This presentation made on University of Leuven on my research on credit risk analyis

1. Credit Risk with AI tools
The old, the new and the unexpected
ARMANDO VIEIRA
Armandosvieira.wordpress.com

2. Customer fails
to pay
Losing money
Wrong Strategy
Change in
market
prices
Processing failures and
frauds
Regulatory compliance
Customer fails
to pay
Losing money
Wrong Strategy
Change in
market
prices
Processing failures and
frauds
Regulatory compliance
RISK

3. Importance of Credit Risk

4. A statistical means of providing a quantifiable risk factor for a given
applicant.
Credit scoring is a process whereby information provided is converted into
numbers to arrive at a score.
The objective is to forecast future performance from past behavior of
clients (SME or individuals).
Credit scoring are used in many areas of industries:
Banking
Decision Models Finance
Insurance
Retail
Telecommunications
What is Credit Scoring?

6. • Predict financial distress of private companies one year ahead
based on account balance sheet from previous years.
• Enventualy the probability to become so.
• Obtain reliable data from up to 5 previous years before failure
• Classify and release warning signs
Bankruptcy prediction problem

7. The curse of dimensionality
Problems
• Sparness of the search space
• Presence of Irrelevant Features
• Poor generalization of Learning Machine
• Exceptions difficult to identify
Solutions
• Dimensionality reduction: feature selection
• Constrain the complexity of the Learning Machine

8. The Diane Database
• Financial statements of French companies, initially of 60,000
industrial French companies, for the years of 2002 to 2006,
with at least 10 employees
• 3,000 were declared bankrupted in 2007 or presented a
• restructuring plan 30 financial ratios which allow the
description of firms in terms of the financial strength,
liquidity, solvability, productivity of labor and capital, margins,
net profitability and return on investment

9. The inputs
Number of employees Net Current Assets/Turnover (days)
Financial Debt / Capital Employed (%) Working Capital Needs / Turnover (%)
Capital Employed / Fixed Assets Export (%)
Depreciation of Tangible Assets (%) Value added per employee
Working capital / current assets Total Assets / Turnover
Current ratio Operating Profit Margin (%)
Liquidity ratio Net Profit Margin (%)
Stock Turnover days Added Value Margin (%)
Collection period Part of Employees (%)
Credit Period Return on Capital Employed (%)
Turnover per Employee Return on Total Assets (%)
Interest / Turnover EBIT Margin (%)
Debt Period (days) EBITDA Margin (%)
Financial Debt / Equity (%) Cashflow / Turnover (%)
Financial Debt / Cashflow Working Capital / Turnover (days)

10. Hard problem
0
2
4
6
3 4 5 6 7
Class 0
Class 1
λ
1
λ
2
First two principal component from PCA

11. How HLVQ-C works
0
0.5
1.0
1.5
0 0.5 1.0 1.5
Class 0
Class 1
After
Before
?
d2
d1
X
Y

12. DIANE 1 (error%)
Model Error I Error II Total
MDA
SVM
MLP
HLVQ-C
26.4
17.6
25.7
11.1
21.0
12.2
13.1
10.6
23.7
14.8
19.4
10.8

13. DIANE 1 - HLVQC Results
Method
Classification
Weighted Efficiency
(%)
Z-score (Altman) 62.7
Best Discriminant 66.1
MLP 71.4
OurMethod 84.1
Source: Vieira, A.S., Neves, J.C.: Improving Bankruptcy Prediction with Hidden Layer
Learning. Vector Quantization. European Accounting Review, 15 (2), 253-271 (2006).

14. Personal credit

15. Results I – 30 days into arrears
Classifier Accuracy (%) Type I Type II
G
Logistic 66.3 27.3 40.1
54.8
MLP 67.5 8.1 57.1
61.1
SVM 64.9 35.6 34.6
52.3
AdaboostM1 69.0 12.6 49.4
55.7
HLVQ-C 72.6 5.3 49.5
52.3

16. Results I – 60 days into arrears
Classifier Accuracy Type I Type II
G
Logistic 81.2 48.2 11.0
21.2
MLP 82.3 57.4 9.1
20.1
SVM 83.3 38.1 12.4
19.3
AdaboostM1 84.1 45.7 8.0
14.7
HLVQ-C 86.5 48.3 6.2
11.9

17. DIANE II (2002 – 2007)
• More data
• Longer history
• More features

18. Year
2006
Classifier Accuracy Type I Type II
Logistic 91.25 6.33 11.17
MLP 91.17 6.33 11.33
C-SVM 92.42 5.16 10.00
AdaboostM1 89.75 8.16 12.33
Year
2005
Classifier Accuracy Type I Type II
Logistic 79.92 19.50 20.67
MLP 75.83 24.50 23.83
C-SVM 80.00 21.17 18.83
AdaboostM1 78.17 20.50 23.17
Results

19. How useful?
[ ]mexexNV III )1()1( −−−=η






−
>>
− I
II
e
e
mmG
x
x
11

20. The Rating System

21. French market - 2006

25. -2
-1
0
1
2
-2
-1
0
1
2
-1.5
-1
-0.5
0
0.5
1
cr
eb
Score (EBIT, Current ratio)

26. MOGA
Multiobjective Genetic Algorithms

27. MOGA – feature selection

30. S-ISOMAP – manifold learning

31. The idea behind it

35. Other approaches
• SVM+ - domain knowledge SVMs
• RVM – probabilistic SVMs
• NMF – Non-negative Matrix
Factorization
• Genetic Programming
• …

36. The Power of Social Network
Analysis

37. Bad Rank Algorithm

38. Where are the bad guys?

39. Bad Rank for Fraud Detection

40. Results with Semi-supervised Learning

41. Networks Analysis
A world of possibilities
• Identify critical nodes / links / clusters
• Detailed information of dynamics
• Stability / robustness of system
• Information / crisis Propagation
• Stress tests

44. Team
João Carvalho das Neves
Professor of
Management, ISEG.
Ph.D. in Business
Administration,
Manchester Business
School
Armando Vieira
Professor of Physics, &
entrepreneur. Ph.D. in
Physics and researcher
in Artificial Intelligence
Bernardete Ribeiro
Associate Professor
of Computer
Science, University
Coimbra,
researcher at
CISUC.
Tiago Marques
Marketing and
Business
Consultant,
E-Business
Specialist,
Director of
Research
Business
Director
IT Researcher Marketing
10+ years experience in AI
25 years experience in Credit Risk & Financial Analysis
15 years of marketing experience

45. What do banks need in credit
management?
Efficiency Accuracy
Savings of Capital – Basel requirements
This is a highly regulated industry with detailed and focused regulators

46. What do they get?
Boosting the accuracy of credit risk methodologies will lead to considerable gains for banks
Source: Issue 2 of NPLEurope, a publication overing non-performing loan
(NPL) markets in Europe and the United Kingdom (UK).,
PriceWaterhouseCoopers
Non-performing loans - Europe
0
50
100
150
200
250
Germany UK Spain Italy Russia Greece
2008
2009
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
2005 2006 2007 2008 2009
% Corporate Debt Default -
Portugal
BillionsofEUR
NPL(%)
Source: Bank of Portugal

47. AIRES Solution

48. AIRES.dei.uc.pt