Data analysis for fraud detection

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Fraud represents a significant problem for governments and businesses and specialized analysis techniques for discovering fraud using them are required. Some of these methods include knowledge discovery in databases (KDD), data mining, machine learning and statistics. They offer applicable and successful solutions in different areas of electronic fraud crimes. [1]

Contents

In general, the primary reason to use data analytics techniques is to tackle fraud since many internal control systems have serious weaknesses. For example, the currently prevailing approach employed by many law enforcement agencies to detect companies involved in potential cases of fraud consists in receiving circumstantial evidence or complaints from whistleblowers. [2] As a result, a large number of fraud cases remain undetected and unprosecuted. In order to effectively test, detect, validate, correct error and monitor control systems against fraudulent activities, businesses entities and organizations rely on specialized data analytics techniques such as data mining, data matching, the sounds like function, regression analysis, clustering analysis, and gap analysis. [3] Techniques used for fraud detection fall into two primary classes: statistical techniques and artificial intelligence. [4]

Statistical techniques

Examples of statistical data analysis techniques are:

Some forensic accountants specialize in forensic analytics which is the procurement and analysis of electronic data to reconstruct, detect, or otherwise support a claim of financial fraud. The main steps in forensic analytics are data collection, data preparation, data analysis, and reporting. For example, forensic analytics may be used to review an employee's purchasing card activity to assess whether any of the purchases were diverted or divertible for personal use.

Artificial intelligence

Fraud detection is a knowledge-intensive activity. The main AI techniques used for fraud detection include:

Other techniques such as link analysis, Bayesian networks, decision theory, and sequence matching are also used for fraud detection. [4] A new and novel technique called System properties approach has also been employed where ever rank data is available. [6]

Statistical analysis of research data is the most comprehensive method for determining if data fraud exists. Data fraud as defined by the Office of Research Integrity (ORI) includes fabrication, falsification and plagiarism.

Machine learning and data mining

Early data analysis techniques were oriented toward extracting quantitative and statistical data characteristics. These techniques facilitate useful data interpretations and can help to get better insights into the processes behind the data. Although the traditional data analysis techniques can indirectly lead us to knowledge, it is still created by human analysts. [7]

To go beyond, a data analysis system has to be equipped with a substantial amount of background knowledge, and be able to perform reasoning tasks involving that knowledge and the data provided. [7] In effort to meet this goal, researchers have turned to ideas from the machine learning field. This is a natural source of ideas, since the machine learning task can be described as turning background knowledge and examples (input) into knowledge (output).

If data mining results in discovering meaningful patterns, data turns into information. Information or patterns that are novel, valid and potentially useful are not merely information, but knowledge. One speaks of discovering knowledge, before hidden in the huge amount of data, but now revealed.

The machine learning and artificial intelligence solutions may be classified into two categories: 'supervised' and 'unsupervised' learning. These methods seek for accounts, customers, suppliers, etc. that behave 'unusually' in order to output suspicion scores, rules or visual anomalies, depending on the method. [8]

Whether supervised or unsupervised methods are used, note that the output gives us only an indication of fraud likelihood. No stand alone statistical analysis can assure that a particular object is a fraudulent one, but they can identify them with very high degrees of accuracy. As a result, effective collaboration between machine learning model and human analysts is vital to the success of fraud detection applications. [9]

Supervised learning

In supervised learning, a random sub-sample of all records is taken and manually classified as either 'fraudulent' or 'non-fraudulent' (task can be decomposed on more classes to meet algorithm requirements). Relatively rare events such as fraud may need to be over sampled to get a big enough sample size. [10] These manually classified records are then used to train a supervised machine learning algorithm. After building a model using this training data, the algorithm should be able to classify new records as either fraudulent or non-fraudulent.

Supervised neural networks, fuzzy neural nets, and combinations of neural nets and rules, have been extensively explored and used for detecting fraud in mobile phone networks and financial statement fraud. [11] [12]

Bayesian learning neural network is implemented for credit card fraud detection, telecommunications fraud, auto claim fraud detection, and medical insurance fraud. [13]

Hybrid knowledge/statistical-based systems, where expert knowledge is integrated with statistical power, use a series of data mining techniques for the purpose of detecting cellular clone fraud. Specifically, a rule-learning program to uncover indicators of fraudulent behaviour from a large database of customer transactions is implemented. [14]

Cahill et al. (2000) design a fraud signature, based on data of fraudulent calls, to detect telecommunications fraud. For scoring a call for fraud its probability under the account signature is compared to its probability under a fraud signature. The fraud signature is updated sequentially, enabling event-driven fraud detection.

Link analysis comprehends a different approach. It relates known fraudsters to other individuals, using record linkage and social network methods. [15] [16]

This type of detection is only able to detect frauds similar to those which have occurred previously and been classified by a human. To detect a novel type of fraud may require the use of an unsupervised machine learning algorithm.

Unsupervised learning

In contrast, unsupervised methods don't make use of labelled records.

Bolton and Hand use Peer Group Analysis and Break Point Analysis applied on spending behaviour in credit card accounts. [17] Peer Group Analysis detects individual objects that begin to behave in a way different from objects to which they had previously been similar. Another tool Bolton and Hand develop for behavioural fraud detection is Break Point Analysis. [17] Unlike Peer Group Analysis, Break Point Analysis operates on the account level. A break point is an observation where anomalous behaviour for a particular account is detected. Both the tools are applied on spending behaviour in credit card accounts.

A combination of unsupervised and supervised methods for credit card fraud detection is in Carcillo et al (2019). [18]

Geolocation

This section is an excerpt from Internet geolocation § Fraud detection.[ edit ]

Online retailers and payment processors use geolocation to detect possible credit card fraud by comparing the user's location to the billing address on the account or the shipping address provided. A mismatch – an order placed from the US on an account number from Tokyo, for example – is a strong indicator of potential fraud. IP address geolocation can be also used in fraud detection to match billing address postal code or area code. [19] Banks can prevent "phishing" attacks, money laundering and other security breaches by determining the user's location as part of the authentication process. Whois databases can also help verify IP addresses and registrants. [20]

Government, law enforcement and corporate security teams use geolocation as an investigatory tool, tracking the Internet routes of online attackers to find the perpetrators and prevent future attacks from the same location.

Available datasets

A major limitation for the validation of existing fraud detection methods is the lack of public datasets. [21] One of the few examples is the Credit Card Fraud Detection dataset [22] made available by the ULB Machine Learning Group. [23]

See also

Related Research Articles

<span class="mw-page-title-main">Supervised learning</span> A paradigm in machine learning

Supervised learning (SL) is a paradigm in machine learning where input objects and a desired output value train a model. The training data is processed, building a function that maps new data on expected output values. An optimal scenario will allow for the algorithm to correctly determine output values for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way. This statistical quality of an algorithm is measured through the so-called generalization error.

<span class="mw-page-title-main">Data mining</span> Process of extracting and discovering patterns in large data sets

Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information from a data set and transforming the information into a comprehensible structure for further use. Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.

<span class="mw-page-title-main">Pattern recognition</span> Automated recognition of patterns and regularities in data

Pattern recognition is the automated recognition of patterns and regularities in data. While similar, pattern recognition (PR) is not to be confused with pattern machines (PM) which may possess (PR) capabilities but their primary function is to distinguish and create emergent pattern. PR has applications in statistical data analysis, signal processing, image analysis, information retrieval, bioinformatics, data compression, computer graphics and machine learning. Pattern recognition has its origins in statistics and engineering; some modern approaches to pattern recognition include the use of machine learning, due to the increased availability of big data and a new abundance of processing power.

<span class="mw-page-title-main">Machine learning</span> Study of algorithms that improve automatically through experience

Machine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines "discover" their "own" algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches. Machine-learning approaches have been applied to large language models, computer vision, speech recognition, email filtering, agriculture and medicine, where it is too costly to develop algorithms to perform the needed tasks.

Unsupervised learning is a paradigm in machine learning where, in contrast to supervised learning and semi-supervised learning, algorithms learn patterns exclusively from unlabeled data.

In statistics, classification is the problem of identifying which of a set of categories (sub-populations) an observation belongs to. Examples are assigning a given email to the "spam" or "non-spam" class, and assigning a diagnosis to a given patient based on observed characteristics of the patient.

Adaptive resonance theory (ART) is a theory developed by Stephen Grossberg and Gail Carpenter on aspects of how the brain processes information. It describes a number of neural network models which use supervised and unsupervised learning methods, and address problems such as pattern recognition and prediction.

In predictive analytics, data science, machine learning and related fields, concept drift or drift is an evolution of data that invalidates the data model. It happens when the statistical properties of the target variable, which the model is trying to predict, change over time in unforeseen ways. This causes problems because the predictions become less accurate as time passes. Drift detection and drift adaptation are of paramount importance in the fields that involve dynamically changing data and data models.

<span class="mw-page-title-main">Anomaly detection</span> Approach in data analysis

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<span class="mw-page-title-main">Election forensics</span> Statistical methods to possibly find voter fraud

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References

  1. ↑ Chuprina, Roman (13 April 2020). "The In-depth 2020 Guide to E-commerce Fraud Detection". www.datasciencecentral.com. Retrieved 2020-05-24.
  2. ↑ Velasco, Rafael B.; Carpanese, Igor; Interian, Ruben; Paulo Neto, Octávio C. G.; Ribeiro, Celso C. (2020-05-28). "A decision support system for fraud detection in public procurement". International Transactions in Operational Research. 28: 27–47. doi: 10.1111/itor.12811 . ISSN   0969-6016.
  3. 1 2 3 4 Bolton, R. and Hand, D. (2002). Statistical fraud detection: A review. Statistical Science 17 (3), pp. 235-255
  4. 1 2 G. K. Palshikar, The Hidden Truth – Frauds and Their Control: A Critical Application for Business Intelligence, Intelligent Enterprise, vol. 5, no. 9, 28 May 2002, pp. 46–51.
  5. 1 2 3 Al-Khatib, Adnan M. (2012). "Electronic Payment Fraud Detection Techniques". World of Computer Science and Information Technology Journal. 2. S2CID   214778396.
  6. ↑ Vani, G. K. (February 2018). "How to detect data collection fraud using System properties approach". Multilogic in Science. VII (SPECIAL ISSUE ICAAASTSD-2018). ISSN   2277-7601 . Retrieved February 2, 2019.
  7. 1 2 Michalski, R. S., I. Bratko, and M. Kubat (1998). Machine Learning and Data Mining – Methods and Applications. John Wiley & Sons Ltd.
  8. ↑ Bolton, R. & Hand, D. (2002). Statistical Fraud Detection: A Review (With Discussion). Statistical Science 17(3): 235–255.
  9. ↑ Tax, N. & de Vries, K.J. & de Jong, M. & Dosoula, N. & van den Akker, B. & Smith, J. & Thuong, O. & Bernardi, L. Machine Learning for Fraud Detection in E-Commerce: A Research Agenda. Proceedings of the KDD International Workshop on Deployable Machine Learning for Security Defense (ML hat). Springer, Cham, 2021.
  10. ↑ Dal Pozzolo, A. & Caelen, O. & Le Borgne, Y. & Waterschoot, S. & Bontempi, G. (2014). Learned lessons in credit card fraud detection from a practitioner perspective. Expert systems with applications 41: 10 4915–4928.
  11. ↑ Green, B. & Choi, J. (1997). Assessing the Risk of Management Fraud through Neural Network Technology. Auditing 16(1): 14–28.
  12. ↑ Estevez, P., C. Held, and C. Perez (2006). Subscription fraud prevention in telecommunications using fuzzy rules and neural networks. Expert Systems with Applications 31, 337–344.
  13. ↑ Bhowmik, Rekha Bhowmik. "35 Data Mining Techniques in Fraud Detection". Journal of Digital Forensics, Security and Law. University of Texas at Dallas.
  14. ↑ Fawcett, T. (1997). AI Approaches to Fraud Detection and Risk Management: Papers from the 1997 AAAI Workshop. Technical Report WS-97-07. AAAI Press.
  15. ↑ Phua, C.; Lee, V.; Smith-Miles, K.; Gayler, R. (2005). "A Comprehensive Survey of Data Mining-based Fraud Detection Research". arXiv: 1009.6119 . doi:10.1016/j.chb.2012.01.002. S2CID   50458504.{{cite journal}}: Cite journal requires |journal= (help)
  16. ↑ Cortes, C. & Pregibon, D. (2001). Signature-Based Methods for Data Streams. Data Mining and Knowledge Discovery 5: 167–182.
  17. 1 2 Bolton, R. & Hand, D. (2001). Unsupervised Profiling Methods for Fraud Detection. Credit Scoring and Credit Control VII.
  18. ↑ Carcillo, Fabrizio; Le Borgne, Yann-Aël; Caelen, Olivier; Kessaci, Yacine; Oblé, Frédéric; Bontempi, Gianluca (16 May 2019). "Combining unsupervised and supervised learning in credit card fraud detection". Information Sciences. 557: 317–331. doi:10.1016/j.ins.2019.05.042. ISSN   0020-0255. S2CID   181839660.
  19. ↑ Vacca, John R. (2003). Identity Theft. Prentice Hall Professional. p. 400. ISBN   9780130082756.
  20. ↑ Barba, Robert (2017-11-18). "Sharing your location with your bank seems creepy, but it's useful". The Morning Call . Retrieved 2018-01-10.
  21. ↑ Le Borgne, Yann-Aël; Bontempi, Gianluca (2021). "Machine Learning for Credit Card Fraud Detection - Practical Handbook" . Retrieved 26 April 2021.
  22. ↑ "Credit Card Fraud Detection". kaggle.com.
  23. ↑ "ULB Machine Learning Group". mlg.ulb.ac.be.
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