BI in Banking, Financial Services, and Insurance

BI in Banking, Financial Services, and Insurance
Technology is transforming the banking and finance industry. Thanks to the Internet and the proliferation of mobile devices and apps, today's financial institutions face mounting competition, changing client demands, and the need for strict control and risk management in a highly dynamic market.
At the same time, technology has given rise to powerful business intelligence tools. Tools that the banking and finance industry can use to leverage customer data for insights that can lead to smarter management practices and better business decisions. To that end, here's a look at some of the ways banking and finance institutions are using Business Intelligence (BI) solutions to drive profitability, reduce risk, and create competitive advantage.
Business intelligence (BI) mainly refers to computer- based techniques used in identifying, extracting and analyzing business data, such as sales revenue by products and/or departments, or by associated costs and incomes. BI technologies provide historical, current and predictive views of business operations. Common functions of business intelligence technologies are reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, and text mining and predictive analytics. A Data Warehouse is one of the most valuable things for Business Intelligence System or Data warehouse rises and effective use can help decision-making intelligently that can improve the operations of Business Intelligence System or Data warehouse rises notably. It provides a collection of integrated data for on- line analytical processing (OLAP). A data warehouse is "a subject-oriented, integrated, nonvolatile, and time- variant collection of data in support of management's decisions".
Here, Business Intelligence System or Data warehouse is:
  • 'Subject-oriented' means the data are arranged and optimized to provide variety of analysis requirements from diverse functional departments within an organization.
  • 'Integrated' means the data warehouse combines operational data derived from different departments & strategic business units of the organization. It can use consistent naming conventions, measurement standards, encoding structures and data attribution characteristics.
  • 'Time-variant' means the data are periodically loaded to the data warehouse, all time-dependent aggregations need recomputed.
  • 'Non-volatile' means Data warehouse are static. Data in the warehouse system are read-only generally; data in the database are rarely changed. Data in the warehouse database are updated or refreshed on a periodic, incremental or full refresh basis
Information is one of the most valuable assets of Business Intelligence System or Data warehouse rises and when utilized properly can help Decision making intelligently that can improve the operations of Business Intelligence System or Data warehouse rises significantly. Data Warehousing is a technology that allows information to be easily, efficiently, timely and accurately accessed for decision-making purposes. A data warehouse can be viewed as a very large database that integrates the data stored in several different operational data sources. The operational data sources are usually maintained separately to support daily on- line transaction processing (OLTP).
Data Warehouse
With the huge potential of data warehouse applications, many publications on data warehousing research have appeared in the past ten years. However, to the best of our knowledge, no systematic review and classification of these literatures have been done. In this study, two hundred and thirty-one articles were reviewed and classified based on Business Dimensional Life Cycle. This study provides as a beginning for understanding of data warehouse research for readers interested in this area. For academics, it helps to review the historical trend of published data warehouse articles and to explore potential research areas for future study. For practitioners, it helps companies to understand the potentialities and possible issues need to be considered in data warehouse implementation projects.
Data Design
The data design phase design multidimensional models to hold aggregated data for queries which is defended with the company in customize way.
The category is dedicated to the architect design of data warehouses. It consists of five subcategories: novel architecture, DW software, the design of Meta data, security of data warehouses, and guideline of product selection.
  • Novel architecture: The subject includes the topics of advanced architecture design that different from traditional data warehouse architecture such as moving the data warehouse to the Internet architecture.
  • Data warehouse software: It includes commercial data warehouse systems for companies to implement and some developing systems for academic research.
  • Metadata: Metadata is like an index for the warehouse contents that tracking of what data is where in the warehouse. Metadata maintenance is an important issue, because it has influence on the entire warehouse from initial model through data extraction and load processes to the exploration and access of users.
  • Security of data warehouses: The security issues are important to the data warehouse since many important data are collected in the system. The data warehouse must provide a mechanism to help user access data. The issues include data encryption, authentication, authorizations, etc.
  • Product selection: Given various design and architect on the market, the subject discusses a formal procedure to decide which product fits company need better. The factors needed to be considered including price, training, and maintenance services, etc.
The realization phase transforms the logical design of a data warehouse project into physical implementation. The details of physical implementation vary according to different applications and size of projects. This phase includes five subcategories: physical design of data, data staging, query processing, data quality and applications.
  • Physical design: The phase converts logical data design into physical database. One of the techniques used to improve data warehouse performance is the creation of set of materialized view. A data warehouse stores integrated information from multiple data sources in materialized views over the source data. Materialized views are used to pre-compute and store aggregated data such as sum of product sales or are used to pre- compute joins with or without aggregations.  They are employed to reduce the overhead associated with expensive joins or aggregations for complex and time- consuming queries. The research topics of materialized views include view selection, view maintenance and view synchronization.
  • View selection: Normally, the data warehouse system cannot materialize all possible views due to the constraints of some resource such as disk space, computation time and maintenance cost. Accordingly, how to select an appropriate set of views to materialize under limited resources has significant effect on query processing performance.
  • View maintenance: When the data in any data source changes, the materialized views in the data warehouse need to be updated consistently:
  • Data staging: The process collects operational source data and integrates the data into data warehouse. It consists of three major steps: extraction, transformation and load (ETL). Extraction is the process of retrieving data from a variety of sources.
  • By modifications, validations and conversions of the source data, transformation makes sure the data is in a consistent state. Loading data is the final step of the ETL process; it loads quality data into the warehouse.
  • Query processing: Data warehouse typically involves the execution of complex queries with join, group- by, and sort operations for a large volume of data. To support these kinds of queries, a large variety of query processing techniques are used to increase the query performance.
  • Data quality: Since data quality will impact on the credibility of data warehouse. To ensure quality data in the warehouse, the data gathering process and full lifecycle of data warehouse must be well designed.
  • Applications: Data warehouse can be applied to many areas and industries for better decision making. The applications cover health care management, construction management, marketing and web data, etc. The applications of a data warehousing are seen to have considerable potential for different usage in the future.
Deployment and Maintenance
Deployment is to deliver the data warehouse related technology, data and application to end-users along with necessary education and support. End user education must match the role the users play. After successfully deploying a data warehouse, the attention should be focus on the ongoing support and education for operation of the warehouse and future growth. As data warehouse is a type of IS, the user satisfaction is applicable to measure the success of data warehouse.
Banking, financial services and insurance industries are sophisticated and complex. These industries are subject to significant regulation and they generate mountains of data. For the enterprise that participates in one or more of these markets, it can be difficult to stay abreast of changes in the market, changes in competition, and customer and regulatory changes.
Change is more rapid than ever before, and the enterprise that competes in these markets must find new and better ways to attract and retain clients and to ensure compliance with industry and government regulations. Strict national and international governance and regulation, and merger and acquisition activities require dependable strategies to ensure legal and industry compliance.
The enterprise must also achieve an integrated view of data across all systems and data sources in order to provide up-to-date data for analysis and decisions. At the same time, these enterprises must focus on a secure transaction environment to ensure compliance and protect confidential and private data.
The business user must monitor product, transaction and fraud and risk data on a daily basis to leverage the full value of data, and capitalize on market opportunities. The identification and analysis of risk is crucial to the success of a financial industry enterprise. Users must have the ability to view and analyze data and to make swift corrections to strategy, processes and objectives with a full understanding of strategy, client portfolio, customer demographics, and current and planned profitability.
To be successful in this environment, an enterprise must establish and monitor appropriate key performance indicators (KPIs) and metrics, identify and support profitable customers, improve operations at the grass roots level and achieve true business intelligence to understand and improve portfolio performance and predict and forecast results based on trends and business patterns.
Business Intelligence solutions help enhance the operations for banking and financial sector by identifying, analyzing, addressing and resolving issues in real-time and more. While many financial institutions have already started benefitting from the BI, the full potential of this technology is yet to be discovered. Real-time business intelligence makes information processing convenient and for decisions to be made faster with accuracy.
Why Should Financial Companies Focus on Business Intelligence?
BFSI domain faces the challenges of rising competition, risk management, and changing customer demands. Business intelligence tools aid them to leverage customer data for deriving useful insights. They help with analyzing trends, identifying patterns, and equipped with real-time reporting. BI offers them with a flexible and transparent approach to make better financial operations and decisions.
How Business Intelligence Drives Profitability in Banking, Financial Services & Insurance Industry
Listed are some of the pointers that highlight how BI in banking & finance sectors offer a definite competitive advantage:
  • Ease of Data Handling: BFSI services are supposed to generate and manage a vast amount of data every year. It includes information about customer behavior, needs, and preferences. BI solutions analyze and turn this data into actionable insights by aligning it with market trends. As a result, organizations can offer better financial services with the ease of data handling.
  • Enhanced Work: Business intelligence in financial industry helps track the performance of various departments and employees. With this kind of information, organizations get insight into their business operations. Let's take an example. Banks utilize business intelligence to determine customer needs and how their employees can respond to such demands. As a result, they can offer a better customer experience.
  • Improved Customer Retention: BI tools help financial companies to identify and pinpoint the reasons why customers switch to the competition. Further, they can improvise and provide better products & services to meet the needs of old and new consumers. With this, banking institutions can reduce customer attrition and improve customer retention & loyalty.
  • Risk Management: The finance industry is volatile with sudden and uncertain changes. BI applications in banking and financial sector provide fact-based actionable insights. It helps to detect and reduce fraudulent activities to minimize risks. A BI solution can ensure compliance with national and international regulatory standards.

  • Real-time Reporting: BI in banking sector provides visualization of historical and current data of the company to identify customer behavioral patterns and potential blockages in the system. It offers visual cues to take necessary actions to achieve goals.
  • Improved Operational Efficiencies: In today's ultra-competitive marketplace financial institutions need to be as lean and efficient as possible. Using BI solutions to analyze operational processes, organizations can reduce ongoing costs and maximize existing resources and expertise. For example, by analyzing the performance of customer-facing employees, such as sales personnel, tellers, and account managers, organizations can discover ways to improve and enhance the customer experience at the point-of-contact.
  • Improved Products and Services: BI solutions allow organizations to track individual revenue streams to better determine which products and services are profitable and which are not. But the benefits don't stop there.
Business Intelligence solutions also enable financial organizations to analyze vast amounts of customer data to gain insights about customer needs and sentiments regarding banking that can be used to improve products and services. As an example, perhaps it is learned that customers want a quicker, easier way to track and analyze their earning and spending patterns. Institutions, may be able to send more timely alerts to customers. Or they are looking for a smoother and less complicated application and funding process. Armed with these kinds of insights, organizations can develop new and improved financial products and services to better meet customer needs, and in turn create a competitive edge.
  • Improved Marketing: Using BI, marketers can analyze CRM data based on a range of criteria to uncover the most profitable customer profile. In addition, the customer base can be analyzed to identify and develop new cross-sell and up-sell opportunities, and to carry out more targeted online marketing campaigns. This presents a major advantage, as research shows that it costs five times more to sell financial products and services to new customers than to existing customers.
  • Improved Customer Retention: As previously discussed, BI applications can help financial institutions identify and pursue those customers that are the most profitable. BI also plays an important role in improving customer retention and loyalty. Using business analytics tools and techniques, organizations can discover the reasons why customers switch to a competing institution. They can then implement new processes to help reduce customer churn. The ability to track customer habits, preferences and behaviors also allows organizations to tailor their products and services in ways that meet needs, solve problems, and promote customer retention and loyalty.
  • Developing New Investment Strategies: Asset managers are utilizing new data sets to develop new strategies for investing. By developing models around social media, investors can gain specific insight on sentiment and develop trading signals. Other research analysts are using satellite imagery to understand global supply of commodities like oil & gas or triangulating consumer spend based on the number of cars in shopping center parking lots. Whole new categories of investing are emerging from leveraging analytics and BI applications.
  • Risk Reduction: The financial world is constantly changing and filled with uncertainty. More than ever, banking and finance institutions need to use every tool at their disposal to reduce risk. Fortunately, today's Business Intelligence solutions provide actionable information that organizations can use to mitigate risk in a number of areas.
Business intelligence in banking use analytics software, or SAAS (software as a service), to create data visualizations that are interactive and can be created at the desk top level by end users for banks and financial service companies. Commonly used banking business intelligence software includes: Microsoft Power BI, Tableau, Tibco Spotfire, and Domo. Banking business intelligence applications can be hosted on the cloud and configured to run private dedicated servers for financial services companies that are strict on data security requirements.
Top 5 benefits of business intelligence in banking
Business intelligence offers banks the adaptability needed to excel in both business-as-usual conditions and in more turbulent economic times. Worldwide, BI processes and software give banks deeper understanding of their business, their customers, and their future. It can also open the door for efficiency by shining a light on areas ready for cost-cutting measures, new business prospects, and more.
Examples of banking business intelligence benefits include:
  • Feed as much data as you want into BI software, it will never get overloaded as long as it's good, clean data (a process we'll get into later).
  • Faster reporting – Banking BI allows organizations to visualize both historical and current data in real time. This makes spotting patterns, potential bottlenecks, and setting goals easier based on historic metrics. No more waiting for a report for 2 months after you requested it from finance.
  • Business intelligence in banking connects across disparate systems, removing the need to generate reporting from each one individually. Business intelligence in banking allows organizations to measure big data on their customers in quantities never seen before to help increase customer satisfaction.
Banks can have a deeper understanding of their customers with banking BI, allowing them to address concerns proactively.
  • More accurate reporting – business intelligence in banking removes the need to manually wrangle data by plugging directly into core systems databases.
The ability to track customer transaction histories allows institutions to quickly detect and reduce the incidents of fraudulent activities, the most notable being credit card fraud. The ability to track the communications and behavior of internal employees in trading securities helps institutions comply with new regulation frameworks brought on by the 2008 financial crisis and recent insider trading cases. Unlocking data from siloed asset class systems could help global banks predict credit risk for counterparties across all asset classes.
Accurately estimating the risk of customer loans based on key criteria such as the borrower's earning capacity and current financial assets—while factoring in new data sets and the prevailing economic climate—is another risk mitigation benefit that BI can provide. BI tools can also be used to analyze credit portfolios, detect potential delinquency cases early, and take quick preventative action. Technology is transforming the banking and finance industry, and it's not done yet. Going forward, those institutions that adopt and fully utilize BI solutions to manage risk, increase operational efficiency, and provide products and services that meet real customer needs will be better positioned to enjoy sustained growth, profitability and a competitive edge for years to come.

Business Intelligence for Sustainable Competitive Advantage
The model is unique in the sense that it has been developed based on the data obtained from 10 interviews in 4 different. The below model can still be taken as a research model for further investigation. A causal modeling approach such as structural equation modeling (SEM) can be undertaken to test the model. The combined model has 9 factors and 34 variables. It is observed that the basic determinants, which are obtained from the literature, apply quite effectively in the successful BI deployment.  Its determinants are Quality BI Information, Quality BI Users, Quality BI Systems and BI Governance, which falls under firm's unique resources. Organization Culture, Business Strategy and Use of BI Tools are considered moderators between successful BI deployment and the use of BI-based knowledge for sustainable competitive advantage. Organizations especially in telecommunication related industries which are planning to embark on BI can consider these variables as criteria of successful deployment. However, these criteria may not be applicable to all industries as careful analysis is first needed to select the appropriate criteria for the company. A multiple criteria modeling approach can then be undertaken to access the suitability of the company for BI deployment.
Figure-1 BI for Sustainable Competitive Advantage Model

Inclusive Growth and Various Impact Facts in India
The trend toward evidence-based decision-making is taking root in commercial, non-profit and public sector organizations.  Driven by increased   competition   due to changing business models, deregulation or, in some cases, increased regulation in the form of new compliance requirements, organizations in all industries and of all sizes are turning to business intelligence (BI) and data warehousing (DW) technologies and services to   either   automate or support decision-making processes. An increasing number of organizations are making BI functionality more pervasively available to all decision makers, be they executives or customer-facing employees, line-of- business managers or suppliers.
Pervasive BI results when organizational culture, business processes and technologies are designed and implemented with the goal of improving the strategic and operational decision-making capabilities of a wide range of internal and external stakeholders. Even though the term Business Intelligence was first coined in 1958 and the first BI software tools emerged in the 1970's, BI is not truly pervasive in any organization. As organizations identify more stakeholders who can benefit from improved decision-making capabilities, they are choosing to deploy BI and thus come increasingly closer to achieving pervasive BI. For organizations struggling with changing organizational structure and culture, business and IT processes and technologies, several lessons can be learned by examining the best practices organizations employ on their path toward achieving pervasive BI, it includes various benefits like time & cost. Knowledge is becoming more and more synonymous to wealth creation and as a strategy plan for competing in the market, place can be no better than the information on which it is based, the importance of knowledge and information in today's business can never be an exogenous factor to the business.
Organizations and individuals having access to the right information at the right moment, have greater chances of being successful in the epoch of globalization and cut-throat competition. Currently, huge electronic data repositories are being maintained by businesses across the globe. Valuable bits of information are embedded in these data repositories. The huge size of these data sources makes it impossible for a human analyst to come up with interesting information that will help in the decision-making process. Commercial enterprises have been quick to recognize the value of this concept, because of which the software market itself for data mining is expected to be in excess of 10 billion USD. Business Intelligence focuses on discovering knowledge from various electronic data repositories, both internal and external, to support better decision making. Data mining techniques become important for this knowledge discovery from databases.   In recent years, business intelligence systems have played pivotal roles in helping organizations to fine tune business goals such as improving customer retention, market penetration, profitability and efficiency. In most cases, these insights are driven by analyses of historical data.
Business Intelligence (BI) is a business management tool, which consists of applications and technologies that are used to gather and analyze information about business. Business Intelligence systems are used by Banking and Finance companies to analyze the factors (or data from inside and outside the organization) affecting the Banking and Finance business, so as to help them in making a decision. Various tools and applications of Business Intelligence include query reporting & analysis tools, data mining tools, data warehousing tools, etc. Business Intelligence tools enable the Banking and Finance companies to make real time decisions at all levels; i.e., strategic, tactical and operational, using advanced analytics and powerful data mining tools. Further, these tools provide single integrated enterprise solution for reporting; thus, reducing the time consumed in reporting. Commerce companies operate in a highly competitive environment. As a result, a lot of pressure exists on them to increase their profit margins by introducing new product and deploying new services. Further, these Banking and Finance companies are facing issues of infrastructure up-gradation as large amount of data exists in data sources, which are incompatible. This data that remains underutilized can lead to loss of business opportunity. Further, utilization of this data will help generate businesses resolve technical issues related to customer care, billing, network engineering, product design, and marketing. With the shift in focus of the Banking and Finance industry; from technology to customers, there has been an increasing demand for customization of Business.


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