1. PKDD Discovery Challenge (not only) on Financial Data
Petr Berka
Laboratory for Intelligent Systems
University of Economics, Prague

2. Cups, Challenges, Competitions
KDD Cups (since 1997)
KDD Sisyphus at ECML 1998
PKDD Discovery Challenges (since 1999)
COIL Competition 2000
PAKDD Challenge 2000
PT Challenge 2000, 2001
JSAI KDD Challenge 2001
EUNITE Competition 2001, 2002
. . .
Bold typed – I participated or was involved in organizing
DMLL Workshop, ICML Petr Berka, LISp, 2002

3. PKDD Discovery Challenge Idea
Realistic data mining conditions
collaborative rather then competitive nature
rather vague specification of the problem
Differences to real KDD projects
short time for analysis (2-3 months)
only indirect access to domain and data experts during KDD process
The idea originates from Jan Zytkow who suggested to organize at PKDD99 in Prague a little bit different event than are the KDD Cups.
Ideal contribution should describe the goals of the challenge (in business terms), the method used (preprocessing, data mining) and the achieved results.
2-3 months is enough for building a model to classify a singe table but is too short to understand a complex domain and the data collected.
DMLL Workshop, ICML Petr Berka, LISp, 2002

4. DMLL Workshop, ICML 2002 Petr Berka, LISp, 2002
Challenge Settings
Data and their full description available on the web for all participants
Submissions evaluated by domain experts (but no ordering, no winners and losers)
Workshop at PKDD to present the results and discus them with domain experts
Results and comments of experts available on the web (after the workshop)
Web based access – registering only to keep track of interested people.
DMLL Workshop, ICML Petr Berka, LISp, 2002

5. PKDD Challenges http://lisp.vse.cz/challenge
1999, Prague
financial data, thrombosis data
2000, Lyon
financial data, modified thrombosis data
2001, Freiburg
modified thrombosis data
2002, Helsinki
atherosclerosis data, hepatitis data
I’d like to acknowledge the contribution of Shusaku Tsumoto, who provided the challenge with the medical (thrombosis, hepatitis) data.
DMLL Workshop, ICML Petr Berka, LISp, 2002

6. Financial Challenge Background
Czech bank offering private accounts
Available data for pilot study (29000 clients)
personal characteristics
basic info about accounts
transactions for three months
Proposed tasks
segmentation (defining different types of clients w.r.t. debt)
early detection of debts
DMLL Workshop, ICML Petr Berka, LISp, 2002

7. Financial Challenge Data
DMLL Workshop, ICML Petr Berka, LISp, 2002

8. DMLL Workshop, ICML 2002 Petr Berka, LISp, 2002
Contributions
Method oriented
show a method/system working on the data
Problem oriented (prototype solutions)
loan and/or credit cards description
loan and/or credit cards classification
initial exploration
relation between branches
clients segmentation
DMLL Workshop, ICML Petr Berka, LISp, 2002

9. DMLL Workshop, ICML 2002 Petr Berka, LISp, 2002
Description of loans
Relations between loan category and account characteristics
[Coufal et al, GUHA] [Mikšovský et al, EXCEL]
DMLL Workshop, ICML Petr Berka, LISp, 2002

10. Classification of loans
Detecting risky clients before they are granted a loan
[Mikšovský et al, C5.0]
decision tree to find the relevance of attributes
decision tree for classification (using misclassification costs)
DMLL Workshop, ICML Petr Berka, LISp, 2002

11. Credit Cards Promotion
Description - find characteristics of a card holder
deviation detection
Classification - predict score for „card value“
k-nearest neighbour
[Putten, 1999]
DMLL Workshop, ICML Petr Berka, LISp, 2002

12. DMLL Workshop, ICML 2002 Petr Berka, LISp, 2002
Clients Segmentation
Description - segmentation of clients according to transactions [Hotho, Meadche, 2000]
Kohonen map decision trees
Rule #1 for Cluster 3
If ATTR5 > 9945 and ATTR13 > 0
Then -> Cluster 3 (115, 0.983)
DMLL Workshop, ICML Petr Berka, LISp, 2002

13. Challenge Organizing Lessons
To get and prepare real data is difficult
The time for analyzes should be as long as possible
The response rate was rather low (~ 10%)
No synergy effect observed
DMLL Workshop, ICML Petr Berka, LISp, 2002

14. DMLL Workshop, ICML 2002 Petr Berka, LISp, 2002
DM Lessons (1/4)
Cooperate with experts
domain experts
data experts
. . .
… and with users
DMLL Workshop, ICML Petr Berka, LISp, 2002

15. DM Lessons (2/4) Use knowledge intensive preprocessing methods …
compute age and sex from birth_number
set flags for different types of operations
compute monthly characteristics of transactions (sum, avg, min, max)
lbalance = 1/30 i balance(i)  days(i).
DMLL Workshop, ICML Petr Berka, LISp, 2002

16. DMLL Workshop, ICML 2002 Petr Berka, LISp, 2002
DM Lessons (3/4)
Make the results understandable
[Werner, Fogarty 2001]
DMLL Workshop, ICML Petr Berka, LISp, 2002

17. DMLL Workshop, ICML 2002 Petr Berka, LISp, 2002
DM Lessons (4/4)
Show some (even preliminary) results soon
experts are interested in solutions not in applying sophisticated methods
DMLL Workshop, ICML Petr Berka, LISp, 2002

18. Discovery Challenge Benefits
Experts
deeper insight into the data
Participants
experience with analyzing large real data
motivations for further research
ML/KDD Community
prototype tasks/solutions (like the MiningMart project?)
Organizators
… invitation to DMLL Workshop :-)
DMLL Workshop, ICML Petr Berka, LISp, 2002

19. DMLL Workshop, ICML 2002 Petr Berka, LISp, 2002
Thank You
DMLL Workshop, ICML Petr Berka, LISp, 2002

20. DMLL Workshop, ICML 2002 Petr Berka, LISp, 2002
Contributions
DMLL Workshop, ICML Petr Berka, LISp, 2002