Directing Intelligence in banking

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D i r e c t i n g Intelligence in Finances

http://www.directingintelligence.com/


1. International Bank. The Challenge. ............................................................................... 3

1.1. Toward a customer-centric bank ....................................................................................... 3

2. The Solution. Directing Intelligence in Business ............................................................ 4

2.1. Align Knowledge Strategy to Business Objectives ............................................................... 4

3. DATACTIF Finances©. Integrated Applications .............................................................. 7

3.1. DATACTIF. Knowledge Generator ..................................................................................... 7

3.2. DATACTIF FINANCES©. Credit Card Owners Clustering...................................................... 7

3.3. Conclusion ....................................................................................................................... 9

3.4. DATACTIF FINANCES©. Prediction ................................................................................. 10

3.4.1 Prediction of future clients with DATACTIF SVM Classification System ........................... 10

3.4.2 Prediction of future clients with DATACTIF Fuzzy System. ........................................... 10

3.5. Conclusion ..................................................................................................................... 11



1. INTERNATIONAL BANK. THE CHALLENGE.

A worldwide leader financial institution, faced a

crucial challenge for its private banking business

and especially for a specific category of investment

products and personal loans:

How to increase sales, reduce any risk and reinforce

brand image by communicating the quality of its

private banking department.

1.1. Toward a customer-centric bank

Most banks have never created a close relationship

with their retail customers and understand little of

their actual needs. Tailored products and services

are rare. Instead, complaints about inadequate

advice, disproportionately high interest rates for

overdrafts or call center issues surface in the news

with depressing regularity.

Customer resentment was already running high

before the financial crisis. Since then, trust in banks

has plummeted even further. At the same time

customers have found a new, loud mouthpiece in

online forums and consumer portals.

News about negative experiences can spread

through these media like wildfire. Initiatives like the

“Occupy Wall Street” movement have attracted

media coverage as never before.

The road towards a customer-centric bank

Examples from other industries suggest that

marketing strategy and selective improvements at

individual customer interaction points will not

suffice. Banks need to radically change their

perspective, aligning their entire business model

towards the nexus of their success: the customer.

That means translating deep customer insights into

tailor-made products and services.

Transformation towards a customer-centric

company needs to address 4 areas :

Vision and positioning

Customer engagement model

Development of short and long term strategy

Organization, capabilities, and insights



2. THE SOLUTION. DIRECTING INTELLIGENCE IN BUSINESS

2.1. Align Knowledge Strategy to Business Objectives

After analysis of processes, available data and

information systems, we created a knowledge

strategy in accordance with business objectives,

macroeconomic trends, social environment, banks'

human resources and previous experiences in

marketing activities for the same categories of

products or similar ones.

A strategy based on a progressive transition

(customers loyalty-acquisition and information

consolidation) from the most profitable and loyal

clients, who in parallel offer complete and high

quality information, to prospects. Strategy that is

represented by the following diagram.



In order to implement our strategy we had to define

the notion-axe “time”. Having this axe we can

identify a history, understand its evolution and

predict the future behavior.

We defined the Personal-Family Economy on 3

axes. Income, Savings (positive or negative [loans])

and Consumption; But consumption (using a credit

card) includes time factor.

So we had to understand credit card usage

(consumption) per group of clients (clusters) and

relate those findings with holistic relation between

the clients and the bank.

Consumption of an individual is strongly associated

with his socio-economic profile. As a result of this

association, the groups deriving by the clustering

procedure can also be considered as groups of

people with a similar socio-economic profile.



The next step was to discover the correlation

between these groups with the bank's products

(investments and loans). The conclusion was that

Credit Card owners buying behaviors', allow us to

monitor the banking attitude.

In order to transform the above methodology into

data mining applications, we adapted and

implemented DATACTIF platform, especially for

the needs of The Bank.

DATACTIF embodies a number of different

algorithms from the neural networks and

computational intelligence domain, some designed

from our team and some from the state of the art of

scientific research in data mining and knowledge

discovery areas.

Contrary to the high level of complexity of

DATACTIFs' algorithms, the friendly user interface

allowed its use by decision makers without prior

knowledge or experience of computer science and

statistics.



3. DATACTIF FINANCES©. INTEGRATED APPLICATIONS

3.1. DATACTIF. Knowledge Generator

DATACTIF®, is a Business Intelligence Platform

that generates concept-applications tailor made

for each enterprise needs, enriching in same time

each specific case, with a 15 year experience of

learning processes and accumulating knowledge.

DATACTIF® uses machine learning methodology

and algorithms such as neural network, fuzzy

systems, genetic algorithms, Support Vector

Machines, etc… and contains visualization methods

that allows a global view on the domain that is

under analysis, and an analytical view to all details

offered by the existing data.

3.2. DATACTIF FINANCES©. Credit Card Owners Clustering

DATA. Analytical transactions of credit cards

owners for a 3 years period.

CLUSTERING. Clusters are groups of clients or

other business objects that exhibit a certain degree

of similarity in respect to a number of features that

describe these objects (e.g. transactions of a client).

The discovery and analysis of such clusters leads to

a better understanding of the clients base and offers

an add-on tool for use by the business executives.



The following pictures show the result of Credit

Card Owners clustering and their consuming

behaviors. We used unsupervised learning through

neural networks and Self Organized Map

visualization. The grey dots on the map are the

created groups of people (clusters). The size of the

grey dots is indicative of a cluster’s population. The

numbers on the dots are the cluster index (Cluster

ID). The red color on the surface indicates similarity

between neighboring clusters and the blue the

opposite.

Credit card owners of cluster 14. How this cluster was formed

Cluster 14. Socio demographics Cluster 14. Financial Profile



3.3. Conclusion

Results : 625 clusters with a detailed approach

allowing an in depth analysis and 12 Hyper Clusters

(Figure 5) with common characteristics, a number

that allows the creation of efficient marketing

strategy, taking into consideration particularities at

the same time.

(Figure 5 : 12 Hyper Clusters were discovered)

By associating Hyper Clusters with financial

products (investment products, loan, mortgage,

deposits, etc...) we had a global, direct and

immediate evaluation of the existing clients. As we

see in the example below (Figure 6) the association

result between credit card owners and mortgage,

defines one major target group and two secondary

for further analysis and marketing actions.

(Figure 6: Association Result)



3.4. DATACTIF FINANCES©. Prediction

We used DATACTIF's supervised learning modules

(SVM and Fuzzy System in this case) as they give

the possibility to incorporate expert knowledge in a

form of rules or in a form of examples.

3.4.1 Prediction of future clients with

DATACTIF SVM Classification System

DATACTIF Multi SVM System was applied to

prospects database and trained to predict new clients

for Credit Cards in a first step and then new clients

for banking products following the below strategy :

As a result we had a Prediction Accuracy : 71%

3.4.2 Prediction of future clients with

DATACTIF Fuzzy System.

Using Credit Card Owners buying behavior we

predicted new clients for Investment Products but

also other products such as Personal Loans (PIL)

following the below strategy :

As a result we had a Prediction Accuracy : 88%.



3.5. Conclusion

SVM and Fuzzy Prediction modules produced lists

of clients for further marketing activities in order to

increase sales of all banks' products (investment

products, personal loans, mortgage, etc..).

List of clients extracted following some selection

criteria (i.e. profession) with individual scoring

for each type of product

By associating Prediction results with clustering

results, we had credit card holder clusters and Hyper

Clusters with a degree of certainty for buying a

particular product, as we see in the picture below.

The usage of clusters and Hyper Clusters

offers a macroscopic point of view on clients'

evolution regarding products categories, a point of

view that allows decision makers to create long

term business and marketing strategy.

Prediction results projection on SOM concerning

Investment Products


Directing Intelligence in banking

Data & Analytics

Transcript of Directing Intelligence in banking