scholarly journals Combating money laundering with machine learning – applicability of supervised-learning algorithms at cryptocurrency exchanges

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Eric Pettersson Ruiz ◽  
Jannis Angelis
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Money Laundering ◽  
Qualitative Interviews ◽  
Learning Algorithms ◽  
Content Type ◽  
Study Results ◽  
Supervised Learning Algorithms ◽  
Multiple Accounts ◽  
Combat Money Laundering

Purpose This study aims to explore how to deanonymize cryptocurrency money launderers with the help of machine learning (ML). Money is laundered through cryptocurrencies by distributing funds to multiple accounts and then reexchanging the crypto back. This process of exchanging currencies is done through cryptocurrency exchanges. Current preventive efforts are outdated, and ML may provide novel ways to identify illicit currency movements. Hence, this study investigates ML applicability for combatting money laundering activities using cryptocurrency. Design/methodology/approach Four supervised-learning algorithms were compared using the Bitcoin Elliptic Dataset. The method covered a quantitative analysis of the algorithmic performance, capturing differences in three key evaluation metrics of F1-scores, precision and recall. Two complementary qualitative interviews were performed at cryptocurrency exchanges to identify fit and applicability of the algorithms. Findings The study results show that the current implemented ML tools for preventing money laundering at cryptocurrency exchanges are all too slow and need to be optimized for the task. The results also show that while not one single algorithm is most suitable for detecting transactions related to money-laundering, the specific applicability of the decision tree algorithm is most suitable for adoption by cryptocurrency exchanges. Originality/value Given the growth of cryptocurrency use, this study explores the newly developed field of algorithmic tools to combat illicit currency movement, in particular in the growing arena of cryptocurrencies. The study results provide new insights into the applicability of ML as a tool to combat money laundering using cryptocurrency exchanges.

Machine Learning and Data Mining in Bioinformatics

2012 ◽  
pp. 695-703
Author(s):  
George Tzanis ◽  
Christos Berberidis ◽  
Ioannis Vlahavas
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Reinforcement Learning ◽  
Supervised Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The World ◽  
Supervised Learning Algorithms ◽  
Computational Systems

Machine learning is one of the oldest subfields of artificial intelligence and is concerned with the design and development of computational systems that can adapt themselves and learn. The most common machine learning algorithms can be either supervised or unsupervised. Supervised learning algorithms generate a function that maps inputs to desired outputs, based on a set of examples with known output (labeled examples). Unsupervised learning algorithms find patterns and relationships over a given set of inputs (unlabeled examples). Other categories of machine learning are semi-supervised learning, where an algorithm uses both labeled and unlabeled examples, and reinforcement learning, where an algorithm learns a policy of how to act given an observation of the world.

Machine Learning and Data Mining in Bioinformatics

2009 ◽  
pp. 612-621 ◽  
Author(s):  
George Tzanis ◽  
Christos Berberidis ◽  
Ioannis Vlahavas
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Reinforcement Learning ◽  
Supervised Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The World ◽  
Supervised Learning Algorithms ◽  
Computational Systems

Machine learning is one of the oldest subfields of artificial intelligence and is concerned with the design and development of computational systems that can adapt themselves and learn. The most common machine learning algorithms can be either supervised or unsupervised. Supervised learning algorithms generate a function that maps inputs to desired outputs, based on a set of examples with known output (labeled examples). Unsupervised learning algorithms find patterns and relationships over a given set of inputs (unlabeled examples). Other categories of machine learning are semi-supervised learning, where an algorithm uses both labeled and unlabeled examples, and reinforcement learning, where an algorithm learns a policy of how to act given an observation of the world.

Predicting the Authenticity of Banknotes Using Supervised Learning

2020 ◽  
Vol 1 (2) ◽  
pp. 1-4
Author(s):  
Priyam Guha ◽  
Abhishek Mukherjee ◽  
Abhishek Verma
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Confusion Matrix ◽  
Learning Algorithms ◽  
High Accuracy ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
False Negatives ◽  
Supervised Learning Algorithms ◽  
Very High

This research paper deals with using supervised machine learning algorithms to detect authenticity of bank notes. In this research we were successful in achieving very high accuracy (of the order of 99%) by applying some data preprocessing tricks and then running the processed data on supervised learning algorithms like SVM, Decision Trees, Logistic Regression, KNN. We then proceed to analyze the misclassified points. We examine the confusion matrix to find out which algorithms had more number of false positives and which algorithm had more number of False negatives. This research paper deals with using supervised machine learning algorithms to detect authenticity of bank notes. In this research we were successful in achieving very high accuracy (of the order of 99%) by applying some data preprocessing tricks and then running the processed data on supervised learning algorithms like SVM, Decision Trees, Logistic Regression, KNN. We then proceed to analyze the misclassified points. We examine the confusion matrix to find out which algorithms had more number of false positives and which algorithm had more number of False negatives.

Out With the Old and in With the New? An Empirical Comparison of Supervised Learning Algorithms to Predict Recidivism

2015 ◽  
Vol 28 (6) ◽  
pp. 570-600 ◽  
Author(s):  
Grant Duwe ◽  
KiDeuk Kim
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Predictive Validity ◽  
Logistic Model ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment Instruments ◽  
Data Set ◽  
Supervised Learning Algorithms ◽  
Overall Performance

Recent research has produced mixed results as to whether newer machine learning algorithms outperform older, more traditional methods such as logistic regression in predicting recidivism. In this study, we compared the performance of 12 supervised learning algorithms to predict recidivism among offenders released from Minnesota prisons. Using multiple predictive validity metrics, we assessed the performance of these algorithms across varying sample sizes, recidivism base rates, and number of predictors in the data set. The newer machine learning algorithms generally yielded better predictive validity results. LogitBoost had the best overall performance, followed by Random forests, MultiBoosting, bagged trees, and logistic model trees. Still, the gap between the best and worst algorithms was relatively modest, and none of the methods performed the best in each of the 10 scenarios we examined. The results suggest that multiple methods, including machine learning algorithms, should be considered in the development of recidivism risk assessment instruments.

scholarly journals Developing a Framework for Detecting Phishing URLs using Machine Learning

2021 ◽  
Vol 11 (11) ◽  
pp. 61-67
Author(s):  
Nguyen Tung Lam ◽  
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Learning Algorithms ◽  
End Users ◽  
Research Results ◽  
Supervised Learning Algorithms ◽  
User Data ◽  
Abnormal Behaviors

The attack technique targeting end-users through phishing URLs is very dangerous nowadays. With this technique, attackers could steal user data or take control of the system, etc. Therefore, early detecting phishing URLs is essential. In this paper, we propose a method to detect phishing URLs based on supervised learning algorithms and abnormal behaviors from URLs. Finally, based on the research results, we build a framework for detecting phishing URLs through endusers. The novelty and advantage of our proposed method are that abnormal behaviors are extracted based on URLs which are monitored and collected directly from attack campaigns instead of using inefficient old datasets. Keywords— phishing URLs; detecting phishing URLs; abnormal behaviors of phishing URLs; Machine learning

Big Data Mining Algorithms

2021 ◽  
pp. 768-777
Author(s):  
M. Govindarajan
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Big Data Mining ◽  
Supervised Learning Algorithms

Big data mining involves knowledge discovery from these large data sets. The purpose of this chapter is to provide an analysis of different machine learning algorithms available for performing big data analytics. The machine learning algorithms are categorized in three key categories, namely, supervised, unsupervised, and semi-supervised machine learning algorithm. The supervised learning algorithms are trained with a complete set of data, and thus, the supervised learning algorithms are used to predict/forecast. Example algorithms include logistic regression and the back propagation neural network. The unsupervised learning algorithms starts learning from scratch, and therefore, the unsupervised learning algorithms are used for clustering. Example algorithms include: the Apriori algorithm and K-Means. The semi-supervised learning combines both supervised and unsupervised learning algorithms. The semi-supervised algorithms are trained, and the algorithms also include non-trained learning.

scholarly journals Evaluating the Performance Estimators via Machine Learning Supervised Learning Algorithms for Dataset Threshold

2015 ◽  
Vol 119 (14) ◽  
pp. 1-6 ◽  
Author(s):  
Warda Imtiaz ◽  
Humaraia Abdul Ghafoor ◽  
Rabeea Sehar ◽  
Tahira Mahboob ◽  
Memoona Khanum
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Learning Algorithms ◽  
Supervised Learning Algorithms

Money laundering through raw diamonds

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fabian Maximilian Johannes Teichmann ◽  
Marie-Christin Falker
Keyword(s):  
Law Enforcement ◽  
Money Laundering ◽  
Qualitative Content Analysis ◽  
Law Enforcement Agencies ◽  
Content Type ◽  
Compliance Officers ◽  
German Speaking ◽  
Three Stages ◽  
Practical Implications ◽  
Combat Money Laundering

Purpose This paper aims to illustrate how illegally obtained funds are laundered through raw diamonds in Austria, Germany, Liechtenstein and Switzerland. Design/methodology/approach To identify specific money laundering techniques involving raw diamonds, this study used a qualitative content analysis of data collected from 60 semi-standardized interviews with both criminals and prevention experts and a quantitative survey of 200 compliance officers. Findings Raw diamonds are extraordinarily suitable for money laundering in European German-speaking countries. In particular, they may be used in all three stages of the laundering process, namely, placement, layering and integration. Research limitations/implications Because the qualitative findings are based on semi-standardized interviews, their insights are limited to the perspectives of the 60 interviewees. Practical implications Identifying gaps in existing anti-money laundering mechanisms should provide compliance officers, law enforcement agencies and legislators with valuable insights into how criminals operate. Originality/value While prior studies focus on the methods used by organizations to combat money laundering and how to improve anti-money laundering measures, this paper investigates how money launderers operate to avoid detection, thereby illustrating authentic experiences. Its findings provide valuable insights into the minds of money launderers and combines criminal perspective with that of prevention experts.

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