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1 Solutions for CRM systems integration in organizational systems Lect. Phd. ADELA BARA Lect. Assist ALEXANDRA FLOREA Lect. Assist IULIANA BOTHA SIMONA OPREA Computer Science Department Academy of Economic Studies from Bucharest Pta. Romana no. 6, Bucharest Romania ROMANIA (Capital, 12pt Times New Roman, centered) Abstract: - This paper addresses the problem of CRM systems integration in organizational systems by presenting a case study involving the development of a sales process automation solution for banking institutions. The solution focuses on the management of lending activities and achieves the integration of two elements frequently used in current organizations: a solution for customer and business process management (developed with Micrososft Dynamics CRM 4.0) and an online portal for document management (developed with SharePoint 2010). To improve the efficiency of decision making regarding the eligibility of loan applications a flexible scoring algorithm was developed as well as data mining algorithms for profiling customers. Also a departmental data warehouse was built for more flexibility in the analysis of current activity. Key-Words: - CRM systems, integration, scoring algorithm, lending process, data mining. 1 Introduction The sales of retail credit type banking products involv a high potential risk for companies that offer this type of product. Therefore it is necessary for such institutions to make a sound and prudent management of the credit granting process, thus reducing associated risks such as the risk of solvency, liquidity, etc.. For financial institutions to operate in optimal conditions certain criterias need to be fulfilled, criterias that a bank may achieve by introducing an integrated sales automation solution. The purpose of this paper is to develop a framework for interoperability of software products used in banking and financial institutions whose activities focus primarily on the retail area. Each development stage will follow the specific steps of data, processes and software integration in order to minimize the influence of risk factors and to maximize interoperability and system performance. The solution will analyze the main risk factors that can affect interoperability and propose at each realization stage effective methods and techniques for successful integration of technologies used. Finally we will make an assessment of key performance criteria. 2 Developing the solution s architecture In terms of architectural levels, the system is built on three major components: the data layer, business logic layer and presentation layer, as shown in Figure 1. The data layer consists of the CRM database, which stores information about the current activities of the institution, the relational database for the Data Stage and the Data Mart warehouse. These databases are managed through a Microsoft SQL Server 2008 R2 and an Oracle Database 11g R1 database server. The business logic layer communicates with the interface and the database through Web services and consists of the following components: Microsoft Dynamics CRM 4.0, SharePoint 2010, Oracle Data Miner 11g. Business processes are managed using MS CRM in collaboration with MS SharePoint2010 and data is stored in the CRM database, noting that a processing extension will be made through CRM integration with SharePoint, so that all document processing will be conducted at portal level. The presentation layer includes interfaces for visualizing and analyzing data on both desktops and mobile devices such as laptops, PDAs, smartphones, etc.. These interfaces are available through the reporting tools of both CRM system and the multidimensional analysis system (BI tools). ISBN:

2 After developing the system architecture, the next step is to develop the component and deployment diagrams, in order to structure the software modules and the hardware elements of the system. System modules are divided according to the architectural layers shown in Figure 1 and we identify key components for the development phase, components illustrated in Figure 2. Fig. 1. Solution architecture Fig. 3. Deployment diagram Fig. 2. Component diagram The system will be developed using five Windows 2008 servers. Identified components will be distributed as shown in Figure 3. The five servers are configured as follows: - on server 1 the Active Directory component is installed to connect with the CRM solution; - server 2 is an application server with SharePoint Foundation 2010 installed; - server 3 is a database server with Microsoft SQL Server 2008 R2 used for the management of the CRM database and of the relational database with materialized virtual tables used to build the Data Mart objects; - server 4 is the server on which the Microsoft Dynamics CRM solution and the Reporting Services component of MS SQL Server 2008 R2 are installed; - server 5 is the database server for the data warehouse implementation and it has the following components installed: Oracle database 11g R1 with Oracle OLAP 11g and Oracle data Mining, as well as Oracle Data Warehouse Builder 11g and Oracle data Miner 11g. ISBN:

3 3 Implementation of the CRM system In order to implement a CRM system, Microsoft Dynamics CRM provides developers the ability to customize the entities stored as metadata in the data dictionary. Metadata abstracts data storage details such as schemas and access to data and represent a description of the data structures that control how the application (platform and user interface) operates. The platform uses metadata to adapt to the changes that occur such as changes to table structure. This means that Microsoft Dynamics CRM can be significantly modified to implement a specific business logic and continue to operate normally. A first step in obtaining a loan is the registration of the potential client. There can be three types of clients: individuals, businesses and authorized individuals and data about them are grouped into several categories: general information, professional information, contact address, contact preferences, etc. This information is specified by customizing attributes stored in the client entity. Different types of relationships can be established between client categories such as: between two individuals: family, life partner, acquaintance; between individuals and businesses: administrator, employer, collaborator. For each client some registration information is needed in the form of documents, such as a scanned copy of the ID card. For this there is a dedicated area called Documents which links to the SharePoint portal where the documents are physically stored. For each client a document folder bearing his name is automatically created when creating a CRM client. Later, when creating a credit application, all required documents are added to the corresponding folders following the same process. Document loading is performed directly in the CRM application and documents are immediately visible both in the application and in the portal, as shown in Figure 4. Fig. 4. Documents in Sharepoint portal as viewed from the CRM application Another important module of the solution is the management of loan applications. Defining the entities involves atributes regarding the amount claimed, the date of application, financial performance indicators of customer, information necessary for the scoring sheet. On one loan application several simulations of prescoring/scoring can be made, depending on the changes made directly to the application form. Eligibility is determined based on the result of the last simulation. 4 Implementation of the prescoring / scoring algorithm In the lending process the pre-scoring/scoring algorithm represent the base of the decision to grant or refuse a customer s application. This algorithm is developed based on the requirements of each banking institution and includes a number of general indicators, customized according to specifications. Also the weights of different categories of indicators included in the algorithm are variable depending on: the current credit conditions, the type of credit requested and decisions of the organization s strategic management team. Following the analysis conducted in this field we have found that each banking institution develops its own internal scoring algorithm, which is kept secret and cannot be customized for other institutions. In this context, we propose a generic algorithm that takes into account credit conditions and the type of product required and that contains attributes and weights that can be customized. This algorithm is based on the following formula: Where: S = score scoring; I D = personal data (gender, age, marital status, residence, dependents); I V = revenue information (sources of monthly income, amount of income, occupation, economic sector, employer type, age at current job); I R = relationship with the bank (bank client with a history, other rates, rate type, late payment); I H = interests (hobbies, related monthly expenses); I B = specific information (I 1, I 2, I 3, I 4, I 5 - generic indicators for specific bank information); P i, i = 1.. n = indicator s weight. To model the attributes on which the algorithm will rely we built two new customizable entities via the CRM product: - TIP_SCORING_PONDERI that contains attributes and weights to be taken into account by the algorithm. It contains predefined attributes ISBN:

4 whose weights can be customized and some additional attributes that can be further customized. - TIP_SCORING_CATEGORII in which for each attribute there are scores defined for the intervals in which the values from the loan applications fit. For example for the venit_anual attribute the following ranges can be defined: RON with a score of 10 points, between and with 20 points, between and with 30 points and over 48,000 with 40 points. The algorithm will use a function to calculate on the basis of data within each application the ID, IV, IR, IH, IB scores which will then be weighted by a function with weights p1, p2,..., pn. Finally, the total score for each application will be calculated, ranging between 0 and 100, where 100 represents the maximum score. After using the algorithm, each application will have a calculated score. These applications will be initially evaluated by a credit officer with two options: if the score is at least 50 points the application is sent to the central for final evaluation, if the score is below 50 points, the application may be amended for reassessment or it may be rejected. 5 Implementation of Data Mining Algorithms After calculating the score, some of the information from the credit application and several client information (gender, age, marital status, profession etc.) are used in the Data Mining algorithm described above, to determine the probability of the loan being approved by using a series the techniques implemented with the Oracle Data Miner. This tool provides users, with options for data processing, template application and model design, results testing as well as evaluation and application on new data sets The algorithm is implemented based on customer attributes, and aims to determine the customer profile in terms of probability of loan approval. Integration between CRM system tables in SQL Server and the Oracle Data Mining engine was made through XSD schemas and jtds connectors. Based on tables in SQL Server, we generated types of objects for them, the associated XSD schemas using the DBMS_XMLSCHEMA package. 5.1 Determination of important attributes We have applied the algorithm for establishing the significant attributes for determining the maximum amount that can be granted. Thus, as in [1] and [4], we used the algorithm "Attribute Importance" including all the attributes from the credit application, including the score obtained from scoring. Figure 5. Significant attributes for determining the maximum amount that can be awarded to a client After analyzing the results it is observed that for determining the maximum amount that can be awarded to a specific client the attributes with the highest degree of relevance are: annual income, total debt, the total value of assets held by the client, the guarantees provided for credit, profession, age, marital status, amount of customer deposits, if any, loyalty to the bank, prescoring / scoring score. These attributes will be used in the regression model that will determine the maximum amount allowed to be lent. 5.2 Estasblishing the maximum amount that can be granted To determine the maximum amount that can be awarded to a client we implement the regression algorithm using as determinant attributes the ones identified following the application of the "Attribute Importance" algorithm. Model accuracy is 87.48% which for a regression algorithm is an acceptable value. In Figure there can be observed the relatively ISBN:

5 uniform distribution of estimated values, and from a comparative analysis of the estimated real values it is observed that the deviations are within ± 1000RON, which is an acceptable margin. Figure 6. Distribution of values after regression The two attributes: prag_sumă_acordată and probabilitate_acordare attributes and values estimated from DM algorithms will be visible on a credit application will be considered in evaluating its Atributele probabilitate_acordare și prag_sumă_acordată cu valorile estimate în urma algoritmilor DM vor fi vizibile pe cererea de creditare urmând să fie luate în considerare în procesul de evaluare a acesteia 6 Creating a Data Mart for multidimensional analysis of lending The integration solution we developed allows preserving history simulations performed on each application. In order to realize multidimensional analysis of customer profile and product sales as well as of volume of lending by customers type and sales units, we have implemented, as in [4], a data integration solution with a Data Mart starting from relational data sources in SQL Server. Data integration with a data warehouse involves defining an organizational Data Mart based on data from the CRM system and interconnection methods by following these steps: Step 1. Identifying the necessary data sources for constructing multidimensional objects. These data sources are the tables of the CRM s relational model regarding entities such as: customers, loan applications, unit sales, loan products, sales agents. Step 2. Using the sources identified in the previous step, we build a relational model consisting of materialized virtual tables in which data obtained from the CRM system s sources will be loaded. This step is necessary for the construction of a Data Stage required in the process of extraction, transformation and loading (ETI) for the data warehouse. The relational model underlying the ETI process is built using a series of data transformations via stored procedures in T-SQL language in SQL Server. Step 3. Making multidimensional objects. At this stage the facts tables and dimensions of the Data Mart are defined, using Oracle Warehouse Builder, an instrument that allows the interconnection of various data sources, including SQL Server using JDBC connectors. Another option for interconnection sources from SQL Server and Oracle Database is to generate XSD schemas,. After defining the data sources represented by materialized virtual tables built in the previous step, we build the dimension type objects based previous specifications: DIM_CLIENTI, DIM_PRODUSE, DIM_ZONE, DIM_UTILIZATORI, DIM_TIMP. For these we have also built the hierarchies: H_CLIENT with the levels L_CATEGORIE, L_TIP, L_JUDET, L_CLIENT, H_ZONA with the levels L_ZONA, L_TERITORIU, L_UNITATE, H_PRODUSE with the levels L_CATEGORIE, L_VALUTA, L_TIP, L_DESTINATIE, L_PRODUSE, H_UTILIZATORI with the levels L_DOMENIU_RESPONSABILITATE,L_TIP_UTI LIZATOR,L_MOD_ACCES,L_UNITATE,L_RES PONSABIL. Figure 7. Star type schema of the objects in the Datamart ISBN:

6 For the time dimension we considered the default hierarchy: year, quarter, month, day. We built the CUB_CERERI facts table which defines the necessary measures for analysis (Fig. 7). After the construction of the data cube, we also realized the mappings between the relational sources represented by materialized virtual tables and the multidimensional objects. These mappings are the basis of the data loading process into the Data Mart, process that will be executed in the next step. Also, we built a series of analytical reports that enable multidimensional analysis of lending activities. After determining the attributes regarding the likelihood of contracting and the amount awarded, we run the process loading data into the Data Mart process. Based on previously completed interconnection a series of analytical reports will be obtained through Business Intelligence tools (Fig. 8) Figure 8. Multidimensional analysis of data from the Data mart For example, in the report shown in Fig.6 we performed a comparative analysis between the amount requested and the amount awarded for different categories of loan products, according to a certain period of time (in 2011) and one specified currency (EUR). It can be observed from the graphical representation that the algorithm for determining the threshold for the amount granted is effective, the amount granted being always less than the maximum required. 7 Conclusion The developed solution is a complete and comprehensive solution that can be applied successfully in any banking institutions with minimum changes according to the specific implementation of that institution. From a technical and functional perspective the solution is flexible and can be easily adapted by adding new features such as integration with an ERP system or with a complete organizational portal. It also ensures the interconnection of objects from the Data Mart with an organizational data warehouse. The solution shows a high degree of scalability. Thanks to CRM, SharePoint portal and system Data Mart solution can be resize according to the changes of organization without affecting overall system performance. Acknowledgment: This paper presents some results of the research project PN II, TE Program, Code 332: Informatics Solutions for decision making support in the uncertain and unpredictable environments in order to integrate them within a Grid network, financed within the framework of People research program. References: [1] Bâra A., Botha I., Velicanu A., Florea A. - Framework for Executive Information Systems Development in Cloud Computing Architecture, Proceedings of the International Conference on Information Society (i-society 2010) June 2010, London, UK. [2] Altova GmbH, Whitepaper - Enterprise Data Modeling Using XML Schema - Investigating an emerging paradigm using components of Altova's MissionKit for Software Architects, [3] Mitin A. I., Semenov D. M. - Web technologies for integration of applications in a training situation center environment, Automatic Documentation And Mathematical Linguistics Journal, Volume 43, Number 1, pp , 2009,ISSN: [4] Bâra A., Botha I, Diaconita V., Lungu I., Velicanu A., Velicanu M. - A model for Business Intelligence Systems Development, Informatica Economica Journal, Volume 13, No. 4/2009, pp , ISSN ISBN: