By Srijan Bijjam — Sep 29, 2023

🛍️ Stopping Retail Fraud With AI

AI helps detect and prevent fraud in retail and e-commerce by employing a multitude of algorithms.

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How AI is helping detect and prevent retail fraud

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AI has been playing a major role in detecting and preventing fraud in the retail industry. Fraud in the retail and e-commerce sector is a major problem that has an impact on customers and businesses. Credit card fraud, identity theft, fake refunds, and other dishonest practices are examples of fraudulent activity. Here are some ways in which AI is helping the retail and ecommerce industry:

Real-time Fraud Detection: Real-time analysis and response is one of the key benefits of employing AI in fraud detection. As transactions occur, machine learning models may examine the transaction data and flag any strange activity for more inquiry. This rapid response can aid in stopping fraud before it harms the client or the company. Neural Networks, which can analyze large volumes of transactional data to identify complex non-linear patterns, are used to prevent fraud. Paypal uses deep learning models such as Neural Networks in order to do this task by processing millions of transactions daily and the algorithms allow it to adapt to new patterns of fraudulent activities.
Predictive Analysis: By examining previous transaction data, AI systems could predict possible fraud. The risk of future fraud is reduced when companies use predictive analysis to find and fix systemic flaws. Companies use feature engineering, which is when relevant features are extracted from raw transactional data, to help their algorithms learn more efficiently and to be able to predict future fraud. Mastercard utilizes feature engineering among other algorithms to analyze transactions. They have a proprietary system called Decision Intelligence that uses the aforementioned predictive analysis features to assess and score every transaction for potential fraud risk, allowing for immediate action.

Enhanced Accuracy: AI improves the accuracy of fraud detection through machine learning and data analysis. Traditional approaches can produce false positives and negatives, which are frequently the consequence of rule-based systems. AI reduces these mistakes, resulting in more precise fraud detection (up to 96% accurate) and improved user experience. In addition to deep learning algorithms, supervised learning algorithms and unsupervised algorithms are employed to increase accuracy in preventing fraud. Supervised learning algorithms are taught to identify transactions as real or fraudulent based on historical data and demand labeled data. Unsupervised learning algorithms are capable of detecting abnormalities or strange patterns in transaction data that may indicate fraud and do not require labeled data.

Since worldwide e-commerce fraud losses, which reached $41 billion in 2022, are expected to rise to more than $48 billion in 2023, using AI in fraud detection has become essential. Particularly, North America accounts for almost 42% of e-commerce fraud and has the highest average fraudulent transaction value.

By improving fraud detection and prevention, AI is revolutionizing the retail and e-commerce industries. In order to guarantee the security and integrity of retail and e-commerce transactions, companies must invest in AI for fraud detection in order to protect their business and improve customer trust.

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