a soft computing technique for transshipment management decisions ...

Journal of Economic Development, Management, IT, Finance and Marketing, 4(1), 49-63, March 2012

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An Intelligent Decision Making Architecture for Banks: Business Intelligence And Knowledge Management Systems Integration G. Koteswara Rao and Shubhamoy Dey Information Systems, Indian Institute of Management, Indore, M.P, India [email protected]; [email protected]

Abstract In the modern business context, the information needs of the banking industry are undergoing a transformation. Growing out of business intelligence led analytics, the need for a more flexible human user oriented information creation and sharing platform is emerging. In this paper, we have provided the conceptual framework for new product development (NPD) plan for banks through the integration of Business intelligence (BI) and Knowledge Management (KM). The paper discusses the applications of BI and KM and also the status of ICICI bank and public sector banks’ (PSB) information systems infrastructure and their preparedness to adopt an integrated architecture for supporting complex decision-making. ICICI bank example helps to understand integration plan, if BI and KM practices are in place. PSB example helps us to understand integration plan, if KM practices are not in place. One can start a KM for BI and extend it throughout the organization. Keywords: Decision Support, New Product Development, Business Intelligence, Knowledge Management, Information Sharing

Introduction Globalization has changed the strategic context for business. It is this connectedness, and its complexity, that is also increasing the source of disorder, engulfing nearly all industries (and all nations). At the same time, the dynamics of information production, dissemination, storage, display and retrieval have changed radically to the point where significant amounts of information are produced and obtained well outside the control of any organization. As the cost of processing and communications power has tumbled, it has become cost-effective for organizations and individuals to adopt and utilize information technologies (Singer G, 2006). To address the difficulties of globally dispersed business units and to balance local autonomy with global control, organizations have resorted to the use of advanced technological solutions. Advanced technologies are also making the organizations provide products and services to its customers in an effective manner. Therefore the organizations themselves are becoming more technology-oriented. Survival and sustainability strategies to keep pace with the change are being manifested in several ways in the banking industry through employee empowerment, process improvement and applications of technology. Today's customer is busier, socially better connected, better


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informed and inquisitive. Therefore, banks need to understand their customers thoroughly and offer products and services to match their requirements. To acquire new customers, retain existing customers and to find the new ways to operate the business, banks need to provide the consolidated view of its operations to their decision makers. As a result, BI came into existence. BI system brought the perception of knowledge discovery and bankers have quickly adopted to support the decision making process for business decisions. KM, on the other hand, helps an organization to gain insights and understanding from its own experience. Specific KM activities helps to focus the organization on acquiring, storing and utilizing knowledge for such things as problem solving, dynamic learning, strategic planning and decision making integrity (Richard T, 2005). KM system emphasizes the creation of novel knowledge and the timely applications of organizational knowledge to maintain strategic advantage. We have discussed the importance of BI and KM applications for banks, provided a framework for new product development in banks after BI and KM Integration. Further, we have proposed a model for ICICI bank and Public Sector Banks in India to Integrate BI and KM.

Applications of Business Intelligence Banking Industry

A BI system has an evident importance as a communication and information diffusion channel, preferably one that is open, trustworthy, transparent and permanent. In supporting the monitoring and evaluation of business results while maintaining information integrity (Petrini & Pozzebon, 2009). Zeng et al (2009) define BI as ―The process of collection, treatment and diffusion of information that has an objective, the reduction of uncertainty in the making of all strategic decisions‖. Experts describe BI as ―business management term used to describe applications and technologies which are used to gather and provide access to analyze data and information about an enterprise, in order to help them make better informed business decisions (Zeng, et al, 2009)‖. The increased importance of BI infrastructure reflects three interacting trends, 1) More turbulent global environments, 2) Additional pressures to unveil valid risk and performance indicators to stakeholders, and aggravated challenges of effectively managing the densely interwoven processes. Unless banks have the complete profile of a customer from the integrated database, they cannot effectively differentiate between ‗good‘ and ‗bad‘ customers. Data warehousing technology can help to make by integrating various sub-systems into a BI framework. As per Ranjan (2008), BI is the conscious, methodical transformation of data from any and all data sources into new forms to provide information that is business-driven and results-oriented. It often encompasses a mixture of tools, databases, and vendors in order to deliver an infrastructure that not only will deliver the initial solution, but will incorporate the ability to change with the business and current market place (Ranjan, 2008). Hocevar et al, (2010) found that the main categories of BI benefits can be successfully linked to the defined long-term business strategy. Therefore, the investment helps the organization to achieve its strategic objectives and is crucial criteria for deciding whether the investment in BI is justified (Hocevar, et al, 2010).


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In a typical bank, BI would centralize the customers‘ information to provide valuable insights to decision makers to improve the efficiency and provide better customer support. Applications of BI in banks can be summarized as follows (Curko K, et al, 2007; Koh & Chan, 2002; Madan B, 2006; and Decker, 1998): Marketing: The bank‘s marketing department can use data mining to analyze customer databases and develop statistically sound profiles of individual customer preferences for products and services. Products/services can be offered by understanding the customers‘ requirements which ultimately lead to saving money on promotions and offerings that otherwise be unprofitable. Risk Management: BI is widely used for risk management in the banking industry. Bank executives need to estimate the risk of lending associated with their customers. Lack of knowledge regarding customers‘ risk profile may prove to be a great disadvantage while offering new customers credit cards, extending existing customers lines of credit, and approving loans. Construction of models to give signals of possible transactions on stolen credit cards: for example, card theft analysis showed that number of transactions increases rapidly after the theft. By comparing expected average number or value of daily transactions, the authorization system can issue an early warning. Customer Segmentation: Customer segmentation is to analyze customer characteristics and behaviours with an appropriate criterion, the benefits are valuable for the bank to improve its services. It helps to understand customer needs/ sentiments about banking products/ services, and as a consequence, develop, implement and offer new market-leading products/ services to gain & maintain competitive advantage. Effective customer segmentation can help to uncover the ideal / most profitable customer profile. Fraud Detection: Being able to detect fraudulent actions is an increasing concern for many businesses; and with the help of BI system, fraudulent actions can be detected and stopped. Customer Acquisition & Retention: It can be used to study customers‘ past purchasing histories to know what kind of promotions and incentives can help the bank to reach the targeted customers. Cross-Selling: The new mantra of marketing in banking is ―the right product to the right customer at the right time‖. Banks can construct models to predict the probability of selling certain products/ services in order to facilitate cross-selling. Advantages of having correctly estimated probabilities are two-fold: lowering the marketing campaign costs along with a high response rate, and, raising the quality of customer relations. Customer Lifetime Value: Customer lifetime value management estimates expected revenue from each customer in the future period. It is expected that a person with high education has higher income and is willing to meet the expense of additional products. The BI can build models for expected client lifetime value, so that bankers can treat clients accordingly, considering client‘s profitability based on his complete history. Information extracted from BI needs to relate with business goals and objectives as well as business processes. Unfortunately, these aspects are poorly supported by BI. The developers of


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the BI have a data-centric viewpoint of business operations, rather than a process-centric perspective (Kubheka, 2007). KM System will play an important role at this stage. KM system puts the information into a business context, improves the business decision-making and actiontaking process. Flexibility and open architecture allow for easy expansion of the BI system. It is necessary in a situation when there are new informational needs or when an amount of information to be processed remarkably increases. Through KM system employees can send their feedback, need for improvement and add new information to the BI Solution. Implementation of BI applications usually takes time to develop and perfect, it also needs to modify the existing applications or add new applications as per the change in the market or business process. KM system helps to interact with the end-user during the development and enhancement phases to increase their satisfaction level with the system. Knowledge Management End-users are required to use their experience/knowledge to make the decisions after receiving the information from BI. Additionally, the decision making process may require interactions with others. KM enhances these interactions among the participants in a decision process by providing a platform for collaboration. In other words, KM systems serve as tools for collaborators to gather and exchange information regarding the organizational, social and environmental influences affecting the decision. Nonaka‘s and Takeuchi (1995) have developed a knowledge spiral model to represent how implicit and explicit knowledge interact to create an environment for organizational learning. The framework for organizational learning identifies four knowledge conversion processes or patterns (Nonaka‘s & Takeuchi, 1995): A. Socialization (implicit to implicit) B. Externalization (implicit to explicit) C. Combination (explicit to explicit) D. Internalization (explicit to implicit) KM is the practice of adding actionable value to information by capturing implicit knowledge and converting it to explicit knowledge; by filtering, storing, retrieving and disseminating explicit knowledge; and by creating and testing new knowledge. In this context, implicit knowledge includes the beliefs, perspectives, and mental models so ingrained in a person‘s mind that they are taken for granted (Nonaka & Takeuchi, 1995). KM is primarily a human and process issue; once these two perspectives were addressed, technology can then actively support KM. ―The concept of KM technology is less concerned with any degree of technology sophistication and more concerned with the usefulness in performing knowledge work in and between organizations. KM can be perceived to have six dimensions: 1) creating knowledge, 2) acquiring knowledge, 3) organizing knowledge, 4) saving knowledge, 5) disseminating knowledge and 6) applying knowledge. Lee (2000) writes that: "inclusion of human's collaboration and help is the factor that distinguishes knowledge from corresponding data and information with it and this adds more value to the individual to whom knowledge is transferred". KM improves the knowledge workers‘ performance, process performance,


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employee performance, market performance, and organizational performance (Cebi F, et al, 2010). Knowledge Management in Banks Maryam B et al (2010) have studied the KM practices and experiences of Iran banks. Their study shows that informal training is the main source of communication for sharing knowledge. Working on the other aspects such as IT systems, for the ease of strong and sharing experiences or lessons learned are useful. The study elaborates on capturing knowledge from industrial resources in three investigated banks, such as industrial associations, competitors, clients and suppliers. It showed that banks adopt themselves with the changing environment and can be more proactive than reactive (Maryam, et al, 2010). Benefits of KM for banking industry can be summarised as follows (Chen Y, et al, 2006; Yamagata K, 1989; and Ammary A, et al, 200): A. KM can help banks to have a well defined placement policy to ensure that the right person is on the right job to meet the growing challenges. Frequent job rotation also contributes to the creation of internal knowledge. B. In this global age, the silver lining for any organization is the younger generation, who with their present day higher qualifications, is by and large prepared to shoulder higher responsibilities. What is required is a lot of mentoring by the experienced and successful superiors. Experience shared by senior/skilful employees will make a greater impact on this matter. C. Banks need to continually create new products and value for their customers as their preferences have become more diversified in recent years. It is important for banks to track those preferences and relevant information. In other words, banks need to make the best use of their customers‘ information & the knowledge inherent in their employees. Therefore, banks need to acquire knowledge from internal as well as external knowledge sources. D. The ability to learn across the group and form an effective team for a specific project/ assignment is a powerful tool. KM will provide a building environment by encouraging collaborations and push mechanisms. KM will also offer a general discussion forum, where employees can share/discuss/talk about the economy, trade, finance and other related topics. KM will substantially enhance the productivity of individuals and groups in a bank by allowing to use and reuse of knowledge, and doing strategic tasks such as customer relation management.


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Integration of BI and KM While traditional information systems and BI convert data into information, and then into knowledge that finally meets the needs of its users, KM systems are designed for extracting knowledge from data and information. KM puts more emphasis on the knowledge itself and improves the utilization process of BI (Zhang L, et al, 2009). From the point of view of performance of the BI functionality, such a system can be thought to form an intelligence cycle, representing a continuous process that can be improved through feedback. This feedback process helps management to understand the end-user expectations, requirements and their usage patterns. The most critical element in the intelligence cycle is the determination of the intelligence requirements because this determines the nature of the information/BI system (Baars H, 2005). With the help of BI and KM, global businesses are increasingly successful. KM is deployed across a network of social and technical, human and material components. In the global economy, KM is a form of intercultural management (Richard T, 2005) and is designed to assist the interpretation of business cases by providing expertise, and global domain knowledge. The level and quality of BI and KM integration is vital to sustain competitive advantage. The benefits of integrating of BI with KM are 1) ensure a real support in deploying successful businesses across the organization by smoothly managing multicultural teams of employees in providing highest quality products and global services to multicultural customers, 2) end-users preference and experience can be included in BI implementation, and 3) provide better understanding on business context, interpretation results and training to the end-user. Though both of them differ in their objectives and technologies used to develop them, together they can improve the organizational performance. BI and KM integration assists today's managers in improving/optimizing decision making process by sharing data and information across the organization; getting the details from internal and external sources; and forecasting the future trend and taking better decisions. As per Hiltbrand T (2010), adoption of BI with social practices can improve our ability to participate with information to produce significant and strategic corporate outcomes (Hiltbrand T, 2010). BI focuses on explicit knowledge, but KM encompasses both implicit and explicit knowledge. Both concepts promote learning, decision making, and understanding. Yet, KM can influence the very nature of BI itself (Richard T, et al, 2005). Researchers have proposed knowledge warehouse (KW) architecture as an extension to the BI model. The KW architecture will not only facilitate the capturing and coding of knowledge but will also enhance the retrieval and sharing of knowledge across the organization The KW proposed suggests a different direction for BI. This new direction is based on an expanded purpose of BI (Richard T, et al, 2005; and Nemati, 2002). That is, the role of BI in knowledge improvement. This expanded role also suggests that the effectiveness of a BI will, in the future, be measured based on how well it promotes and enhances knowledge, how well it improves the mental model(s) and understanding of the decision maker(s), and thereby how well it improves their decision making and hence firm performance (Liu, P.L., et al, 2005). As per Zhang L, et al (2009), there is no framework of KM technology to support the delivery of original analytical knowledge generated from BI, which to some extent means that the way of incorporating knowledge derived from BI into knowledge management systems remains unexplored. They have proposed A4T transformation model involving data, rough knowledge, intelligent knowledge, and actionable


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knowledge, as well as the research direction, content and framework for future intelligent knowledge management (Zhang L, et al, 2009). There are three levels of integration between BI and KM (Baars, 2008): A. Presentation level integration provides a horizontal integration with a joint user interface, B. Data level integration provides the content of KM systems for BI processes by storing the related metadata into data warehouse, and C. System level integration provides distribution and re-utilization of BI analysis models by a knowledge management system. BI and explicit KM technologies deal with a subset of the KM model. Ideally, KM will deal with both explicit as well as implicit knowledge and the interaction between them. This idea may be suitable for the organizations that have a well established KM practices. But in case of the organizations that do not have the basic KM practices in place, and in the process of implementing a BI, KM can be initially implemented for BI and later that can extended to include other components. In the following sections we discuss this aspect in the context of two examples of BI and KM integration, 1) an organization that is practicing KM, and 2) an organization that is not practicing KM. Integrate BI and KM in Banking Industry Scott et al (2001) have shown the extensive role that domain knowledge plays in every step of the BI Implementation process. In this case, the information was supplied by banking domain experts. The aim is to integrate, BI technologies to enable banks to increase profitability by the improved use of the vast amounts of customer-related data they hold. The interpretation of BI results is another step in the BI implementation process that relies heavily on domain knowledge. Often, this interpretation is based on the intuition of a domain expert and is therefore difficult to model (Scott, et al, 2001). KM play a role on all four processes of Nonaka‘s model whereas BI plays a role on combination, i.e., BI will transform the dirty data into explicit/codified knowledge. But the experience of the end-users on BI usage can be called as tacit and KM will help end-users to share their experience by converting that into codified /explicit knowledge.BI deals with a subpart of the KM, without BI it is not possible to get that knowledge, so the Integration of BI and KM will provide a more value. Banks that have the well established KM practices would not only capture the knowledge from BI solutions, but also from other sources. KM captures the structured and un-structured information from internal as well as external sources. Table 1-Knowledge acquisition

Internal sources External sources

Structured Information BI, SCM,HRM, and CRM etc Industry reports, stock market reports, and other content and databases

Unstructured information Experience, Innovative ideas, and documents etc Media, RBI, IBA, world bank and other external sources, banking agencies and blogs and social networks


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Integrated BI and KM provide the robust system with the capability of process-driven decision making. Employees may analyze potential growth and profitability of customers, and reduce the risk exposure through more accurate financial credit scoring of their customers.

CRMemployees

Other employees

KM system

D A T A

Business Intelligence (ETL, DW, OLAP and DM)

R&D-employees

Top Management

Proposed NPD plan

Discussion forum / Online survey

Customer survey (Targeted/Existing)

Sales & Marketing employees

Survey form

Structured /Unstructured data

Intelligence and statistical tools (IST)

Customer profiling / cross-selling / risk-free customers/ and so on

Plan for new product development plan

New Product Launch

Offer product to the customer through various channels

Figure 1. Skeleton for New product development in banks after BI and KM Integration

It is important for banks to provide customer-centric products/services to sustain and get the competitive advantage. To achieve this, banks need to understand their customer needs and wants. Researchers have discussed about the importance of considering customers experience and expectations in NPD process. Furthermore, they have showed a BI approach for extracting knowledge from customer‘s information to support decision making during NPD process (Jae B,


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2011; and Shu-hsin L, et al, 2010). Knowledge extracted from BI can enable the implementation process of innovative products/services. Customers have to be treated as partners and a main source for knowledge. Banks need to make the customers to participate in knowledge creation process (Zenab R, 2009). As per Reidenbach R (1986), better structured and more fully developed NPD programs may be a function of better management, which also manifests itself in the bank‘s better performance. The nature of this relationship merits further investigation to better understand the contribution that NPD makes not only to the retail bank segment but also to all other areas of the industry (Reidenbach & Moak, 1986). Liu P (2005) study indicated that the use of KM method in NPD strategy exhibited a positive effect on NPD process performance. It was concluded that, ―the stronger the KM method, the more complete the NPD strategy, the better the NPD performance‖ was proven significantly (Liu, et al, 2005). HOEGL, et al (2005) has demonstrated that there are a number of KM methods that have the potential to strongly support knowledge creation in NPD projects. They have also explained how ten such methods affect different modes of knowledge creation (i.e., socialization, externalization, combination, internalization) and provide examples of how companies have successfully deployed those projects (Hoegl & Schulze, 2005). Figure 1, depicts our proposed process of launching a customized product for a suitably configured ‗Integrated Intelligence Architecture‘. Research and Development (R&D)-Center and Top Management will have a way of continous monitoring of market changes, technology advancments, and will be able to propose a new product model by adding their experience and innovative ideas. The proposed product plan can be made available to the employees of the bank through the KM system, so that they can send their feedback and discuss about the same on the A survey can also be conducted among the targeted/existing customers. Use Text Mining and other statistical tools to procees the structred/unstructured data gathered from the KM system and customer survey results can be used to develop the final plan for the new product. Finding the profitable but low-risk customers is a challenging task for marketing team. This can be done with the help of the information provided by the BI system. Example 1: ICICI bank ICICI bank, India‘s second largest bank and largest private sector bank by market capitalization. It provides a broad spectrum of financial services to individuals and companies. This includes mortgages; car and personal loans; credit and debit cards; and corporate and agricultural finance. ICICI bank has its own BI unit with the objective of ―leveraging analytics to aid management‘s decision making‖. This unit supports the bank by providing continuous decision support to minimize risk and maximize profit. The foundation for ICICI Bank‘s wide-ranging customer relationship management is a Sybase IQ-based data warehouse (The Bank initially used Teradata as its data warehouse platform and migrated to Sybase IQ) and developed specifically for analytics and BI. The first iteration of the warehouse in 2000 generated a wealth of insights that enabled the bank to build customer intimacy, reduce churn, and offer cross-sell and up sell promotions. ICICI Bank deployed PowerCenter in 2003 as it embarked on the next phase of its warehouse, which would add data from five new sources, in addition to the initial three sources of retail banking, credit cards, and securities information. PowerCenter‘s easy-to-use drag-anddrop interface, native connectivity to a wide range of data sources, and standards-based architecture have helped ICICI Bank‘s internal IT personnel rapidly develop expertise in


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Informatica-based data integration momentum (Source: ICICI bank-Success story 2009). In September 2005, ICICI Bank selected SAS to replace the existing disparate reporting systems in various divisions with a Single Enterprise-wide Framework. It includes SAS ETL Server, SAS Enterprise BI Server, and SAS Enterprise Miner. According to Vohra, "Adoption of SAS in ICICI Bank is in line with our strategy to consolidate our BI framework and establish an enterprise wide BI platform. With the SAS Data Integration Server it will now be possible for us to integrate our data sources across the enterprise (Source: Success story 2007).

Retail banking sys Credit cards sys

E T L

Data warehouse (Data Model, Metadata, and Relations)

Ad-hoc /Query based reports Summarized Old data

Data Mining Bonds system

Web Trade sys

OlAP reports

Business Intelligence

Demat system Define Problem Fixed deposit sys system, and loans data Loans system

BI and other IT sys Section wise experts voice, Analyses and observation, Feedback /experience, FAQ, Best practices, Information of Modified / Changed reports, etc

BI solution Business process

Knowledge of market Knowledge of customer Knowledge of organization Knowledge of employees Knowledge of competitors And so on

KM system

Document sharing, Groupware, Taxonomy, Metadata repository, E-learning Community of practice People to people Person to person

Information/ Innovative ideas Employee of the Bank

Stock Market/ e-trading

Market Research/ Associations

Media/ Industry Reports

Figure 2. Architecture for ICICI bank to Integrate BI and KM KM portal named ‗Wiseguy‘ at ICICI India began on an experimental basis and carried on expanding and exploring, widening its ambit of operations. To develop ‗WiseGuy‘, a team was put together encompassing KM, HR, technology and research with a brief to ‗just do it‘. Indeed


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they did and a beta version was ready within just three months. The requirement of relatively young age group of employees to achieve an understanding of the working culture and support provided by the top management had led to the progress of the concept of KM. To a large extent, it is seen that the benefits of implementing ‗Wiseguy‘ fulfils the needs of the technical and professional workforce by giving them a platform for airing their views, contributing as well as upgrading their expertise. It has also achieved in being a learning organization. KM at ICICI Bank was started in a non-dictatorial manner and its use is voluntary, but a programme of this nature cannot be expected to continue the momentum without some sort of ‗official‘ recognition (Chandana, 2008). To be successful in today‘s dynamic business environment, ICICI has to continually improve and upgrade its BI system. Employees require information at all levels of the organization for ongoing decision making processes. Integration of BI and KM (Figure 2) increases the usage of the knowledge generated through BI system. This allows top management to understand the end-users perception and make further changes in BI system, if required. Though KM was started with the initiative taken by young age group employees with top management support, later the middle level employees realizing the benefits started using it. It may face problems in future as it is not getting upgraded in a strategic manner. They need to have a meta-data repository, which supports to maintain KM repository in a systematic way and helps users to find the required information in an effective manner. Example 2: Indian Public Sector Banks The customer-orientedness and competence of employees are the two most important service quality factors, in the context of the Indian retail banking sector and what is most important is the provision of competent service, caring, individualised attention to the customers, employees' knowledge and courtesy, and the ability of the firm and its employees to inspire trust and confidence (Manabendra & Choudhury, 2009). Though PSBs in India have been using the technologies to reach the customers, adoption of KM in PSB is yet to be explored. PSB in India have adopted Core Banking Solution (CBS) and it has helped the customer to carry out 'Anywhere Banking'. It consisted of an integrated suite of applications such as customer information system, deposits system, loans system and transactions processing system. CBS was essentially a transaction based system, and it was primarily designed for day-to-day operations at the branch level and generation of reports from transaction data. It was not developed to solve the specific decision making problems of the employees, managers and executives of the bank. CBS and modernization of business processes had allowed the banks to centralize computerized processing and operations functions, thereby enabling it to offer new banking products to customers all over India; reversing the trend of customer attrition. The next move forward can be the integration of various systems to create a centralized database consisting of customer information and other data. PSB can make use of various tools and techniques to analyse this data for enhancing their businesses. Individual systems help in running the business of banks, but if the banks have to optimize their businesses, they have to invest in BI technology. Many banks in India already have adopted this technology (Anand S, 2011).


Customer Information system

Business Intelligence (ETL, DW, OLAP and DM)

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BI solution

Loans System Product management system

Transactions processing system

Structured information

Innovations Central office IT-Center (Data Center) Zone/Region/Branch etc

Bank‘s Business process (Activities)

KM system

Decisions

Figure 3. Architecture for PSB bank op integrate BI and KM BI system could be accessed by the employees, managers and executives of the Public sector banks (PSB) in a hierarchical fashion, i.e., end-users from IT-Center and Central office can access the bank as a whole; users from different countries, zones/regions/branches can access the information which is related to their particular country, zone, region, or branch. Executives/ employees from IT-center and Central Office can understand well about the technical aspects of BI, but not the users from regional, zonal or branch offices. Hence, users may not get the information they required and this process can take significant time depending on the change process initiated by the bank. However, banks can get positive result through 1) Proper guidance and continuous support from the IT and domain experts, 2) Information sharing within the bank. For example, Technical team members can provide solution for the technical problems and provide brief descriptions about the critical reports on KM. Technical experts can visit the KM portal weekly to answer technical queries. Feed back of end-users can be received and modify existing BI system. The biggest problem for employees is to find the answer to specific questions, for this expert from each department of the bank can be asked, 1) to share their innovative ideas about the information that is available within system and 2) to encourage users & promote the system. Knowledge and information sharing can be made the culture of the bank. KM system captures explicit and tacit information from employees, internal systems and external systems. Users of BI can make use of the Knowledge shared by other users though KM to make better decisions. They can also share/discuss about the problems or innovative ideas through KM. Top management can go through these discussions and identify the need & market trend to make required changes in the BI solution. As the public sector banks don‘t have the existing have the KM practices, they can start a KM for BI and extended to the firm wide after increasing the awareness among the users.


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Conclusion Integrated BI and KM architecture provide the robust system with the capability of process-driven decision making. The processes are stored in process model base and their flexibility and reuse help enterprises improve the speed and effectiveness of business operations (Lee 2000). Employees may analyze potential growth and profitability of customers and reduce the risk exposure through more accurate financial credit scoring of their customers. This helps to solve problem related to churn management and helps to analyze why and how customers had left or are likely to leave. End-users can identify their most profitable customers and the underlying reasons for those customers‘ loyalty, as well as identify future promotional schemes for customers. To further this research, one can choose a bank to conduct case study based research with the help of primary data to draw useful elements, which are not possible with the secondary data. It is also evident that the integration of BI and KM will provide positive results for the banks. Therefore, banks can follow ‗Intelligent Decision Making Architecture‘ while developing new product or while making changes in the existing products/services. One can imitate the ‗Integrated Intelligence Architecture‘ to form new policies or changes to the existing policies within the organization. Each bank has different organisational structure and customer base, so they need to study their existing BI and KM practices before the integration of BI and KM. References Ammary A, & Hassan J. (2008). Knowledge management strategic alignment in the banking sector at the Gulf Cooperation Council (GCC) Countries, PhD thesis, Murdoch University. Anand, S. (2011). Banking on Technology. Seminar for Directors of banks on ‗IT Governance, Technology Management and Data Warehouse/CRM Cyber Security‘, IDRBT. Baars, H. (2005). Integration von Wissens management- und Business-Intelligence- Systemen – Potenziale. WM2005: Professional Knowledge Management – Experiences and Visions. Deutsches Forschungszentrum für Künstliche Intelligenz DFKI GmbH, pp 429-433 Cebi, F., Aydin, O. & Gozlu, S. (2010). Benefits of Knowledge Management in Banking. Journal of Transnational Management, 15(4), 308321.doi:10.1080/15475778.2010.525486 Chandana, G. (2008). Knowledge Management in India: A case study of an Indian bank. The Journal of Nepalese Business Studies, 5(1), pp 37-49 Chen Y, Li L. (2006). Deriving information from CRM for knowledge management—A note on a commercial bank. Systems Research and Behavioral Science, 23(2), pp 141–146.


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