Big Data for Fraud Detection in Banking Sector - Free Samples

The detection of fraud in banking sector is an important part to eliminate risks of any cyber-attack or data breach. Banks are often vulnerable to fraud and this affects banks and customers (Flood, Jagadish and Raschid 2016). Most of the frauds in banking sector occur due to either human negligence or any malpractice or system defect. Frauds in banking sector impact customers and bank itself in a very negative way because both banks and customers can lose sensitive data and money. Nowadays, big data analytics has emerged as a game changer in every sector and it provides a more reliable and flexible usage in working of every sector (Fuschi and Tvaronavi?ien? 2014). Banking sector has now started to adopt big data analytics for its operations due to its usefulness, reliability and speed. The purpose of this report is to analyze big data use in banking sector and how big data analytics help banking sector to detect fraud. The outline of the report is data collection and storage system, consumer-centric product design, r mendation system and business continuity plan in case of power outage. The data in banking sector collected are credit card usage details, personal emails sending and receiving or account details or any other regular actions on a daily basis. The data collected are not only from internal source of banking sector but also from external sources which sometimes requires permission from third party. These sources are internet based navigation sites such as social media, Yahoo, Google or Bing. Google and Yahoo provide Gmail and Yahoo mail respectively (Srivastava and Gopalkrishnan 2015). The data are categorized into two types and they are primary data and secondary data. Primary data are information about employees, their head supervisors, managers, senior managers and customers, which are collected for proper functioning of banking sector. Secondary data are information of internal and external behavior and working of banking sector which are collected for different purposes and used for betterment of banking sector (Kim, Trimi and Chung 2014). Both types of data are in the form of structured, semi-structured or unstructured data. Therefore, they are arranged in orderly manner to access and operate easily on each form of data. The data in banking sector are unstructured data mainly and they are plicated to use in its initial form. Big data deals with this type of data and in banking sector, unstructured data are either machine or human generated. Machine generated unstructured data are scientific data or photographs and videos such as security or surveillance photos or images. Human generated unstructured data are internal texts within document files, logs, credit card or debit card details and emails, and website content (Raju, Bai and Chaitanya 2014). The data collection is through various sources are then mined that is data mining is done on the collected data. Data mining is exploring and analyzing of collected data to find data suitable for different purposes in banking sector. Data mining technique is used for five major categories of banking sector. They are customer retention, automatic credit card approval, fraud detection in banking sector, marketing and risk management. Data after data mining is used mainly for risk management and fraud detection in banking sector (Pouramirarsalani, Khalilian and Nikravanshalman 2017). This is explained as when data is stored in storage then big data has features of protecting thes e data from going into hands of fraudsters. Banks have massive amounts of data which needs to be stored in an efficient way. The new storage systems in banking sector for big data provides solutions and they are reconstructing the backup systems with improved performance that will not change the existing backup routine. The second solution is building a Disaster Recovery (DR) system that will help in an emergency case such as disaster or power outage. The third solution is managing data lifecycle for improvement of data utilization efficiency (Chitra and Subashini 2013). The explanation for first solution is to upgrade physical tapes from existing Disk-to-Tape (D2T) mode to the new Disk-to-Disk-to-Tape (D2D2T). The new tape provides more reliability and space to store data of size more 9TB and has high backup speed. The description of second solution is new Disaster Recovery system which is built after upgrading local backup system using tape. The Disaster Recovery system is used for storing data at different location in banking sector. The full back up in first solution using tapes is further stored in storage system that is Disaster Recovery system (Jones, Aggarwal and Edwards 2015). The storage is done by identifying unique blocks of huge data and store in Disaster Recovery system. The next backup is done to match the unique block with the blocks stored in the system to destroy duplicate data and then save all unique data. The leftover data is again checked so that no data is left vulnerable to any fraud. The left over data is also checked to analyze if any data can be effective for future purpose. The third solution is that the data is processed and stored on peripheral system and near-line data (twenty to thirty days old) is backed up regularly and stored on disks (Rao and Ali 2015). These data is tested for integration and effectiveness and to recover if any fault occurs. The long- term data (ninety days old or older ) is backed up regularly and stored on physical tapes. Both the data is then stored at different locations in Disaster Recovery system. This new storage system solution helps in better backup performance, recovery process is quick, and data storage is multi-level. The long-term relationships with customers will require fulfilling demands and needs of customers. This is achieved through customer relationship management (CRM) systems. Customer relationship management is used by organizations to optimize contact with customers and build long-term relationships (Elgendy and Elragal, 2014). The various ways are telephone calls or emails to attract and retain customers. Customer relationship management system is based on infrastructure of customer data and information technology. Electronic customer relationship management systems provides all ways of munication with the customers. The ways are sales, delivery, email, online marketing and purchasing, online banking or many other online services. Customer relationship management system in banking sector is achieved by maintaining relationships with existing customers and creating relationships with new customers (Dalir et al. 2017). The benefits are providing better service to existing and new custom ers and identification of specific values related to each sector of the business environment and existing or new customers. The other are dividing different market segments to improve long-term relationships with target customers and service fees which is charged increases revenue for banking sectors. The additional benefits are implementation of this system helps in increasing customer satisfaction and their loyalty and interest rates are increased to attract more customers (Baesens, Van Vlasselaer and Verbeke 2015). The seventh one is online advertising to attract customers and increased effectiveness and classification of customers. Electronic customer relationship management system in banking sector has a structure which is based on two factors and they are trust and satisfaction. They are mitment, loyalty, customer retention, and r mendation willingness. The other factors which construct the system through customer’s point of view are information, convenience and munication channel (Srivastava and Gopalkrishnan 2015). Trust is important for customers and bank relationship and the trust is referred to protection of every individual’s bank account details and credit card or debit card details. Customer satisfaction is a quality in bank and customer relationship that will help them to trust on banks. Customer satisfaction in bank is very important to retain existing customers. mitment is to partner close relationship with customers for valuable effort. Loyalty provides future benefits to banking sector even when there is a strong petition (Moro, Cortez and Rita 2015). Loyalty is a mitment to banks f rom customers to deal with them. Loyal customers will also r mend particular banks to their relatives or customers. Customer retention is important as exiting customers are more profitable than new customers. Therefore, fulfilling needs of existing customers is more important. The above factors help customers to willingly r mend services of bank to others as they are satisfied with services of bank. Information is correct, accurate or updated are not is necessary for the structure of the system. Convenience is important as customers will e after considering location of bank (Greenberg 2014). Geographic location of bank with working hours and others are included in the system. munication channel like mobile, ATM, text, e-mail are used by customers to know bank services. R mendation system is used as a tool in banking sector to help customer by giving service when bank employees are not available on a particular time. R mendation system provides precise and timely information to customers. The system is virtual consultant to customers providing better information and services (Ravi and Kamaruddin 2017). The r mendation system can be explained by the following process. The system analysis provides specifications that are authenticated with username and password for logging into system and questionnaire type survey for the user regarding product interest. The next two specifications are giving advice to user after the pletion of interview and when there is query regarding search engine, explanation term should be there in the search engine (Lin et al. 2015). The last two specifications are to provide answers by the expert to questions by the customer and also update the knowledge base in system (Davenport and Dychà © 2013). The system design contains human expert, knowledge acquisition facility, knowledge base, inference engine, working memory, user interface and the user. This is the system bank follows in r mendation system. R mendation system is tested using black-box and white-box testing to know that the system is properly functioning and also integrated (He, Tian and Shen 2015). The testing is also done to ensure satisfactory working of every feature. The testing is done on the database so that the data can be accessed with respective attributes and required data can be fetched. The application is important in r mendation system because it provides a platform for direct munication of user and banking sector (Ng and Kwok, 2017). This is a place where user can register and then they can login with username and password. This is a place where user can get details about banking process in about us section and also contact details of bank in contact us section. The system design is implemented in application and the working of system structure is defined in application. These are the features and functions of r mendation system and this helps in clearing customer’s doubts and queries. The customers can also give feedback in r mendation system (Flood, Jagadish and Raschid 2016). The r mendation system in banking sector are developed using information system and are also called expert system in other sectors. Survival of online business in case of power outage or any other disasters is a major discussion for any banking sector. The business continuity plan has four steps in banking sector and they are business impact analysis, risk assessment, risk management and monitoring and testing. The first step is business impact analysis that helps to identifies critical business functions and impact of loss of functions for example operational and financial on banking sector. This process is analyzed by senior management representatives and board of directors. The business impact analysis is required at times when there is disruption in power outage and any disaster (Harvard Business Review, 2017). The second step is risk assessment which helps to determine cause of power outage or other disasters. Senior management analyzes the risk through risk assessment processes and then develop program to tackle the risks. The third step is risk management which is important to develop and maintain business continuity plan in baking sector. Risk Management in banking sector is based on first two steps that is business impact analysis and risk assessment (West and Bhattacharya 2016). These realistic events can be formally declared and updated by senior management annually to employees in banking sector. The fourth step is monitoring and testing which is a confirmation to business continuity plan in banking sector that all the steps are revised and evaluated without overlooking any significant changes. This step is finally evaluated by senior bank management (Forbes 2017). This is when they can mit necessary workforce, budget and time to test the program for validation of business continuity plan in an event of any disruption in banking sector. The above discussions conclude that fraud detection in banking is a very important process and big data analytics is used in banking sector for fraud detection techniques. The discussions shows that the data collection system in banking sector is plicated as there are huge data sets in banking sector. The data collected need to be stored in places where there is security and proper storage place to be chosen. The actions to be taken on collected data that is services to customers and system to r mend customers are also discussed. The business continuity plans on the basis of possible disruptions were the key points of this report. 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