Business Intelligence and Security Analytics employed in ANZ Bank

Question: Discuss about the Business Intelligence and Security Analytics employed in ANZ Bank. Answer: Introduction The Australian and New Zealand (ANZ) Banking Group Limited is the fourth largest bank in Australia. The ANZ bank has uplifted the utilization of data analytics for detecting the frauds and for improving the customer service. Abbruzzesse stated that the global analytics software leader SAS analytics insights on Banking on Data in Digital Environment have a committed data repository and data mining resources (Loshin, 2012). Moreover, implementation of business intelligence tools leads to attain competitive advantage in external and internal business processes of the bank. Business Processes Internal Business Risk Administration By determining certain events and analysing them from the perspectives of evaluating a response approach and detecting progress. Competitive Advantage: Reduction of Risks and aggressive magnitude can be determined. Customer Expediency Management It is the process of determining the valuable and invaluable customers and restricts them from some services. Competitive Advantage: The profitability can be increased. External Customer discontent solution The customer can make complaints over email or phone call. The call center operator registers the complaint, keys in the summary, and allocates docile code. Competitive Advantage: The customer satisfaction can be improved. Credit Card Fraud Revelations A method can be employed for determining the likelihood of fraudulent activities. Competitive Advantage: The fraudulent transactions can be prevented and the reliability of the bank can be improved. Customer Segmentation The intrinsic business processes in the bank are associated with customers whose whole life cycle ranging from procurement to end of their relationship with the bank. 20% of the consumers can produce 80% of revenues if the bank keeps them engaged and profitable (Ularu, Puican, Apostu Velicanu, 2012). Competitive Advantage The cross-selling amenities and several rewards can be offered to entice the customers. Metrics used ROI Return on Investment per consumer ROE- Return on Equity for each consumer Data Sources Frequency of Transaction Consumer Retention Size of each transaction Data Variants Required Customer Characteristics Consumer Expertise Consumer Cost Margins Transactions done in an user account Methods used Costing based on User Activity Evaluation of Analytical limitations Data classification Analysis of customer transactions and profits Month-wise or Quarter-wise Reporting Forecasting the customer demands Tools Used Oracle, SAS, Teradata, SPSS, etc. Fraud Detection in Credit Cards The detection of credit card frauds is an extrinsic business process to note down any fraudulent transactions in credit cards of the customers (Rutrell, 2012) . Competitive Advantage The fraud cases can be restricted and penalty can be reduced. Metrics Used Successful Fraud Detection probability Alarm embossed for real frauds Decrease rate of frauds Data Sources Frequency of user transactions Size of transactions like loan amount, deposited amount, etc. Data Variants Required Recognizing the buying characteristics and location of customers Customers Direct input Statement of User Accounts Methods used Evaluation of Analytical Parameters Classification of fraud patterns Analysis of unanticipated digital data Merging distinct data sources Identifying duplicate transactions Recommendations The recommendations of business intelligence implications are listed below: Customized Proffering Cross-Selling Facilities and Consumer centric rewards Spend dissection of customers Customer Detainment Management Targeting Techniques on customers References: Loshin, D. (2012, October 19). Business Data Suited to Big Data Analytics. Retrieved from https://data-informed.com/businessproblems-suited-to-big-data-analytics/; Rutrell, Y. (2012, August 12). Analytics platform helps agencies fight cyber crime, government computer news. Retrieved from https://gcn.com/articles/2012/07/12/sassecurity-intelligence-platfromanalytics.aspx; Ularu, G., Puican, F., Apostu, A. Velicanu, M. (2012). Perspectives on Big Data and Big Data Analytics. Database Systems Journal, 3(4), 3-14.