Measures for Financial Fraud Detection Using Data Analytics and Machine Learning

Full text

(1)International Journal of Advanced Science and Technology Vol. 28, No. 17, (2019), pp. 270-280. Measures for Financial Fraud Detection Using Data Analytics and Machine Learning Ms. Barani Shaju1 Research Scholar Department of Computer Science, Avinashilingam Institute for Home science and Higher Education for Women, Coimbatore, India. Dr.N.Valliammal2 Assistant Professor (SS) Department of Computer Science, Avinashilingam Institute for Home science and Higher Education for Women, Coimbatore, India. Abstract Financial fraud analysis, recognition and threat estimation are the spine of financial organizations. Impact of fake exchanges, fraudulent event or triggers can rapidly transform into huge dollar losses because of the lack of early identifiers or frameworks to notify fraudulent classifiers. Fraudulent activities are on the rise in various sectors like Investment market, Insurance, Mortgage, Credit card services, Money laundering. Fraud detection, persistently gets focus and consideration from various stakeholders, including financial institutions, controllers, specialists, scientists, network and dealers. Currently, deduction mechanisms apply rule-based frameworks as key means for identification of frauds or misrepresentations. This approach can work admirably by revealing known instances but the challenge lies in its ability to scale up with technologically updated fraud designs. As an alternate and scalable measure, Data analytics and Artificial Intelligence (AI) methods using Machine Learning are aimed to provide a proficient solution. Objective of this survey paper is to investigate current methods associated to financial fraud detection system, by utilizing Data Analytics along with AI strategies. It recognizes the principle highlights of current smart solutions for financial fraud detection and outlines the fundamental patterns as ground base for detection of fraudulent transactions. Keywords: Financial fraud, Data Analytics, Artificial Intelligence, Machine Learning I. Introduction The PwC worldwide financial crime review of 2018 exposed that 49% of the 7,200 organizations studied had been affected by various financial frauds. In 2018, about 600 vendors were centered on understanding the development, difficulties and advancements in the mobile channel. Accordingly, online transactions volume upto $194.1 billion in 2017 as well as online immediateness transactions additionally expanded to $30.2 billion in 2017, contrasted with $18.7 billion in 2016[1]. Because of the fast increment in online grade as well as the extension of the mobile payment growth, the scam in this system has emerged and turned out to be progressively normal. It can happen in a few different ways. However the most regular case is unapproved utilization of mobile payment by means of Master Card number. The online transactions are independent and primarily does not need existence of a hardware device. Rather, it needs significant data such as card information, termination time details, pin number for the transaction and card confirmation code. Credit and debit card related frauds are common among the most. ISSN: 2005-4238 IJAST Copyright ⓒ 2019 SERSC. 270.

(2) International Journal of Advanced Science and Technology Vol. 28, No. 17, (2019), pp. 270-280. referred and committed financial crimes which result in high financial loss for banks and financial institutions. To recover quickly from these kinds of issues, financial organizations utilize different fake avoidance systems such as constant credit approval, address confirmation systems, card check codes, rule-based recognition and so on [3]. In any case existing approaches rely upon characterized criteria that make it hard to distinguish new attack plans. Financial fraud identification is extremely relevant for many areas. Many statistical as well as computing methods are utilized for data analytics issues of late. Data analytics framework is used to handle large data volumes. The function of the intelligent system can be employed to collect ongoing data from suitable organization and the exact dataset is then utilized to construct finest logical form. This model provides pointers by way of analytical exactness or nearness in fraud identification. Use of various AI algorithms helps to train and test the data in an effective manner. AI computerizes the extraction of recognized and unidentified samples from extracted data. In this paper, Section II describes the Literature Review of this approach. Section III introduces the Types of Financial Fraud. Section IV presents Financial Fraud Detection Using Data Analytics system. Section V lists Machine learning Techniques for financial fraud classification. Section VI illustrates the Proposed Model and Methodology. Conclusions are drawn in Section VII.. II. Literature Review Wide range of literatures associated with fraud detection in the financial application research has been reviewed, consolidated and presented in this section. It includes the methods for classifying the major types of fraud in financial system by using data analytics and machine learning algorithms. In addition, this review is used to justify the scope of the present work.. ISSN: 2005-4238 IJAST Copyright ⓒ 2019 SERSC. 271.

(3) International Journal of Advanced Science and Technology Vol. 28, No. 17, (2019), pp. 270-280. Current research in fraud detection has been unquestionably shifted in strategies considered, despite the fact that the previous methods are as yet typical in nature.. Table 1: Benefits & Weakness of existing system. ISSN: 2005-4238 IJAST Copyright ⓒ 2019 SERSC. 272.

(4) International Journal of Advanced Science and Technology Vol. 28, No. 17, (2019), pp. 270-280. ISSN: 2005-4238 IJAST Copyright ⓒ 2019 SERSC. 273.

(5) International Journal of Advanced Science and Technology Vol. 28, No. 17, (2019), pp. 270-280. Above table shows advantages and disadvantages of existing methods based on published research work. Existing data analytics method provides quick and near perfect outcomes for huge volume of datasets. But it requires extra time as well as input sources to train data appropriately. Consolidating both AI and cognitive methods can make it significantly powerful in handling enormous volumes of data. III. Categorization of Financial Fraud Many types of frauds occur in financial transaction system affecting customer satisfaction and trust. Some of the common frauds are described below: 3.1Credit Card Fraud: Credit card frauds allude to the unapproved utilization of an individuals’ card to perform fake exchanges without the investors’ knowledge. End user or client’s data is extracted through undetected fraudulent routes. Phishing includes a fraudster imitating a money authority to influence the client to reveal their subtleties. Through phishing, impostor can break the financial institutions server security. Customer’s card details are trapped by way of intruding into their mail accounts. This has become more common nowadays [40]. 3.2 Securities Fraud Securities Fraud refers to a set of strategies, by which an individual is misled into investing the amount in a forged organization. 3.3 Financial Statement Fraud Financial statement frauds are hard to analyze due to the absence of knowledge in the field [41]. Financial statements are the reports discharged by an organization that clarifies aspects such as costs, advances, salary and benefits. They can likewise incorporate remarks from the board on the business’ action and estimated problems which may emerge later on. The different reports that the organization discharges give a general image of the organization's status and can be utilized to demonstrate how productive the organization is marching towards its vision, impact stock costs and decide whether they are pertinent for credits. 3.4 Insurance Fraud Insurance fraud can occur most likely at the time of claim in an insurance procedure. A typical case occurring in an automobile company, where repair or damage costs are claimed excessively. This can lead to fake claims and kickbacks to dealers [42]. 3.5 Mortgage Fraud A particular type of money related fraud that alludes to the control of property or mortgage documents. It leads to distorting the estimation of a property, which impacts a moneylender to support an advance for it [42]. 3.6 Money Laundering Money laundering is a technique handled by fraudsters to mask the source of the cash, giving them the presence of real pay and making it hard to follow their crimes [43]. Below figure shows various categories of Financial Fraud:. Fig 1: Financial Fraud Categories ISSN: 2005-4238 IJAST Copyright ⓒ 2019 SERSC. 274.

(6) International Journal of Advanced Science and Technology Vol. 28, No. 17, (2019), pp. 270-280. It shows a deep perspective of current frauds in the financial system. Learning the various kinds of fraud is important to examine the attributes of fraudulent transactions as well as to pick refined strategies that evade fraud detection complexity issues. IV. Financial Fraud Detection Using Data Analytics (DA) Fraud recognition system faces many challenges with the rising number of transactions performed by clients at every second. This causes heavy burden to financial structures. The size of everyday online transactions has expanded to a Petabytes (PB). In this situation, handling of data model or structure, training and arriving at predictive analysis with least delay and high accuracy is very hard to achieve (in financial fraud detection system). In order to fight over this challenge, financial experts propose the data analytical framework to verify whether performed transaction is legal or fraudulent [44]. Fraud prevention using DA is possible due to:. Below table shows the strengths and limitations of data analytic approaches for various models like Logistic Model, Text mining, Artificial Immune System and Process mining. Table 2 – Strengths and Limitations of Data Analytic Methods. ISSN: 2005-4238 IJAST Copyright ⓒ 2019 SERSC. 275.

(7) International Journal of Advanced Science and Technology Vol. 28, No. 17, (2019), pp. 270-280. It focuses on exploring the positive and negative factors of Data analytic methods that influence on financial datasets. This analysis can help to adjust strategy and extract the common attributes which affects the detection quality of applying data analytic methods. In future, the proposed system is to specify a set of properties that is required for minimizing the false rate and maximizing the accuracy of fraud detection approach. V. Machine Learning Techniques for Financial Fraud Classification Many researchers and financial organizations are applying artificial intelligence and machine learning techniques to detect financial frauds. Machine learning is a technique in which machines are tuned to learn concepts by using data as well as statistical inspection. Machine learning approaches can be categorized as Supervised and Unsupervised learning methods. Supervised methods predict based on labelled input data sets. Unlabeled data elements are used in unsupervised methods. These algorithms can be used to detect and classify financial frauds with financial transaction datasets. 5.1 Classification Based on Performance evaluation Lot of metrics are available to evaluate the performance but, most frequently used parameters are "Accuracy" and "Sensitivity". Below table shows the performance analysis using these parameters for various existing financial fraud classification methods. Table 3 – Parameters for Performance Measures. Above metrics shows promising results with existing models. But to achieve higher and greater levels of efficiency, it is recommended to combine analytics and machine learning with cognitive technologies to processing large volumes of data with minimal amount of time. 5.2 Classification Algorithms Based on Fraud Type Each fraud is characterized with attributes of different nature. Selecting classification algorithms based on fraud types is a best way to improve the classification system. By analyzing the previous classification methods on the category of fraud, suitable methods can be recognized for a particular type of fraud.. ISSN: 2005-4238 IJAST Copyright ⓒ 2019 SERSC. 276.

(8) International Journal of Advanced Science and Technology Vol. 28, No. 17, (2019), pp. 270-280. From literature survey, it is understood most of papers have concentrated on two types of fraud viz., financial statement frauds and credit card frauds. Some of the studies have focused on securities, commodities as well as merchandise frauds. Below figure shows the classification algorithms based on fraud types:. Fig 2: Classification algorithms based on fraud types VI. Proposed Model with Methodology Traditional, "Rule Based" approaches are built on known criteria and defined rule sets. With that, it makes it hard to distinguish new attacks and fraud intents. To find or detect fraud instances and accomplish higher levels of accuracy and sensitivity, approaches using analytics and machine learning (supervised or unsupervised systems) have been focused. Our proposed research investigates most of the latest utilized strategies in financial fraud investigations of AI from 2001 to 2018. The structure of the proposed model is exhibited in flow diagram as shown below: Financial Fraud Data Sources (Transaction Data, Fraud Data, Other Data). Data Warehouse. Intelligent Data analytics system (Data filter and ranked algorithms, Sampling, Feature selection & Evaluation techniques). Artificial Intelligence system- Machine/Deep learning algorithms (Testing Set, Training Set). Result (Classify the fraudulent and Non-fraudulent transaction). Fig 3: Stages of Proposed Model. ISSN: 2005-4238 IJAST Copyright ⓒ 2019 SERSC. 277.

(9) International Journal of Advanced Science and Technology Vol. 28, No. 17, (2019), pp. 270-280. Various stages of proposed model are depicted above. There are millions of transactions each day. Firstly, all the relevant data for analyzing are collected by the buffer of the corresponding system. After buffering the data, it is normalized in the standard format so that it can be grounded to suitable format for subsequent process. To standardize the huge data, it needs very proficient data analytics algorithms. The next step is to build models that can detect fraud. This is done with training datasets extracted from the wide range of transactions and data sources. VII. Conclusion Predicting fraud behavior factors are challenging as technological access to performing financial transactions are in the forefront. Alongside, "Non-linear" relationships among transaction data and fraudulent factors in financial transaction make predictions difficult. Data Analytics and Machine Learning methods are suitable for solving nonlinear approximations and can be applied with greater accuracy levels in financial fraud detection system. This paper aimed to provide survey on recent financial fraud detection systems using Data Analytics and AI techniques. This survey has explored some of the major contributions on financial fraud detection studies published from 2001 to 2018. With this wide survey readers can infer understanding about fraud types, the nature of financial data, performance metrics and techniques used in existing systems for recognition of financial fraud detections. Emerging AI techniques with advancements are recommended for improved accuracy levels and performance. Deep learning methods are one of such techniques, which are not widely applied in such patents, although they are known to show their potential for fraud detection in financial system. Future developments may also benefit from advanced analysis. References [1] Lavion, Didier et al .(2018) "PwC's Global Economic Crime and Fraud Survey 2018". PwC.com [2] Kount (2016) "Mobile payments fraud survey report". Technical report [3] A. Adjaoute (2013) "Systems and methods for adaptive identification of sources of fraud". U. S. Patent 8,458,069 [4] Zhang G, Eddy Patuwo B and Y Hu M (1998) "Forecasting with artificial neural networks - The state of the art". International journal of forecasting 14, 35-62 [5] Sohl JE and Venkatachalam A (1995) "A neural network approach to forecasting model selection". Information & Management 29, 297-303 [6] Fanning KM and Cogger KO (1998) "Neural network detection of management fraud using published financial data". International Journal of Intelligent Systems in Accounting, Finance & Management 7, 21-41 [7] Bolton RJ and Hand DJ (2002) "Statistical fraud detection: A review". Statistical Science 235-49 [8] Bolton RJ and Hand DJ (2001) "Unsupervised profiling methods for fraud detection". Credit Scoring and Credit Control VII 235-55 [9] Zabihollah Rezaee, Ben L. Kedia (2012) "Role of corporate Governance participants in preventing and detecting Financial Statement frauds". Journal of Forensic & Investigative Accounting Vol. 4, Issue 2 [10] Vatsa V, Sural S and Majumdar AK (2005) "A game-theoretic approach to credit card fraud detection". Information Systems Security Vol. pp. 263-76 Springer [11] Yang W-S and Hwang S-Y (2006) "A process-mining framework for the detection of healthcare fraud and abuse". Expert Systems with Applications 31, 56-68 [12] Pinquet J, Ayuso M, and Guillen M (2007) "Selection bias and auditing policies for insurance claims". Journal of Risk and Insurance 74, 425-40. ISSN: 2005-4238 IJAST Copyright ⓒ 2019 SERSC. 278.

(10) International Journal of Advanced Science and Technology Vol. 28, No. 17, (2019), pp. 270-280. [13] Bai B, Yen J, and Yang X (2008) "False financial statements: characteristics of China's listed companies and CART detecting approach". International Journal of Information Technology & Decision Making 7, 339-59 [14] Bermúdez L, Pérez J, Ayuso M, Gómez E, and Vázquez F (2008) "A Bayesian dichoto-mous model with asymmetric link for fraud in insurance". Insurance: Mathematics and Economics 42, 779-86 [15] Holton C (2009) "Identifying disgruntled employee systems fraud risk through text mining: A simple solution for a multi-billion dollar problem". Decision Support Systems 46, 853-64 [16] Panigrahi S, Kundu A, Sural S, and Majumdar AK (2009) "Credit card fraud detection: A fusion approach using Dempster–Shafer theory and Bayesian learning". Information Fusion 10, 354-63 [17] Cecchini M, Aytug H, Koehler GJ, and Pathak P (2010) "Making words work: Using finan-cial text as a predictor of financial events". Decision Support Systems 50, 164-75, [18] Ravisankar P, Ravi V, Raghava Rao G, and Bose I (2011) "Detection of financial statement fraud and feature selection using data mining techniques". Decision Support Systems 50, 491-500 [19] Wong N, Ray P, Stephens G, and Lewis L (2012) "Artificial immune systems for the detec-tion of credit card fraud: an architecture, prototype and preliminary results". Information Systems Journal 22, 53-76 [20] Huang SY (2013) "Fraud Detection Model by Using Support Vector Machine Techniques". JDCTA: International Journal of Digital Content Technology and its Applications 7, 32-42 [21] Sahin Y, Bulkan S, and Duman E (2013) "A cost-sensitive decision tree approach for fraud detection". Expert Systems with Applications 40, 5916-23 [22] Dong W, Liao SS, Fang B, Cheng X, Chen Z, and Fan W (2014) "The Detection of fraudulent financial statements: An Integrated Language Model". ACM Digital library Volume 57 Issue C [23] Sohl JE and Venkatachalam A (1995) "A neural network approach to forecasting model selection". Information & Management 29, 297-303 [24] Weiyu Si, Jinke Li, Ruonan Rao,Peng Ding (2017) "A Multi-Objective Deep Reinforcement Learning Approach for Stock Index Futures’s Intraday Trading" 10th International Symposium on Computational Intelligence and Design, IEEE [25] Roger A.Leite, Theresia Gschwandtner, Silvia Miksch, Simone Kriglstein, MargitPohl, ErichGstrein, and Johannes Kuntner (2018) "EVA: Visual Analytics to Identify Fraudulent Events" IEEE Transactions on Visualization and Computer Graphics, Vol 24 [26] Dongxu Huang, Dejun Mu, Libin Yang, Xiaoyan Cai (2018) "CoDetect: Financial Fraud Detection with Anomaly Feature Detection" 2169-3536 (c), Access 2018 2816564, IEEE [27] Z. Massimiliano et al., (2017) "Credit card fraud detection through parenclitic network analysis" Intelligent Financial Fraud Detection: A Comprehensive Review, Research gate [28] M. Carminati, L. Valentini, and S. Zanero (2017) "A Supervised Auto-Tuning Approach for a Banking Fraud Detection System". In proceeding of the International Conference on Cyber Security Cryptography and Machine Learning, Springer, Cham, Switzerland [29] P.Yamini (2017) "Credit Card Fraud Detection using Deep Learning" International Journal of Advanced Research in Computer Science. ISSN: 2005-4238 IJAST Copyright ⓒ 2019 SERSC. 279.

(11) International Journal of Advanced Science and Technology Vol. 28, No. 17, (2019), pp. 270-280. [30] F. Fadaei Noghani and M. Moattar (2017) "Ensemble Classification and Extended Feature Selection for Credit Card Fraud Detection" Journal of AI and Data Mining, vol. 5, no. 2, pp. 235–243 [31] G. Maryam and S. Mohammad Abadeh (2017) "Fraud Detection of Credit Cards Using Neuro-fuzzy Approach Based on TLBO and PSO Algorithms" Journal of Computer & Robotics, vol. 10, no. 2, pp. 57–68 [32] M. Sorkun Cihan and T. Toraman (2017) "Fraud Detection on Financial Statements Using Data Mining Techniques" International Journal of Intelligent Systems and Applications in Engineering, vol. 5, no. 3, pp. 132–134 [33] M. Kamboj and G. Shankey (2016) "Credit Card Fraud Detection and False Alarms Reduction using Support Vector Machines" International Journal of Advance Research, Ideas and Innovations in Technology, vol. 2, no. 4 [34] F. Kang et al., (2016) "Credit Card Fraud Detection Using Convolutional Neural Networks" in Proceeding of the International Conference on Neural Information Processing, Springer International Publishing [35] M. Khodabakhshi and M. Fartash (2016) "Fraud Detection in Banking Using Knn (K-Nearest Neighbor) Algorithm" International Conference on Research in Science and Technology [36] S. Kamaruddin and R. Vadlamani (2016) "Credit Card Fraud Detection using Big Data Analytics: Use of PSOAANN based One-Class Classification" In proceeding of the International Conference on Informatics and Analytics, ACM [37] S. Sharmila and S. Panigrahi (2016) "Use of fuzzy clustering and support vector machine for detecting fraud in mobile telecommunication networks". International Journal of Security and Networks, vol. 11, no. 1-2, pp. 3–11 [38] M. Carminati et al., "BankSealer (2015) A decision support system for online banking fraud analysis and investigation" Computers & Security, vol. 53, pp. 175– 186 [39] M. Bansal and Suman (2014) "Credit card fraud detection using self organised map" International Journal of Information & Computation Technology, vol.4,no.13,pp.1343–1348 [40] Bhattacharyya S, Jha S, Tharakunnel K, and Westland JC (2011) "Data mining for credit card fraud: A comparative study". Decision Support Systems 50, 602-13 [41] Glancy FH and Yadav SB (2011) "A computational model for financial reporting fraud detection". Decision Support Systems 50, 595-601 [42] Ngai E, Hu Y, Wong Y, Chen Y, and Sun X (2011) "The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature". Decision Support Systems 50, 559-69 [43] Duman E and Ozcelik MH (2011) "Detecting credit card fraud by genetic algorithm and scatter search". Expert Systems with Applications 38, 13057-63 [44] Suraj Patil, Varsha Nemade, PiyushKumar Soni (2018) "Predictive Modelling for Credit Card Fraud Detection using Data Analytics" International Conference on Computational Intelligence and Data Science Elsevier, 1877- 0509. ISSN: 2005-4238 IJAST Copyright ⓒ 2019 SERSC. 280.

(12)

No results found