[Paper Review] Framework to Integrate Business Intelligence and Knowledge Management in Banking Industry

G. Koteswara Rao, Roshan Kumar|arXiv (Cornell University)|Sep 3, 2011

Big Data and Business Intelligence2 references21 citations

TL;DR

This paper proposes a unified framework integrating Business Intelligence (BI) and Knowledge Management (KM) in the banking sector to enhance decision-making by leveraging structured data and unstructured knowledge from employees. By combining BI's data-driven insights with KM's collaborative knowledge sharing, the framework improves organizational performance and strategic alignment through integrated knowledge discovery and distribution.

ABSTRACT

In this digital age organizations depend upon the technologies to provide customer-centric solutions by understanding well about their customers' behaviour and continuously improving business process of the organization. Business intelligence (BI) applications will play a vital role at this stage by discovering the knowledge hidden in internal as well as external sources. On the other hand, Knowledge Management (KM) will enhance the organisations performance by providing collaborative tools to learn, create and share the knowledge among the employees. The main intention of the BI is to enhance the employees' knowledge with information that allows them to make decisions to achieve its organisational strategies. However only twenty percent of data exist in structured form, majority of banks knowledge is in unstructured or minds of its employees. Organizations are needed to integrate KM with Knowledge which is discovered from data and information. The purpose of this paper is to discuss the need of business insiders in the process of knowledge discovery and distribution, to make BI more relevant to business of the bank. We have also discussed about the BI/KM applications in banking industry and provided a framework to integrate BI and KM in banking industry.

Motivation & Objective

• Address the challenge of integrating unstructured knowledge from employees with structured data analytics in banking.
• Improve organizational performance by aligning BI insights with enterprise-wide knowledge sharing.
• Bridge the gap between data-driven decision-making and human expertise in financial institutions.
• Develop a practical framework for implementing BI and KM integration in real-world banking environments.
• Enhance the relevance of BI by incorporating business insights from domain experts during knowledge discovery.

Proposed method

• Propose a conceptual framework integrating BI and KM systems in banking organizations.
• Utilize BI tools to extract actionable insights from structured internal and external data sources.
• Incorporate KM practices to capture, store, and share unstructured knowledge from employees.
• Integrate business insiders (domain experts) into the knowledge discovery process to validate and enrich BI outputs.
• Design collaborative workflows that connect data analytics with knowledge sharing platforms.
• Align the framework with strategic banking objectives through feedback loops between knowledge users and data analysts.

Experimental results

Research questions

• RQ1 How can business intelligence and knowledge management be effectively integrated in the banking industry?
• RQ2 What role do business insiders play in enhancing the relevance of BI-derived knowledge?
• RQ3 How can unstructured knowledge from employees be systematically captured and combined with structured data?
• RQ4 What framework components are essential for aligning BI insights with organizational strategy in banking?
• RQ5 How does integrating KM with BI improve decision-making and performance in financial institutions?

Key findings

• The integration of BI and KM significantly enhances the relevance and application of data-driven insights in banking decision-making.
• Business insiders contribute critical context that improves the accuracy and strategic alignment of BI outputs.
• Only 20% of organizational data is structured, highlighting the need to integrate unstructured knowledge from employees.
• The proposed framework enables a feedback-rich environment where knowledge is continuously refined through collaboration.
• The integration supports improved organizational performance by linking data analytics with human expertise.
• The framework provides a scalable model for implementing BI and KM integration in regulated, data-intensive environments like banking.