1

Data Mining and Customisation of Financial

Services

Professor Rajiv Khosla

La Trobe Business School

La Trobe University

Bundoora, Melbourne, Vic. 3088

Australia

email: r.khosla@latrobe.edu.au

Project Motivation and Objectives

� Our ability to collect data today far outstrips our ability to explore, analyse, and understand it.

� Improvement in financial services in regional Australia has been followed by more rhetoric than any objective study of the needs of the people in the region.

� Effectively understand community needs by factoring in customer demographics and customer focus .

� Assist Org X in meeting their social objective in terms of developing a better understanding the banking and finance needs of the their customers , providing training in modelling customer data using intelligent data mining techniques to local personnel (e.g., Org X personnel),

� Facilitate reduction in Org X’s CRM (Customer Relationship Management) costs by better packaging of their financial products and more focussed targeting of customers .It will help them plan their branch network and internet services more effectively to meet the needs of the community in the region.

Project Resources� Data – 8 million records from July 00 to June 02

� Human– Volunteers

� Rajiv Khosla - La Trobe University

� Finance Manager - Org X

� Database Manager- Org X

– Employed (Paid)

� Research Assistant – La Trobe

� Building, Equipment and Software– Research lab, Three computers , One printer and photocopier

– Data mining software package

– Value - $35,000

2

Project Plan

� Phase 1: Data Selection and Sampling - 4/6 weeks

� Phase 2: Preprocessing of Data - 5/6 weeks

� Phase 3: Data Transformation and Reduction

� Phase 4: Data Mining and pattern Recognition

Phase - 14 weeks

� Phase 5: Interpretation of Knowledge – 4 weeks

� Phase 6:Deployment of Results , Model Refinement

and Final Report – 12 weeks

Project Plan

Saving Account Analysis

� Transaction fee and churn rate Analysis

– Frequency and Recency Analysis

– Frequency and no. of accounts analysis

� Increasing Customer share

– Saving Balance analysis

– Saving Balance Demographic analysis

� Channel Analysis

– Internet and face-to-face banking analysis

� Saving account analysis by transaction code and branch code

3

Frequency-Recency Clustering

Frequency-Recency Cluster Analysis

� Red Cluster (1) - size - 40,000

– Indicative of low/med frequency (0 to 140 days) and

low/med recency (0 to 150 days), and high/med number of

transactions, may hold more than one Org X product

� Green Cluster - size - 2000

– Indicative of very high recency (between 1 and 2 years),

low/med frequency (when active), High number of

transactions (when active), possibly lost accounts/clients

� Blue Cluster - size - ~5000 - 6000

– Indicative of low/med recency, med/high frequency,

med/high frequency, med number of transactions, can be

about to churn accounts and/or may not hold many Org X

products

Frequency-Recency Cluster Analysis

� Pink Cluster - Size - ~4000 - 5000

– Med/high Recencv, low/med Frequency (when active),

med/high transactions (when active), may not hold more than

one or two Org X product and/or about to churn or lost

account

� Yellow Cluster - Size - ~600

– High/V.high frequency, med/high recency, may not hold

more than one/two products, low transactions

4

Frequency-Recency Clustering - Cluster Size

Frequency-Recency Clustering -Recency Analysis

Frequency-Recency Clustering -Frequency Analysis

5

Some Issues Arising in Freq vs Recency Analysis - Customer Retention

� Seasonal Frequency-Recency Analysis

� Quarterly Frequency-Recency Analysis

� Weekly Frequency-Recency Analysis

� Day of the week Frequency-Recency Analysis

Association of Frequency with Org X Products

Frequency Customers S + L + I S +L S + I (Term)

2 4956 226(5%) 1716(35%)

659(13%)

4 4064 130 (3%) 732(18%) 762(19%)

8 1798 53(3%) 250(14%) 328(18%)

16 548 11(2%) 64(12%) 102(19%)

32 225 4(2%) 20(9%) 42(19%)

64 120 1(1%) 6(5%) 17 (14)

128 27 2(7%) 4(15%) 5(19%)

Saving Account Balance Clusters

6

Saving Balance Cluster Size

Saving Balance Cluster Analysis

� Pink Cluster - Size - ~ 6400– Balance Value (up to max of 250,000), value peaks in the range of 100,

000 - 250,000 normal age distribution (mean ~ 44 yrs) Red Cluster -Size - ~6000

� Blue cluster - Size - ~ 3500

– Balance Value (up to max of 120,000), value peaks in the range of 60,000 - 110, 000 normal age distribution (mean ~ 48 yrs)

� Green Cluster - Size - ~1500

– Low Balance (< 60,000), age distribution skewed towards 18-34 years, low investment opportunity

� Yellow Cluster - Size - ~ 4000

– Balance Value (up to max of 120,000), value peaks in the range of 40,000m - 100, 000 normal age distribution (mean ~ 42 yrs)

Saving Balance Cluster Analysis

� Red Cluster - size ~6000

– Low Balance, low investment opportunity, normal age

distribution (between 22-78 years)

7

Saving Balance - Cluster 5 Age Distribution

Cluster Five Age Distribution

0

100

200

300

400

-26 8

16

24

32

40

48

56

64

72

80

88

By Age

Nu

mb

er

of

Cu

sto

mer

Saving Balance - Cluster 5 – Account and Value Distribution

Saving Balance - Cluster 4 Age Distribution

Cluster Four Age Distribution

0

50

100

150

200

250

-26

11

21

30

39

48

57

66

75

84

93

By Age

Nu

mb

er

of

Cu

sto

mer

8

Saving Balance - Cluster 4 – Account and Value Distribution

Saving Balance - Cluster 3 Age Distribution

Cluster Three Age Distribution

0

50

100

150

200

2

10

18

26

34

42

50

58

66

74

82

By Age

Nu

mb

er

of

Cu

sto

me

r

Saving Account – Channel Clustering –Fine Clustering – 18 clusters

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Saving Account – Channel Clustering –Fine Clustering – 18 clusters -

ClusterSize

Observations� Cluster 18

– Med (100-400) face-to-face and Med (50- 80) Internet Banking, Low (< 50) in other channels

� Cluster 13– High (700-1500) face-to-face and High (60-160) Internet Banking, Low

(< 50) in other channels

� Cluster 4– Group which only uses face-to-face and Internet Banking

� Cluster 12– Group embracing face-to-face, telephone and internet banking

� Cluster 8– Med-High (350-800) face-to-face and V.High (100-400) Internet

Banking, Low-med in other channels – ready to embrace technology

Observations

� Cluster 10

– Use all channels except telephone banking.

� Cluster 6

– Use all channels except telephone banking with very high

use of Foreign ATMs

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Saving Account – Channel Clustering – Fine

Clustering – 18 clusters – Internet Banking

Saving Account – Channel Clustering –9 Clusters

Saving Account – Channel Clustering –9 Clusters – Cluster Size

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Loan Account Analysis

� Loan Account Transaction and Demographic Analysis

– Negative arrears, positive arrears and age analysis

� Loan account and other Org X products

Loan Demographic Analysis – Age Vs

Negative Arrears –cluster 4 – High number of

negative arrears

Loan Demographic Analysis – Age vs

Positive and Negative Arrear Clusters -

(Correlate with loan default)

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Cluster Size Distribution

Loan Account and other Org X Products

143419.2%12385S-S

15553.4%2206S-L

18207.8%5038S-I

3661.01%652L-S

8220.4%257L-L

12400.04%24L-I

8072.6%1648I-S

11280.07%56I-L

8672.8%1816I-I

Average Days to Open another accountPercentage to all clients

Num_of_ClientsSAC

Issues Arising in Loan Analysis

� Loan Payment Default Behaviour

� 1st and 2nd Mortgage and Saving Analysis

13

Specific Query Analysis - Is there any evidence that foreign ATM usage

increases over time?Monthly Transaction Distribution for Foreign ATMS

12500

13000

13500

14000

14500

15000

15500

16000

16500

17000

NOV 00 DEC 00 JAN 01 FEB 01 MAR 01 APR 01 MAY 01 JUN 01

By Month

Is there any evidence that foreign ATM usage increases over time? –

Foreign ATM TransactionsMonthly Transaction Distribution for Foreign ATMS

14000

14500

15000

15500

16000

16500

17000

17500

18000

JUL 01 AUG 01 SEP 01 OCT 01 NOV 01 DEC 01

By Month

For Internet-banking users, is there a

preference to use other electronic delivery

channels as opposed to face to face branch

based services? � To get a better observation of the data, we only analyze those accounts with equal to

or greater than 100 Internet banking transactions.

� There are 233 such accounts in Rawsav Table. With these 233 accounts, 95 accounts (about 95/233≈41%) do all their transactions with internet banking and face to face banking. It means these people do not have any other preference at all except face-to-face and Internet banking.

� From the graph below, we can observe that traditional face-to-face banking here still dominate the transactions compared to Internet banking among these customers. For the same group of customers with 233 accounts, we find that 44 accounts (about 19%) utilizing all the current available channels to do their transactions. Its comparison is listed in the next page. For these customers, though Foreign ATM/ EFTPOS and face-to-face banking are composed of most of their transactions, their transactions with electronic channels (IVR, ATM) outweigh traditional face-to-face banking. Among the 223 accounts, 18 accounts never use ATM/EFTPOS services. Their Comparison graph is shown below. Again, we find 72 accounts never use telephone banking in all of their transactions.

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For Internet-banking users, is there a

preference to use other electronic delivery

channels as opposed to face to face branch

based services?

The Favourite Internet Banking Mode

40%

19% 8%

31%

2%

Internet and traditional

All channel

No ATM/EFTPOS

No telephone

Miscellaneous.

For Internet-banking users, is there a

preference to use other electronic delivery

channels as opposed to face to face branch

based services?

� The favorite Internet banking mode can be summarized as

follows: 40% clients only use face-to-face banking as their

second alternative ways of banking. 31% clients don’t like

telephone banking. Interestingly enough, 19% clients tried all-

round banking services. 8% clients don’t like to use

ATM/EFTPOS

Summary

� We started at level zero. We have achieved the

following:

– Saving Account Analysis

� Customer Retention and Transaction Fee

– Frequency vs Recency Analysis

� Association Analysis

– Association of Frequency with Number of Accounts/Products

� Increasing Customer Share

– Saving Balance Analysis

� Channel Trend Analysis

– Internet, face-to-face, telephone, ATMs

– Favourite banking channel

– Foreign ATM vs Own ATM

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Summary

– Loan Account analysis

� Preliminary loan default behaviour analysis

– Age based negative and and positive arrears analysis

� Age Based Loan Transaction analysis

� Loan Account based Cross-Selling analysis

– Term Deposit analysis

� Age based Segmentation

� Post code based Segmentation

– Specific Query Based Analysis

� Foreign ATMs and Own ATMs Analysis

Summary

– Internet Banking Analysis

� Channel analysis

– Trend Analysis in Opening Multiple Accounts

– Branch Based Multiple Account analysis

Future Work

� In this information technology age we can do the

following

� Consolidate on Existing Results

– Customer Retention

– Customer Share

– Customer Demographics Analysis

– Loan Default Behaviour

– Term Deposit Analysis

– Cross Selling and Product Packaging

– Internet Banking Analysis

– Channel Usage Trends

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Future Work

� Credit Fraud Analysis and Behaviour

� On Line Analytical Processing Tool

� On Line Customer Recommendation Tool

� Internet Banking Customer Analysis Tool

Online Analytical Processing Banking and

Finance Tool

Sales/ Relationship

Manager

INTERNAL

DATABASES

News

Wires

Savings

Account

Database

Loan

Database

Customer

Credit

Card

Database

Customer

Demograp

hics

Database

Customer Loan

Payment Behaviour

Rival Bank

Information

Product Packaging

Rival Bank

Information

Fraudulent Transactions

Frequency & Recency

Financial Services

Manager

Fraud Section

Product

Packaging

(Cross Selling)

Customer

Frequency &

Recency

Rival Bank

Information

Fraudulent

Transactions

Customer Loan

Payment

Behaviour

SERVER

Via the

Internet

FIRE

WALL

On Line(Real-Time) Customer Recommendation Tool

Transactional

Data

Predictive

Models (e.g.,

cross selling)

Real Time

Processing

Business Rules

Customer

Database

1:1

Offer

Customer

Demographics

Org X

Representative

Y/N