Discuss about the Telecommunication Fraud.
Fraudulent telecommunication activities are common through un-mastered computer systems. This project proposal presents the detection of the problem of telecommunication fraud using the various notable techniques of data analytics. The tools that are available to detect the potential telecommunication activities which are fraudulent comprise of a computer machine interfaced to receive a call information and putting it to record. Operators within the computer are used set up to compare the parameters in a current call with the past general practice of that particular subscriber. The output will indicate the areas where there is potential fraud (Palacios, 2016). The comparing tools use structured data analytics tools as presented in this report.
Aims
The purpose of detecting telecommunication fraud is to create an opportunity to identify key potential fraud areas, identify instances of fraudulent operations and catch up with it to safeguard a telecommunication system. The result is a system that is free from fraud to guarantee the interests of the management, system administrators, and the clients. Many institutions handle large volumes of call data subsequently. A minute can record up to 29 calls in a standard organization. Calls will always range from satisfaction complaints, suggestions and feedbacks from customers, dealers and the employees in their regular operations.
Objectives
The purpose of the report is to enable the users of a telecommunication system accurately pinpoint and eliminate fraud as a source of loss to an organization. The large amounts of communications data operated are difficult to verify their levels of being genuine. The only way to handle this task is the implementation of an automatic system for data analytics. Data will comprise of old calls of a particular caller, the current call record, and fraud results. Data mining tools are used to suspected call records mined from information haystack will be compared to records and display the results (Orlaith, 2016).
Possible outcomes
The resulting outcomes are scripts of call information branded as being fraud related. When the data analytics tools are implemented, there will be a higher possibility of detecting fraud in telecommunication, stop progress cases and a consequent investigation to safeguard the interests of the organization. The prevalent costs are the primary inhibiting factor for the implementation. A proper application of data analytics tools will enhance the process of achieving the overall goals of the organization. A successful process also improves the image of an organization to the publics and the investors (Omar, 2017). Few firms that are financially viable in the country have already adopted a fraud detection most reporting positive progress disregard of the accompanying costs in development. It forms a significant achievement in growth. All human and robotic fraud attempts are maintained (Salgado, 2016).
Background of the problem
Both the wireless systems and the systems that use transmission lines from telecommunication systems. Telecom fraud is an unauthorized usage in which the user has not yet paid for the services. Monitoring systems provide the apparatus and tools to detect the potential usages of such applications. Access to information is becoming essential in every field of business, government, and science with much increase in the use of wireless systems. Fraudulent usage of telecommunication systems is also on the rise causing up to a total of not less than $600 billion in a year, and the high figures have created a desirability for a system to detect and prevent those activities (Monde, 2017).
The varieties of fraud include tumbling fraud using different IDs which are generated to place different calls. It is easy if pre-call verifications for the IDs are not conducted (Zolotová, 2017). The user identifications will then remain to be unassigned and cannot be billed. The calling card fraud is done by misappropriating a valid call card number and using it to make calls with billing done to the unsuspecting subscriber. Cloning fraud is associated with cellular systems through plundering a valid customer identification, cloning the ID into a mobile phone which is used for the billing made at the subscriber’s ID. A tumbling-clone fraud hybrids the tumbling and the cloning type scams. Cellular phone calls are placed on successive customers’ IDs all programmed into the telephone. The tumbling-clone fraud is harder to detect among all the others. Another type is the subscriber fraud conducted by a rather valid customer (Monde, 2017). The customer uses a system without the intention of paying and continues to do so until access to the service is blocked.
The current nature of the problem
The analytics tools available include the spectral clustering technique, the cross-object relationships, the Recency, Frequency and Monetary method (RFM) and the Customer Lifetime Value. System implementation poses tremendous operating costs for business and hence acquisition is limited to big telecommunication companies. Furthermore, the nature of the problem is complex and needs to be regularly reviewed due to the flexible nature of fraud. Few service providers are available in the industry to be sourced which makes the prices to rise higher. The database nature of the fraud has also made it difficult to outsource services from external solution providers. The delicate natures of business and industry competitiveness call for extreme privacy in internal issues (Lopez, 2017).
Formulation of the problem and data mining techniques
The Cross Industry Standard Process for Data Mining (CRISP-DM) is the leading approach used in data mining because of its effectiveness. The other methodology for conducting the process is the SEMMA. The ASUM-DM, Analytics Solution Unified Method for Data Mining has been released lately to refine and extend the CRISP-DM. The phases of the CRISP-DM will give the business and data understanding, preparation of data, modeling, evaluation, and deployment.
In the telecommunication industry, it is hard to the next date, time and duration of the genuine subscribers (Jaratsri, 2017). Subscribers do not have a standard frequency of making calls even for the biggest customers. In solving the problem, customers are categorized using the loyalties perceived on them. The method will help to classified by their most likely next use. Data must be collected and analyzed according to types, the method of recording, their storage formats, and any other possible changes. The clustering method is the best to use when classifying subscribers’ data. It will ease prediction to analyze the data and a model that will be useful in future decision making. The analysis is done by the predictions for specific categories (Lersel, 2017).
Data collection and organization strategy
The Cross Industry Standard Process for Data Mining lays down the process for tackling the fraud problem. The data collected is prepared for the modeling stage. Data collection is followed by familiarization to identify the quality problems and get insights. Subsets are created to form hypotheses about hidden information. Preparation of data covers all activities concerning the subscribers to enable construction of a final dataset. It can be done for multiple times. The processes are done in data preparation cover past call records in tables, selection of the subscribers’ characteristics and a transformation succeeded by cleaning of data in preparation for modeling.
In the modeling phase, several techniques are available (Lersel, 2017). The parameters are attuned to optimal values. All the methods available for data mining will solve the same problem of fraudulent users in telecommunication and seem to have similar data from requirements.
Data mining methods
The Cross Industry Standard Process for Data Mining (CRISP-DM)
It has six phases as indicated below which are non-directional. The process is continuous even after finding the solution. It is required that the business is defined regarding its objectives. The purpose of the firm is the detection of fraud. The standard decision model is used as a design plan. The preliminary data collected must be analyzed to identify problems with its quality. Data subsets are also formed here. Data is cleaned up and attributed for a multiple of times. The final set of data is prepared to be fed in the modeling tools. The modeling methods have preset standard values with similar and specific data requirements (Itani, 2017).
Models are built for data analysis according to the quality parameter. The steps executed in the construction of the models must be thoroughly reviewed to achieve telecommunication objectives. At deployment, data is presented to the administrators depending on their requirements. Report generation, data scoring, and data mining from things that should be considered. The client telecommunication firms are made to develop an appropriate business strategy.
Sample, Explore, Modify, Model and Assess (SEMMA) model
It is another statistics and computer intelligence support software that guides data mining. It is a logical functional tool for organizing data in a generalized manner (Longjun, 2017). Sampling involves selection of a large set of data for modeling sufficient information. Data is partitioned into small samples for efficiency. Data is then analyzed to understand relationships between the variables and the anomalies. Modification selects, create and transform the variables as the modeling phase creates models that will provide the desired output. Assessment is done to view the results regarding usefulness and reliability (Figueiras, 2016). The criticism on SEMMA is its focus on modeling alone.
Analytics Solution Unified Method for Data Mining (ASUM-DM)
It is an extension to CRISP-DM refined by the IBM computer company. It covers all the properties of the preluding application and extends it with a functionality of more detailed smaller sets which most analysts label as too complex reducing its popularity (Hofmann, 2016).
Knowledge Discovery in Database (KDD)
It describes technologies and methods to assist people in extracting information that is useful from volumes of information that expands rapidly.
Evaluation of the results
The assessment of results calls for an evaluation of data. The mining of results will be done hand-in-hand with the principles of business success. The models are acceptable according to world standards. The process is not a final process but a continuous process. The next thing is to outlay the solution and then formulate a decision. The model results must be tested on a simulation platform to access the accuracy levels (Darshana, 2017).
Deployment of results into business
The final phase of the CRISP-DM methodology is the implementation of the decision. It should be done within the first month to avoid a further loss of the business. The business has to develop appropriate strategies to comply the analysis solutions (Fahmi, 2017). Continuous monitoring and maintenance must be effected because the fraud environment evolves each day. The business findings must be documented in a final report for presentation to the client. An expert review of the documentation will eliminate possible errors. An error free documentation will provide the reference in succeeding analysis to prevent a repeat of the already perfectly handled areas which could create additional costs for the business and waste time on the already handled issue. Records also facilitate tracking of the identified fraud cases (Al-asadi, 2017).
The client will be required to keep a regular contact with the analysts for support on any needed assistance.
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Darshana, P., 2017. Privacy-Preserving Associative Classification.. Cham, Springer.
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Hofmann, C., 2016. A Two-Layer Method for Sedentary Behaviors Classification Using Smartphones. Tokyo, Springer.
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Lersel, V., 2017. Going concern decision prediction using predictive analytics. Analytics, 1(9), pp. 43-44.
Longjun, Z., 2017. Privacy-Preserving Data Mining on Big Data Computing Platform: Trends and Future. Cham, Springer.
Lopez, J., 2017. Application of Data Mining Algorithms to Classify Biological Data. Cham, Springer.
Monde, A., 2017. Application of Data Mining techniques to identify the significant patterns. Stellenbosch, Stellenbosch University.
Omar, N., 2017. Home-Based Intrusion Detection System.” (JTEC) 9.2-4 (2017): 107-111.. Journal of Telecommunication, Electronic, and Computer Engineering, 2(4), pp. 107-111.
Orlaith, M., 2016. Predicting Intake of Applications for First Registration in the Property Registration Authority. Dublin Institute of Technology, 4(17), pp. 133-139.
Palacios, H., 2016. A comparative between CRISP-DM and SEMMA. Journal of Technology, 3(9), pp. 1-93.
Salgado, R., 2016. Data mining and cluster organisations. Database Systems, 7(4), pp. 1-59.
Zolotová, I., 2017. Data mining in cloud usage data with Matlab’s statistics and machine learning toolbox. London, IEEE.
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