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Augmented Analytics Assignment: Business Intelligence In Banking Industry


Task: Develop a report on augmented analytics assignment critically examining the scope of business intelligence theme in banking industry.


1. Introduction
The objective of the current discussion build in the augmented analytics assignment is to analyses the scope of business intelligence theme on modern business approach. Augmented Analytics has been chosen in the current background that would be outlined in context with business functionality and future growth. The importance of Natural Language Processing (NLP) and Machine learning explored in the context of augmented analytics assignment has been contributing to the data analysis methodologies. Apart from data analysis, Augmented Analytics also plays significant role in business intelligence development and data analytics.

2. Theme/ industry/ for augmented analytics
Banking industry has been chosen in this augmented analytics assignment to analyse the scope and impact of Augmented analytics on day to day basis business functionality. Data plays important role in shaping future direction of the business objectives. Augmented analytics provides a distinctive opportunity for the banking industry to implement and unitize big data for predicting future direction and business trend. 89% of the Modern global leaders significantly trusting the utility of big data and data analysis with respect to business development. Modern business analysts predict a growth of $29 million till the end of 2025 fiscal year. Augmented analytics enables the banking sector to transform the big data into smart data which helps to control and monitor the product development process, shaping consumer experience and innovation etcetera [1].

3. Existing usage of business intelligence (augmented analytics)
Since last decades, the global business scenario encountering huge transformation in data handling and management. The future of business has been equipped with augmented reality methodologies which creates physical realm of functionality. Augmented analytics approach provided in this section of augmented analytics assignment enables bankers to automate the data analysis process to yield accurate projection of the data for the future direction. There are various use cases of augmented analytics, contributing to the development of augmented realm of data management and predictive analysis. Firstly, Automated data presentation comprises of natural language processing and machine learning processes. There are uses cases of augmented human intelligence, where augmented analysis has been utilised to investigate the Citizen data. Secondly, “Brainspece Engine” is a significant example of usage of business intelligence to investigate “data in context” [2]. Modern banking sector extensively uses “predictive analysis” approach in case understanding market data and user data. Augmented analytics helps to structure predictive analysis to provide users suitable platform for augmented reality realm that creates user/data interphase for intelligent and accurate data analysis approach. Thirdly, “Self-Service BI” is another important use case depicted in this segment of augmented analytics assignment where augmented analytics. IKEA implemented BI and augmented analytics to provide augmented realm for users to understand the furniture setting within their own home. This realistic visual data analysis helps customers to make decisions based on the augmented setting [3].

4. Scope and boundaries
The scope of augmented analytics noted herein augmented analytics assignment is to transform big data into smart data in case of transaction, deliver and predictive analysis of the customer data and financial data as well. There are numerous boundaries associated with current topic of discussion that is augmented analytics. Firstly, expertise in augmented analytics is an essential part for functionality whereas banking sector lacks expertise and proper training. Transformation and utilization of big data in case of banking sector associated with data loss issues and lack of data management that leads to misleading data and falsified outcome of the data analysis [4]. 

5. What is the role of Business function of augmented analytics in the industry examined in this augmented analytics assignment?
 The adaptation of augment analytics creates disruption of functionality in banking network and predictive analysis methodologies. Data embedded AI elements are the part of augmented analytics system process that creates a realm of business function integrating various components of business functions and processes. Therefore, data embedded AI system acts as integrator between natural language processing and data transformation process. The logic behind business intelligence are divided into three steps. Firstly, for any transaction, as in case of money there needs to be economic exchange [5]. The same is with the case of sharing ideas. The market as a whole can flourish only when there will be exchange of ideas. The next is interdisciplinary research whereby it needs to be seen as to how daily collected data is transformed into useful knowledge for the company. This may provide organizations the way or key to improve their quality of product that will also enhance their marketing scheme. The quality will improve by loads if the company encounters practical challenges such as feedback from customers, competition from other companies, handling market recession and trying to keep up pace with demand of the customers, but at the same time balance their margin of profit or loss [6].

Moreover, limited use or utility of resources is a big constraint. Not only natural resources, but even human resources if used below their potential won’t give the optimum output. Thus, it results in potential resource being wasted. All this is an offshoot of the time-consuming implementation of the various marketing strategies that one forms.

Thus, the argument raised on this augmented analytics assignment signifies that both the consumer and the producer along with the seller have equal responsibility in maintaining equilibrium in the market and economy on the whole. The formative assessment of this business incident will at length give a summative assessment on the business intelligence [7].

6. Possible scenarios where augmented analytics could be utilized in banking sector
Augmented analytics could be used in banking functionality to optimize the day to day-based banking processes. This section of the discussion focuses on possible scenarios where augmented analytics could be utilized for creating an integrated system or system automation for the banking functionality.

6.1 Scenario 1: fraud detection
It is stated herein augmented analytics assignment that the first scenario where augmented analytics could be used in case of fraud detection. Machine learning, big data and corresponding predictive analysis helps to derive the platform for banks that can detect fraudulent acts in case banking functionality. Data mining techniques plays critical role in detecting fraud whereas unstructured data could be integrated for the analysis of the fraud prediction and prevention [7].

6.2 Scenario 2: Application screen
 The concept of Application screening examined in the augmented analytics assignment is an important functional scenario where augmented analytics successfully utilized for processing volume applications, screening. Important variables associated with bulk applications are not altered in case of augmented data analytics. The use of augmented analytics enables application screening process to yield authentic data.

6.3 Screen 3: understanding customer buying trends
The big data associated with banking customers significantly analyzed by the augmented analytics which creates augmented physical data that helps banker to analyses trends of buying habits of the target customers [8]. Augmented analytics enables bankers to develop products for right person. A lot depends on the decisions that are being made along with the capability to take accurate decisions and improve the capability to take the correct decisions at the right time depending on the mood of the market. The increase in revenue and lower margins between cost price and selling price will automatically upgrade the benefits in business. This is the sole reason outlined in the present context of augmented analytics assignment why stock exchange analyzers are required [9].

7. Business intelligence practices for Banking functionality scenario
There are some major banking practices that can be facilitated with augmented analytics practices as mentioned in below section of the discussion on augmented analytics assignment.

7.1 Regulatory compliances:
Banking sector has been reforming since last decade whereas business intelligence has been playing crucial role in case of managing compliances and customer data maintenance. Business intelligence and augmented analytics act as primary driving force for Insolvency and Bankruptcy methodologies. Banks need to in line with company law tribunal whereas business intelligence techniques and augmented analysis processes helps banks to comply with finance guidelines of the state [10].

7.2 Budget development and monitoring:
Augmented analysis helps banks to analyse future trend and expenditure direction depending upon the outcome of the big data analysis of the client base and customer trend. Business performance associated with various banking peripherals need to be measured on the basis of data analysis outcome. The outcome of the investigation needs to be implemented for the budgeting and monitoring for any fiscal year. The augmented analytics helps to predict actual budgeting requirement for the bank that eventually contribute to overall productivity and development of the bank [11].

7.3 Risk management
The research on
augmented analytics assignment also illustrates that the augmented analytics helps management and assess risks associated with business functionality. The array of risks associated with business process need to be assessed for the shaping future budget and development plan for the organisation. Augmented analytics helps to analyse the trend in business sector analysing big data and global trend. The business intelligence process helps to yield accurate outcome that helps to predict the future trend and direction of the banking sector and the sustainable market scenario [10].
7.4 Possible usages of Business intelligence
As per the investigation on augmented analytics assignment, there are ten possible use that is viable for the modern business organisations in global perspective. Primarily, efficient business information sharing is the key role of the business intelligence whereas data analytical tools incorporated with business intelligence acts as performance indicator in the business arena. Data assessment of the employees provided extra control over their performance which empowers employee’s position in the organisation. Modern banking and organisations use augmented analytics for faster data entry and analysis as well. Development of the project and financial report is significantly easier for manager in banking sector. Augmented analytics helps to gain customer insight associated with banking sector [12].

8. Conclusion:
The outcome of the augmented analytics in case of banking sector within this augmented analytics assignment proved to be a significant factor that is contributing to future development of the banking sector and paradigm shift in business methodology. Augmented analysis as well as predictive analysis helps to shape the data analysis processes that transform big data into smart data that enables analysists to predict and investigate future direction and functional excellence.



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