd labs ag, analytical CRM and data mining
Summary
Knowledge Discovery Services – need a clear business value– data are available everywhere– adequate data preparation is the key success factor– think big, start small search for the most valuable and realistic sub-problems to solve
Knowledge Discovery Applications – enable application, that are not possible without KD– The applications provide a fixed context that
enables the automatisation of the KD-process