This article was written by Gopalakrishnan Kumar, pursuing the Training program on Using AI for Business Growth Course from Skill Arbitrage, and edited by Koushik Chittella.

Introduction

Fraud is an intentional deceit, trickery, and misrepresentation of truth that results in an unlawful gain for the fraudster and harm for the victim in monetary aspects and information. Traditionally, the fraud detection process was manual, slow, and could not address the intricacies of the motive of the fraud. In the era of digital transactions and global connectivity, organisations such as financial institutions, e-commerce platforms, insurance agencies, healthcare providers, and many government organisations are under a constant threat of being cheated by fraudulent activities that may result in loss of money, damage to trust, and damage to integrity. It is highly impossible to detect fraudulent activities when there are lakhs of digital transactions happening worldwide. Data science is the only saviour in monitoring, identifying, and controlling the threats of fraudulent activities occurring worldwide.

Data science enables organisations to understand data, identify hidden patterns, spot hidden values, and detect outliers, which in turn help in predicting fraudulent behaviours in the data. As in today’s era of artificial intelligence, fraudulent activities multiply with the application of new technologies, it is mandatory for organisations to continuously evolve with data science technologies to combat fraudulent activities.

Need for data science in fraud detection

Fraud analysis is needed to protect the growth and development of the country because it prevents financial loss for organisations and customers by safeguarding the organisation’s reputation and preventing the legal liabilities associated with fraudulent activities.

In earlier times, manual inspections were adopted to detect fraudulent activities, which included checking the transactions and finding suspicious patterns, which was not economical in terms of time, money, and manpower. Moreover, it did not ensure accuracy and fairness. The primitive rule-based system of fraud detection depended on static parameters such as transaction amount, frequency, time, and geographical location. On the contrary, with the advent of technologies in data science such as machine learning algorithms, artificial intelligence, deep learning, neural networks, and NLP, it is now very dynamic and efficient to identify fraudulent activities. 

Instances of fraudulent transactions       

Victim of online credit card fraud

In 2017, one day around midnight, a person received an SMS stating that $100 had been swiped from the card account without authorization in a remote place in the USA. The next morning, a complaint was lodged by sending an email to the concerned bank and requesting to block the said card immediately. A complaint was lodged with the local police station, and a copy of the complaint was submitted to the bank with a request to investigate the issue and reverse the debit in the said credit card account. After about three weeks, the concerned bank confirmed the fraudulent transaction, and the amount was credited by the bank.

Another incident that happened a couple of months ago regarding the issue of a new credit card applied to a bank is worth mentioning. In this matter, an online application was submitted for a new credit card. A couple of days later, a person posing as a customer care executive of the credit card department of the concerned bank informed him that the card had been issued and incentives would be given on submission of the OTP received and confirming the last 4 digits of the card number. The bank was immediately informed about the said phone call and promptly blocked and cancelled the card, as no bank official would ask for any OTP or card details, thus closing the matter.

Finding anomalous behaviour in the dataset

It is mandatory to apply well-defined steps in the form of techniques and algorithms to find the anomalous behaviour of the given dataset as a data scientist. Out of various domains such as health care, life science, nutrition, social media, banking, and finance, there is a project ‘Loan Eligibility Prediction’ in the banking domain, which is worth mentioning. In this project, unsupervised machine learning algorithms, namely K-means clustering and the Gradient Boosting Regression algorithm for obtaining high accuracy percentages, and also supervised learning algorithms., namely Neural Network, LR, LDA, KNN, CART, NB, and SVM, are used to effectively verify the accuracy of the models. In this project, the data preprocessing and processing step helps in detecting the outliers that are deviating from the normal features of the loan eligibility criteria.

Basic steps in machine learning for fraud detection

To analyse the data in a systematic way and derive meaningful insights and inferences, it is mandatory to understand the nature of the data so that it is free from misconceptions. There are certain steps to be followed, and variable description data processing and preprocessing play a crucial role in making the data not only error-free but also authentic and genuine. 

Variable description

This is the first and foremost requirement of machine learning techniques. It helps in identifying the categorical and numerical variables of the independent variables. It helps in understanding and focusing on the objectives of the problem and in identifying the constraints of datasets, thus enabling the data scientist to be aware of the possible fraudulent patterns in the datasets.

Data Processing and Pre-processing 

Here are four important steps, which are:

• Data cleaning: It involves filling in the missing data, smoothing the noisy data, resolving the inconsistency, and removing the outliers. Missing values are handled by filling the tuples when the dataset is huge and numerous missing values are present within a tuple. The missing values can be filled in manually, or they can be predicted by the regression method or by adopting numerical methods like attributes. Noisy data is data that does not have meaningful information or produces unexplained variability in the dataset, which can be smoothed by techniques such as binning, regression, and clustering. Outliers are the tuples that lie outside the clusters, which can be identified and removed by clustering techniques. 
• Data integration: It is the process of merging data from multiple sources, which helps in the detection and resolution of data value conflicts. 
• Data transformation: Data transformation is the process of consolidating quality data into alternate forms by changing the structure, attributes, and values by using techniques such as generalisation, normalisation, attribute selection, and aggregation.
• Data reduction: When the dataset is too large to handle, there is a need to obtain a reduced representation of the dataset that is much smaller in volume but gives the same quality of analytical results. There are many techniques used, among which dimensionality reduction helps in feature extraction. This helps in reducing the number of redundant features by using principal component analysis (PCA).                                                           

Machine learning models for fraud detection

To avoid fraud detection, the fraudsters keep evolving their tactics. So, unsupervised learning models in machine learning should be used for fraud detection as they do not rely on pre-defined rules or labelled data and adapt to new fraudulent patterns. In this context, machine learning models such as random forest, decision tree, logistic regression, support vector machines, neural networks, ensemble methods, clustering algorithms, and anomaly detection methods are used. 

• Logistic Regression: It is a binary classification algorithm, the outcome of which is a fraudulent or non-fraudulent transaction.
• Decision Tree: Decision trees are graphical representations that show a complex relationship between features and target variables. It recognises the critical qualities of the system that depicts the malicious activities. 
• Support Vector Machines (SVM): When the data is non-linear, a technique called the kernel trick is used to find a decision boundary called a hyperplane to classify the data. 
• Neural Networks: Deep learning models such as CNNs and RNNs are used in processing unstructured data such as images, texts, and sequences to uncover intricate patterns and anomalies. 
• Ensemble Learning: It is used for multiple learning algorithms to obtain better predictive performance for fraud detection.
• Clustering Algorithms: Clustering algorithms such as k-means clustering and DBSCAN clustering group similar data points together based on features and identify outliers and anomalies. 

Fraud detection in industries

There are several fraudulent activities prevalent across industries. There should be appropriate models, strategies, and techniques to be adopted to prevent fraudulent activities. The following are the domains of industries where malicious behavioural patterns are prevalent at an alarming rate to such an extent that they result in financial loss and reputational damage to industries and consumers.

Finance sector

In the finance sector, credit and debit card systems are the most affected domains due to fraudulent activities. Due to the implementation of machine learning technologies, the financial sector can analyse transaction data, user behaviour patterns, and historical fraud cases by applying predictive models such as logistic regression, decision trees, and ensemble techniques. 

Insurance sector

Insurance companies face challenges in detecting fraudulent insurance claims, such as exaggerated damages, staged accidents, and false information. Insurance providers apply anomaly detection algorithms along with policyholders’ data patterns and external factors to detect fraudulent claims. 

Healthcare sector

Healthcare industries combat fraudulent activities in the area of Medicare and Medicaid programmes. Machine learning techniques such as anomaly detection algorithms and clustering techniques analyse medical claims data, provider histories, and patient information and identify fraudulent billing practices and collusion networks.

E-commerce platforms                 

E-commerce platforms face challenges in the form of malicious behaviour in online transactions such as account takeovers, payment fraud, and identity theft. Machine learning models, including neural networks and ensemble methods, can be deployed to detect fraudulent activities during checkout processes. By analysing user behaviours, transaction patterns, device fingerprints, and IP addresses, the platform identifies suspicious activities and blocks fraudulent transactions in real-time. 

Government agencies

Government agencies combat tax fraud by identifying anomalies such as inflated deductions, unreported income, and suspicious filing patterns using machine learning algorithms such as XGBoost, ANN (Artificial Neural Network), and SVM (Support Vector Machine).                 

Real-time monitoring and alerting

Real-time monitoring and alerting systems are the need of the hour for all industries to detect fraudulent activities as they occur and respond to and mitigate the fraudulent activities. Along with machine learning models, streaming data technologies such as Apache Kafka, Apache Flink, and Apache Spark and event processing engines like Apache Storm, Apache NiFi, and Amazon Kinesis are used to process a large volume of data. Graph-based algorithms detect suspicious network structures, unusual transaction flows, and fraud rings by identifying patterns of collusion and fraudulent activities. Graph-based algorithms detect suspicious network structures, unusual transaction flows, and fraud rings by identifying patterns of collusion and fraudulent activities. Real-time monitoring systems can automate responses and mitigation actions upon detecting fraudulent activities. Automated responses may include blocking transactions, flagging accounts for review, triggering additional authentication measures, and notifying fraud investigators.

Future trends in fraud detection

• Explainable AI (XAI) is the fast-emerging trend that makes machine learning more understandable, thus creating trust, fostering collaboration between data scientists and domain experts, and enabling regulatory compliance.
• Deep learning techniques such as CNN and RNN are used for unstructured data like text, images, and sequences to process complex data to detect anomalies.
• Blockchain-based solutions provide a decentralised and immutable ledger that records transactions and identity-related information securely and reduces fraud risk and data integrity.
• AI-powered fraud investigations like NLP and cognitive computing techniques automate fraud triage, evidence gathering, pattern recognition, and decision support for fraud investigators.

Conclusion

Fraudulent activities are now an integral part of the digital world in sectors such as businesses, financial institutions, and government agencies. The deceitful transactions are growing by leaps and bounds at an alarming rate. As fraud occurrences evolve with new and innovative technologies, the fraud detection domain should also evolve with new inventions in technology. To accomplish the same, the key components of data science and AI, such as data preprocessing, data engineering, machine learning models, anomaly detection techniques, real-time monitoring systems, and automated response mechanisms, should be updated with newer versions and inventions. Thus, data science with a continuously evolving system of fraud detection should be a proactive strategy to detect, prevent, and deter fraudulent activities so that organisations and the business community can nurture trust, resilience, and sustainability.

References

1. https://www.v7labs.com/blog/data-preprocessing-guide
2. https://www.ijert.org/support-vector-machine-based-credit-card-fraud-detection
3. 10.22034/JAISIS.2022.377265.1057.
4. https://www.bankofbaroda.in/banking-mantra/digital/articles/common-internet-banking-frauds-and-prevention-tips
5. https://cxotoday.com/news-analysis/how-india-is-using-ai-ml-against-tax-evaders-and-frauds/
6. Alsadhan, N.. (2023). A Multi-Module Machine Learning Approach to Detect Tax Fraud. Computer Systems Science and Engineering. 46. 241-253. 10.32604/csse.2023.033375