A Customer Profiling Framework for the Banking

A Customer Profiling Framework for the Banking Sector1 Benkt Wangler Sara Holmin KTH, Sweden {benkt | saraho} @dsv.su.se

Peri Loucopoulos Panos Kardasis UMIST, UK {pl | kardasis} @co.umist.ac.uk

Giota Xini 01-Pliroforiki, Greece [email protected]

Despina Filippidou Datel Advanced Ltd, UK [email protected]

Abstract The European banking marketplace has recently undergone several changes that cause banks to re-engineer their processes and legacy information systems. The ultimate purpose of such projects is to enable better customer understanding, so that products and services are tailored to specific customer needs, and addressed to the right customer groups. As the solution to this challenge possibly lies within the masses of existing data, what lacks is the methodological support for structuring all this knowledge, and for ensuring that it is usable in modern banking practices. Such practices may vary from day-to-day operations, to complex knowledge discovery experiments. This paper reports on a research and development project that deploys enterprise knowledge modelling techniques for structuring, and re-using models of banking knowledge. The outcome of this work is packaged in the "customer profiling framework", being a set of patterns dealing with: (a) informational and organisational structures for the banking sector, (b) means and ends in knowledge discovery activities with specialisation to banking applications.

1.

Introduction

To keep and advance their competitive edge, companies of today need to individualise products and to approach every single customer in a particular way. This is usually referred to as "mass customisation" [Davids 1986] and "micro marketing". In his book on data warehousing, Sean Kelly [Kelly 1996] explains that customisation results in increased customer satisfaction and retention. Customisation requires, however, a profound knowledge of customers and their needs and habits (which applies not only to banking, but to almost every type of industry). Such knowledge would help companies to find answers to questions such as: • • • •

Which customers would be interested in certain types of products and services? How would a product or service be designed so as to suit a particular customer, or a cluster of customers? How effective is the marketing on specific customers? Which attributes suggest that a certain customer cluster should be (or should not be) targeted with a new product or service?

In order to acquire more knowledge about the customer (or in other words "to profile their customers"), banks need to take advantage of the information they already possess, i.e. to unveil 1

This paper was published in: Wangler, B., Holmin, S., Loucopoulos, P., Kardasis, P., Xini, G. and Filippidou, D. (1999) A Customer Profiling Framework for the Banking Sector, 9th European Japanense Conference on Information Modelling and Knowledge Bases, E. Kawaguchi, H. Kangassalo, H. Jaakkola and I. A. Hamid (ed.), IOS Press, Iwate, Japan, 1999, pp. 57-73.

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knowledge buried in their production databases, as well as to complement this with pieces of knowledge acquired from the outside world. A simple study of the transactions of a customer for example could reveal whether the customer has a saving or an expense policy, if he is often collecting information, or if he will have the chance to make a purchase in the future. Little concrete advice is, however, given concerning how to obtain this information, transform it and distribute it through internal company channels [Kotler 1994]. In any case, the utilisation of this knowledge brings about a number of interrelated challenges: in the regular banking operations (e.g. handling customer applications), in decision making (e.g. product pricing, customer targeting), and also in the functionality of systems which maintain the aforementioned operations. Apparently, the banking market faces a need for change to which there is no simple solution. The complexity of the situation is due to the involvement of many different technologies, like business process improvement, legacy migration, data warehousing and data mining, which however have been used in isolation until now. The work presented here intends to deliver a framework which: •

• • • •

Gives advice on how to reconcile any customer-related information derived from heterogeneous data sources on a single data model (naturally aiming at a single warehouse for a specific banking setting). Suggests what knowledge attributes may indicate high (or low) value of a customer, or of an agreement for a bank. Guides the improvement of the banking operations towards a state where customer profiling will be easier and more effective. Details the data mining process in such a way that bankers will be able to perform it with only little help from experts, and they will immediately apply the results in their work. Provides the necessary degree of formality for enabling the development of robust and flexible systems to support customer profiling.

The way forward is to combine knowledge of banking conceptual structures, modern enterprise modelling methods, and ways of working in knowledge discovery in databases. The outcome of this technology integration is a set of generic (reusable) patterns dealing with the information structures of banking applications, the links of these information structures to typical banking operations, and their use in data mining experiments towards the discovery of knowledge useful in customer profiling. In order to enable a better understanding of what the customer profiling framework is, it is probably necessary to refer to the things it is not. The following table is a briefing of that: A banking encyclopaedia

Investigating the concepts of the banking sector has been a significant part of this work. However, coming up with a complete and globally acceptable banking ontology has not been our principal aim. The users of the framework will only find concepts that appeared in our extended case study, and will need to customise them to their own bank. Although it is difficult to guarantee completeness, the framework can ensure consistency when modelling a certain banking setting.

The 10 commands on how banking processes should be

The customer profiling framework does not contain a complete set of patterns of banking operations (e.g. customer application evaluation). In order to apply the customer profiling framework, the banking

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experts will need to depict their bank in terms of our modelling approach. It is our belief that it is easier to become familiar with a modelling approach through the examination of examples, than by studying a declarative manual. The derived models of the banking operations may or may not be similar to the suggested patterns. The patterns will just introduce basic concepts (e.g. what is a business process, and what it is not), and will demonstrate the modelling notation to be adopted. Data mining and data warehousing for dummies

In order to create a data warehouse, users need to follow a number of steps, from the development of the necessary meta-data to the actual data extraction. It is impossible for any methodology to provide ready-made warehousing meta-data. The users will at least need to customise a data model according to their own data sources. Similar is the case with data mining exercises.

Treasures found in other banks' data

Data mining results heavily depend on the particularities of the application from which the data have been derived. That is, what is sound for one application may be wrong or irrelevant for another (e.g. the significance of a customer's marital status for their profitability will vary from one society to another). The presentation of patterns of data mining results aims at demonstrating how such results should be represented.

The exact contents of the framework will be detailed in section 3. Section 2 is an introduction to the approach used for modelling the banking applications, namely the EKD approach. EKD has been enriched with the concept of patterns, and has been extended in such a way that the results of the data mining operations can be depicted in a similar way with the existing enterprise knowledge. As a result, the user of the framework will be able to deal with the overall banking knowledge (developed or discovered) in a unified, consistent manner. Section 4 describes the ways of using the customer profiling framework. Finally, section 5 attempts to present similar approaches to the same problem, in the scope of comparing them with the approach presented here.

2.

Background knowledge

Business knowledge modelling is about describing in a formal or semi-formal way an enterprise with its agents, work roles, goals, responsibilities and business rules together with the technological infrastructure that supports the enterprise. Business knowledge modelling supports the strategic alignment task as well as the management of planning, evolution and change of business practices and also of systems. It provides the means for describing the current structure of the enterprise, its missions and objectives. The impact of business knowledge modelling seems to be wide since its applicability is being considered in a number of different fields. Examples of such fields are business process re-engineering [Curtis, Kellner et al. 1992; Davenport 1993; Hammer and Champy 1994], enterprise integration [Goranson 1992; Petrie 1992], information systems development [Eriksson and Penker 1998], computer integrated manufacturing [Berio, Dileva et al. 1993; Kosanke and Vliestra 1989], and electronic data interchange [Lindencrona 1994]. There are a number of different methodological approaches to business knowledge modelling depending on one’s desired viewpoint. For example the Actor-Dependency model [Yu and

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Mylopoulos 1994] proposes a conceptual modelling framework which attempts to provide a systematic way of organising and using knowledge from multiple organisational analysis perspectives; the M* methodology [Berio, Dileva et al. 1993] aims to assist designers in evaluating, planning and implementing changes in a CIM system; whereas the ORDIT approach [Dobson, Blyth et al. 1994] considers an enterprise in terms of work roles and responsibilities. Each methodology is suitable for solving problems in specific environments of certain cultures. The approach taken here (namely the EKD2 approach) attempts to combine three different perspectives: the organisational or intentional, the operational and the informational. Each of these perspectives is facilitated by one or more models and their graphical or textual notations. The development of the aforementioned models obeys to the constraints of corresponding EKD meta-models, which consist of a number of interrelated concepts, and whose purpose is to describe the enterprise and its universe in a consistent, correct and compete manner. The models used in EKD address the following issues: (a) the objectives pertaining the AS-IS and the TO-BE enterprise situation, depicted in goal models; (b) the identification of actors, the roles they play, and the activities they perform as part of these roles, represented in actor-role and role-activity diagrams; (c) conditions and constraints of various types expressed in business rule statements; (d) information structures that facilitate the execution of enterprise operations, contained in business object models. 2.1

EKD goal modelling

Goal modelling is about describing the causal structure of a system (be it a business system, or a software system), in terms of the goals-means relations from the “intentional” objectives that control and govern the system functions to the actual “physical” processes and activities available for achieving these objectives [Loucopoulos and Kavakli 1995; Kavakli et al. 1996; Kavakli and Loucopoulos 1998]. The notation adopted to represent goal models is influenced by the notion of AND/OR graphs used in problem-solving. According to this technique the goals are organised in a multi-level, more or less hierarchical goals-means scheme. 2.2

EKD role modelling

Role modelling is about representing the organisational and behavioural aspects of an enterprise. This modelling perspective is concerned with the way business processes are performed through the involvement of enterprise actors in discharging the responsibilities and the interaction of their roles with other roles. An abstract view of roles, their interactions and dependencies, and the principal objectives they satisfy are described in actor-role diagrams [Loucopoulos and Kavakli 1997]. A more detailed view of the activities in which roles are engaged is given by role-activity diagrams (RADs) [Ould 1995; Kardasis and Loucopoulos 1998]. 2.3

EKD business rule modelling

Business rule modelling is about defining the constraints for enterprise operations, roles and business objects. The term "business rule" is used to collectively express aspects of policy, standards, procedures, authorisation mechanisms and such like. The role of business rule modelling is twofold. Firstly, it is concerned with constraints placed upon information structures and with the derivation of new information based on existing information. Secondly, it is concerned with the eligibility for the execution of different activities and constraints placed on their order of execution. The EKD approach supports a formal textual language to support the 2

EKD stands for "Enterprise Knowledge Development" and has been developed by UMIST (Prof. Peri Loucopoulos), by the Royal Institute of Technology in Stockholm (Prof. Janis Bubenko) and by PARIS-I SORBONNE (Prof. Colette Rolland) in the collaborative research projects F3, ELKD and ELEKTRA.

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expression of business rules. Rules are represented as a WHEN... IF... THEN… statements [Martin and Odell 1992; Herbst et al. 1994]. The WHEN part of a business rule will contain the triggering expression and the IF part the preconditions for the invocation of an activity, or for the state change of a business object. 2.4

EKD business object modelling

Its purpose is to describe the business objects that are required by the enterprise operations in order to fulfil their objectives, together with the logical relationships between these objects. The EKD approach provides a static view in business object modelling (definition of objects and associations) through class association diagrams [Rumbaugh et al. 1991]. 2.5

Enhanced models for knowledge discovery

A knowledge discovery experiment consists of various knowledge discovery activities. Logically, the experiment is divided into several phases, which may be revisited according to new knowledge gained as outcome of the experiment, or provided by the bank's experts. The first logical phase would involve the dataset preparation. The second step deals with the selection of data records to be examined for hidden knowledge (data selection). Data mining is the actual knowledge discovery operation, which may be data clustering, data classification, or data visualisation. Data mining operations target at certain measures (e.g. 'profitability', 'risk', etc.). These measures are examined in terms of different data attributes, which may be found in or derived from the source data records. Data mining operations produce two different types of results: (a) data attribute dependencies, and (b) knowledge discovery rules. Both types of results reflect hidden relationships among the attributes of the bank's databases. The criteria for separating them deal with whether these dependencies can be quantified. Data clustering and classification result in expressions that can be conformed to a unified expression format (IF… THEN… statements). For every knowledge discovery rule there is an associated degree of confidence and support [Clarke et al. 1998, Hyperbank Consortium 1998]. 2.6

The patterns extension

Patterns capture the static and dynamic structures of solutions that occur repeatedly when producing applications in a particular context [Coplien and Schmidt 1995; Hay 1996; Fowler 1997]. In order to ensure applicability and usefulness, patterns should: •

• •

Define a problem together with the forces that influence the problem and that must be resolved. Forces refer to any goals and constraints (synergistic or conflicting) that characterise the problem. Define a concrete solution. The solution represents a resolution of all the forces characterising the problem. Define their context. A context refers to a recurring set of situations in which the pattern applies.

Patterns consist of a body and their description [Rolland et al. 1998]. The former is a model that is effectively reused whereas the latter aims to describe the context in which the body of the pattern can be reused. The template used for expressing a pattern body within the customer profiling framework (i.e. the design metaphor) is that of EKD. The pattern descriptor is an aggregation of a signature and guidelines. The signature aims at describing the characteristics of a

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pattern, where it can be used, why, etc. The guidelines are recommendations on the way the body of a pattern can be reused.

3.

Overview of the framework patterns

3.1

Usage intentions

Usage intentions are utilised within the customer profiling framework in order to assist: • •

Navigation into the customer profiling patterns. Understanding of the contents of the customer profiling patterns by representing their usability according to different operational, tactical and strategic goals of the banking sector.

Figure 1: Example of the framework's usage intentions The usage intentions appearing in the customer profiling framework relate to the very abstract goal of "directing a bank towards customer profiling". This in turn can be realised by satisfying the following main categories of intentions: 1. "Increase business profit from agreements with individual customers" (Figure 1): Banking experts increasingly recognise the necessity of customer-centric approaches which relate to measures of profitability of individual customers. The refinement of this particular usage intention leads to a set of knowledge requirements that need to be satisfied in order for a bank to understand their customers better and to predict their behaviour in respect to potential profit.

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2. "Manage the bank's organisation process": The need for increased flexibility and for better performance leads to fundamental changes in an organisation's way of working. On this line, banks need to improve understanding of their current business functions, and of the 'why's and 'how's regarding the response of customers to the particular working practices. The refinement of this usage intention leads to patterns about knowledge requirements that need to be satisfied, in order to understand and measure the behaviour of a bank towards different types of customers. Application of knowledge discovery techniques relating to these knowledge requirements will result to patterns of customer behaviour in response to different banking practices. 3. "Manage the knowledge discovery process": The need for rapid and reliable decision-making and for effective and efficient exploitation of data related to customers, prospects, and products impose the need for performing knowledge discovery operations. The usage intention presented here is decomposed by further knowledge requirements regarding solutions on data warehousing and data mining problems, as well as the verification of the corresponding findings (i.e. the data mining results). The aforementioned usage intentions and their refinements are presented in goal decomposition trees (developed according the EKD goal modelling view). Figure 2 shows how the usage intentions of Figure 1 are further decomposed.

Figure 2: A second example on usage intentions

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3.2

Business object patterns for the banking sector

An integral part of the customer profiling framework is the business object model that describes the basic customer-related concepts within a bank. The model has been based on the in depth study of three banking applications (i.e. data found in the banks' legacy systems), and of additional knowledge requirements indicated by the banking experts. The aforementioned data have been enriched and revised in the light of external information and were elaborated during extended workshops among the domain experts and the modellers. The business object model has been partitioned and packaged in a number of conceptually allied patterns. Figure 3 is an overview of the business object model, the concepts of which have been categorised as follows:

B a n k i n g C u s t o m e r * * B a n k t o C u s t o m e r I n t e r a c t i o n

P r o d u c t I n d i v i d u a l C u s t o m e r O r g a n i s a t i o n

Figure 3: Overview of customer-related knowledge Customer-related knowledge: This involves information about parties who use, have used, or may use some service of the bank. The patterns falling into this category deal with geographic and geo-demographic characteristics, employment details, family and financial status, household and lifestyle information, technological infrastructure at the customer's site, and so on. Each one of the aforementioned aggregates contains a whole list of object attributes (for example "household information" has attributes "residence ownership", "period at address", "type of residence", "adults number", "children number", "home insurance type", "years in electoral role"). Bank-to-customer interaction: This pattern describes the typical types of interaction among the bank and the customer such as transactions, payments, exchange of information, and applications for products. Other objects referring to this interaction are costs, generated revenues, communication channels, arrangements, and also products, bank accounts, agents and communication media associated with a particular interaction instance.

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Figure 4: Part of the pattern of "product characteristics" Product: The product class defines the specific characteristics of a product with which the bankto-customer interaction is related (for example for the area of consumer credit the product may be a loan or a card). The "product" class is subject to customisation for particular banking settings. Figure 4 gives a flavour of what is contained in the product-related object pattern of the customer profiling framework. 3.3

Process patterns (roles, activities and rules)

The process patterns being described in the customer profiling framework follow three different modelling notations, according to the required level of detail (abstract level, detailed level) and the required modelling perspective (structural or behavioural). Therefore, we have the following pattern types: • •

•

Actor-role patterns: Describe the roles involved in a process and determine responsibilities and dependencies between these roles (abstract, structural descriptions of processes). Role-activity patterns: Highlight the activities involved in a process and connect specific roles to these activities (detailed, structural descriptions of processes). An example roleactivity pattern is presented in Figure 5. Business rule patterns: Present business rules that constrain and control the banking processes (detailed, behavioural process specifications).

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Figure 5: An example role-activity pattern The aforementioned pattern types have been used in order to cover four conceptual categories of processes that are mostly related to customer profiling activities. These categories are: • • •

•

Customer evaluation: The pattern set of this category involves roles, activities and rules related to customer scoring, profitability prediction and security checking against fraud. Product promotion: These patterns are intended to facilitate improvements in the area of marketing new or existing products. Product design: These patterns describe possible scenarios in the specification of product features, of customer target groups, as well as of techniques for marketing new products or of repositioning existing ones. Money transfer: The pattern set of this category suggests a possible process for handling transfer of money within a bank or between banks, i.e. how a banking system may deal with storing, verifying and processing debit and credit accounts in this context.

3.4

Banking measure patterns

The studies by Hamel and Prahalad [Hamel and Prahalad 1990], concerning core competencies and capabilities of a corporation, and by Porter on the competitive advantage [Porter 1985] underline the need for analysing what customers want, and how they perceive the position of competitors in the market. Major steps in this analysis are: • • •

The identification of the major attributes that customers value (e.g. the functions and performance levels that customers look for). Rating and ranking of these different attributes. Examining how customers rate the performance, e.g. in order to differentiate products.

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•

Rating and ranking of the competitors.

In several cases, recent literature points out specific attributes of the aforementioned type, like customer satisfaction for example (customer satisfaction is not only crucial for retaining customers, but also for turning them to "part-time marketers" who will implicitly or explicitly advertise a product [Storbacka 1993]). The development of the customer profiling framework presented here has been inspired by a number of positions like the one above, together with the experience of banking personnel, coming from three different banking settings. The theoretical definition we set out for measures follows: A measure describes a specific characteristic for a customers, which can be inferred from past experience.

particular

class

of

Once such a measure is derived, then customers’ data holdings are searched for individuals with a close fit to that characteristic. Sets of such characteristics are used as criteria to appropriately evaluate the customers. We distinguish between two types of banking measures: •

Customer behaviour towards a bank (Figure 6), representing those measures that are used for assessing the behaviour and profitability of a group of customers. The sub-types of this measure category are customer needs and preferences, services utilisation, maturity, customer satisfaction, customer value, risk, loyalty-retention and business potential.

•

Banking behaviour towards customers, representing those measures that are used for assessing the behaviour of a bank towards certain customer groups. Essentially, these measures are used for evaluating the "bank-to-customer interaction" aspects. The measure of "banking behaviour towards customers" is specialised into two subcategories. The first is content-based with sub-types "service offering" and "delivery-access channels". The second is process-based with sub-types "marketing communication", "customer support", and "quality of service provision".

While inferring a measure for a customer or a bank, different priorities may take place. Let us assume that "risk" is partly derived from "fraud", and "repayment default". The banking experts suggest that "fraud" has higher priority over "repayment default" for deriving "risk". The customer profiling framework caters for capturing these priorities, together with the formulas for inferring the value of a measure from other measures.

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Figure 6: Measures of "customer behaviour towards a bank" 3.5

Knowledge discovery patterns

The measures included in the customer profiling framework are intended to facilitate a collection of knowledge discovery activities. These activities have been documented within a number of knowledge discovery patterns, and provide guidance on data warehousing and mining problems. These patterns have been constructed through various phases that took place in an iterative manner. The first phase involved experimentation on an initial data set and the subsequent phases dealt with the application of the derived patterns to other banking settings. The knowledge discovery patterns are of the following three types: Data set selection for deriving customer profiling measures: These are object-based patterns. The business object models are tailored appropriately in order to indicate the data entities, attributes and interconnections for performing the necessary knowledge discovery activities. Figure 7 contains the body of such a pattern (the associated data attributes are linked to this pattern through hyperlinks). The informal signature of the pattern is presented in Figure 8. Derivation rules on customer profiling measures: These patterns contain formulas (expressed as rules) for deriving measures and data attributes from other existing information (Figure 9). Data mining techniques for customer profiling: These are patterns on the use of different data mining techniques, according to the business context in which the mining results will be used. A set of application examples is also attached to these patterns. The examples demonstrate the outcome of the described techniques and are expressed in notations contained in EKD (and its latest knowledge discovery extensions).

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Figure 7: Pattern body of "data set content for measuring customer value"

Figure 8: Informal signature of "data set content for measuring customer value"

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Figure 9: Example of derivation rule patterns

4.

Ways of working with the customer profiling framework

The use of the customer profiling patterns could be summarised as follows: • •

•

Patterns are intended to assist in integrating customer profiling techniques with existing banking operations. Patterns will assist banks in building their data warehouses [Inmon 1996]. This would include determining which data to extract as well as designing procedures for extracting, cleansing and scrubbing, and transforming data. Patterns can help bankers to conduct data mining experiments, by indicating the appropriate measures to be assessed and the data attributes needing to be examined.

The usage intentions presented in the previous section constitute a roadmap for applying the right solution for each (long or short-term) problem. The upper levels of the usage intention goal graphs contain high level strategic objectives. These ones are decomposed to lower level operational goals. Operational goals are linked to specific solution patterns, which need to be customised for particular banking settings. The customer profiling framework is meant to be used (a) for preparing a bank in the scope of more efficient customer profiling; (b) for maintaining regular profiling operations. The preparation phase will deal with the identification of the bank's application areas that may be of relevance to customer profiling. The corresponding solution patterns only give a flavour of the roles involved in these application areas, and the contained activities and business rules. The documentation of the banking applications according to the EKD methodology may eventually prove quite effort consuming, given that the existing banking processes can be significantly different than the provided patterns. The derived models will remain as an asset3 though, to be 3

The customer profiling framework presented here has been the outcome of an R&D co-operative project which examined three large European banks. Although all of the three banks had undertaken modelling projects in the past, each aspect of them (i.e. organisational, operational, informational) was approached in a different way. Although the adopted approaches were standards at the time (IDEF0 for example) there

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used by subsequent projects. This is because EKD covers all the necessary views in an integrated way (i.e. by updating one model, it is easy to trace the effects on other modelling views, due to the way the approach is structured on a set of interrelated enterprise meta-models). The development of the business object model on the other hand is a typical situation of pattern customisation. The distinction between customer-related information and information dealing with bank-to-customer interactions is suitable for accommodating every potential need for data. The user only has to decide what objects are of concern for the specific banking setting, and to do the mapping between the framework's object attributes, and the actual data attributes of the bank's database systems. This step is anyway necessary for developing warehouses, or for migrating legacy data. Once the necessary EKD models have been developed, the customer profiling framework will assist in both knowledge discovery experiments and day-to-day banking operations. As far as data mining is concerned, the bankers will have to navigate through the usage intention goal graphs in order to find the appropriate measures to be targeted by the miners. The framework may also suggest what data attributes are of relevance for these measures, although the work presented here is not complete in this respect due to limited number of experiments conducted for the banks examined here. This is not thought to be a major problem though. The nature of data mining is such that the reusability of information regarding data trends is anyway doubted. And mainly, because having brought together all the data sources in a structured, consistent, well integrated and well documented manner, is already enough help before using any data mining tool4. The last (but not least) application of the customer profiling framework deals with feeding the data mining results back to the bank's knowledge base5 which containing business rule statements and RADs. A number of queries will indicate suitable changes (at business practices and systems) and will propagate these changes across the organisation in order for the involved parties to be informed. More specifically: The discovered data mining rules can be matched with existing business rules as both rule types reference business object and their attributes directly. With the appropriate rule engine or even manually, it is feasible to identify conflicts between these rules, so that either the data mining results are refined, or the business rules are updated according to the recent knowledge findings. Although the modification of business rules is subject to decision making, the aforementioned information (i.e. the existence of conflicts between certain rules) will be useful input for such activities, especially in the banking sector where the applied rule sets consist of hundreds of rule statements. Moreover, as business rules are directly linked to business activities (according to the EKD metamodels), it is easy to find out what activities are affected by certain knowledge findings. These can be activities in underwriting customer applications, in targeting customer groups with was no integration between them. As a result, some of these sources of documentation had been abandoned completely, and others were incomplete and inconsistent. Typically, the same things would be found to be named differently across the same bank, contradictory policies would be applied in similar situations, data would be duplicated because analysts were unaware of their meaning or even their existence, and so on. Given the size of the banks, the personnel was in major confusion, and the overall project (including data warehousing and data mining experiments) took much longer than it could take if there was better documentation. 4 The tool used for conducting data mining experiments in the particular case studies was IBM's Intelligent Miner. 5 The knowledge based used in this particular project was the MS Repository.

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financial products, in designing and pricing products, in detecting fraud, etc. And given that activities are linked to roles, and roles to actors (again, according to the EKD meta-models), the appropriate queries will inform about the exact personnel to be trained regarding potential policy changes, to be invited to workshops, etc. Finally, when deciding certain changes in banking policies (either by modifying existing business rules or by developing new ones according to certain data mining extracts), it is easy to know what information entities are needed to be present for the involved banking activities. This may invoke changes in business practices (e.g. customers may be asked to provide more information about themselves), and in systems (e.g. interfaces will contain different fields, or specific applications will have access to different data attributes than before). Apparently, the customer profiling framework creates a solid background for supporting planning and decision making in the banking sector. This can be exploited by the appropriate infrastructure, starting from the knowledge base to store the bank's business models, the data warehouse for reconciling the heterogeneous information sources of the organisation, the data mining tools with applications for transforming data mining results to the format described in previous sections, to the actual decision support environments, and also to the getaway applications for feeding existing operational systems with more valid input [Brodie and Stonebraker 1996]. However, all these issues are outside the scope of this paper.

5.

Evaluation and future work

In the highly competitive banking marketplace, the need for methods and tools that allow users to achieve a more complete and effective understanding of individual customers has been recognised. Such understanding can then lead to appropriate exploitation of customer data, in order to successfully assist a banking organisation to increase their business profit while improving the quality and efficiency of the business processes. State to art decisional analysis techniques -like data mining- can be used to allow discovery of knowledge, like segmented customer groups with common attributes and properties, which would then drive customisation of the business processes into one way or another. Such techniques would collectively assist the organisation in moving from an account-oriented approach into a more customer-centric one, and therefore move their business towards customer profiling. Commercial decisional analysis tools customised for customer profiling include KnowledgeSEEKER (Angoss Software), UIS (SLP InfoWare), enabling derivation of conclusions about customers in terms of "hidden" customer groups, screened and then found fraudulent customers, risk issues, etc. These are mostly based on incorporating traditional statistical methods with new technologies -like neural networks- in order to identify any possible relationships between the fields and the customer-based legacy systems, and therefore to derive conclusions about patterns of behaviour. Other tools (Churn/CPS from SLP InfoWare, DFMS from Compaq Services, ExpertBASE from EDMS) are customised to assist users in specialised customer profiling aspects, like churn propensity behaviour and maximisation of loyalty, detection and analysis of fraudulent behaviour, and identification of target customer and prospect groups, respectively. Clearly, such solutions to customer profiling focus on the technology supported rather than the knowledge behind. In reality, knowledge is repeated in many customer profiling situations, and therefore should be managed in such a way that generation of customised versions of it can be assisted. Towards this direction, knowledge frameworks like IBM’s Information FrameWork (IFW) and research work [Marsura 1998] have been developed, in order to provide sets of models

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and tools for analysing and structuring information related to banking. Such information can then be used to drive business change, and support organisational flexibility. However, these approaches are not targeted to customer profiling intentions, but rather work across several banking areas and purposes, not supporting any of the knowledge discovery processes and produced results. It was the need for a framework and a pre-populated knowledge base (to guide, drive and support the highly iterative and complex customer profiling process) that led to the development of the framework described within this paper. The knowledge supported within the customer profiling framework can be managed and used in developing additional customer profiling settings for banking organisations, resulting to better decisionmaking environments for banks. Future versions of the customer profiling framework will focus on refining the existing banking knowledge in terms of its consistency and applicability in more banking settings. Enhancements in the knowledge base will also take place in order to cover more business areas and associated solutions to the problem of customer profiling. Finally, more importance will be given to the development of the support environment for storing and managing the customer profiling knowledge, which is currently at a prototyping level.

5.

References

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