This document describes how an international bank used the DATACTIF knowledge generation platform to increase sales of investment products. DATACTIF was used to cluster credit card owners into groups based on their transaction patterns. These clusters provided insights into customers' financial profiles and spending behaviors. DATACTIF then predicted which clusters would be most interested in various banking products like loans and mortgages. This allowed the bank to target specific customer segments for marketing campaigns. DATACTIF's predictive models achieved 41% accuracy for investment products and 78% for personal loans. The combined clustering and prediction results helped the bank develop long-term strategies to boost product sales.

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Directing Intelligence in Private Banking
Increase sales for a specific category of Investment Products
by Gregory Philippatos 10/9/2014

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CONTENTS
1. International Bank. The Challenge. .............................................................................. 3
1.1. Directing Intelligence in Business ......................................................................................... 3
2. The Solution ................................................................................................................. 4
2.1. Align Knowledge Strategy to Business Objectives ................................................................. 4
3. DATACTIF. Knowledge Generator ................................................................................. 7
4. DATACTIF. Banking Application .................................................................................... 8
4.1. Credit Card Owners Clustering ............................................................................................. 8
4.2. Conclusion ..........................................................................................................................10
4.3. Prediction ...........................................................................................................................11
4.3.1 Prediction of future clients with DATACTIF SVM Classification System ............................11
4.3.2 Prediction of future clients with DATACTIF Fuzzy System. .............................................11
4.4. Conclusion ..........................................................................................................................12

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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. Directing Intelligence in Business
Business Intelligence is a vital component in strategic planning for companies that are aware of worldwide competition, ever-shorter production cycles and increasing customer requirements. Due to actual speed of communication through internet of things, it is important to identify meaningful patterns quickly within the collected data.
DIRECTING's mission is the design of knowledge architectural plan as part of business engineering and the creation of Business Intelligence applications in order to provide decision makers a knowledge, diffused to all management levels, increasing this way teamwork, efficiency and profitability.

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2. THE SOLUTION
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.

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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.

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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.

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3. 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, accumulating knowledge and finally finding solutions to problems in industrial, financial and retail sectors.
DATACTIF® uses machine learning methodology and algorithms such as neural network, Kohonen SOM, 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.

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4. DATACTIF. BANKING APPLICATION
4.1. Credit Card Owners Clustering
DATA. Analytical transactions of credit cards owners for a 6 months' 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. 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.
We opted for a 25 X 25 map as the ideal for our case

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Credit card owners of cluster 14. How this cluster was formed
Cluster 14. Socio demographics Cluster 14. Financial Profile

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4.2. 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)

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4.3. 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.
4.3.1 Prediction of future clients with DATACTIF SVM Classification System
DATACTIF SVM System was applied to the credit card owners' database and trained to predict new clients for investment products following the below strategy :
As a result we had a Prediction Accuracy : 41%
4.3.2 Prediction of future clients with DATACTIF Fuzzy System.
By Credit Owners buying behavior we predicted new clients for Personal Loans (PIL) following the below strategy :
As a result we had a Prediction Accuracy : 78%.

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4.4. 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 groups 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