PKDD Discovery Challenge (not only) on Financial Data

1
PKDD Discovery Challenge (not only) on Financial
Data

• Petr Berka
• Laboratory for Intelligent Systems
• University of Economics, Prague
• berka_at_vse.cz

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
• . . .

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

4
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)

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

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

7
Financial Challenge Data
8
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

9
Description of loans

• Relations between loan category and account
  characteristics
• Coufal et al, 1999 - GUHA
  Mikšovský et al, 1999 - EXCEL

10
Classification of loans

• Detecting risky clients before they are granted a
  loan
• Mikšovský et al, 1999 - C5.0
• decision tree to find the relevance of attributes
• decision tree for classification (using
  misclassification costs)

11
Credit Cards Promotion

• Description - find characteristics of a card
  holder
• deviation detection
• Classification - predict score for card value
• k-nearest neighbour
• Putten, 1999

12
Clients Segmentation

• Description - segmentation of clients according
  to transactions Hotho, Meadche,
  2000
• Kohonen map decision trees

• Rule 1 for Cluster 3
• If ATTR5 gt 9945 and ATTR13 gt 0
• Then -gt Cluster 3 (115, 0.983)

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

14
DM Lessons (1/4)

• Cooperate with experts
• domain experts
• data experts
• . . .
• and with users

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).

16
DM Lessons (3/4)

• Make the results understandable
• Werner, Fogarty 2001

17
DM Lessons (4/4)

• Show some (even preliminary) results soon
• experts are interested in solutions not in
  applying sophisticated methods

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 -)

19
Thank You
20
Contributions