Thursday Bytes | Detect E-Commerce Frauds Using Artificial Intelligence and Machine Learning
https://www.gspann.com/
Detect E-Commerce Fraud Using Artificial Intelligence and Machine Learning
Juniper Research predicts online retailers will lose $50.5B to fraud in 2024.
E-commerce businesses are responsible for protecting their customers from fraudulent activity, like identity theft, credit card fraud, phishing, etc., by securing their platforms. Any of the above frauds on an e-commerce website almost always ensures that the customer will never come back.
Businesses have been using methods like multi-factor authentication (MFA) or two-step verification to protect themselves and their customers from e-commerce threats.
With the advent of AI, we now have powerful tools at our disposal that can help detect potential fraud. AI can analyze data, identify patterns, and look at anomalies to predict and zero-in on possible frauds.
Here are some ways that ML and AI can be applied to e-commerce fraud detection and prevention:
1. Anomaly Detection
ML algorithms detect anomalies in transactional data and flag anomalies for investigation.
2. Scoring Risks
AI systems assign risk scores to transactions based on transaction history, user behavior, geolocation, and device information. High-risk transactions may be subject to a manual review or additional authentication.
3. Predictive Analytics
Predictive analytics uses historical data to predict fraud and help businesses reduce risks.
4. Behavior Analysis
AI systems can analyze user behavior to identify fraudulent activity or attempts to take over an existing account.
5. Real-time Monitoring
AI and ML make real-time monitoring possible, allowing immediate threat detection and response.
6. Adaptive Learning
AI can adapt to new trends and strategies used by fraudsters. The effectiveness of fraud detection systems can be maintained through continuous learning.
7. Reducing False Positives
Traditional fraud detection methods produce many false positives, which leads to dissatisfied customers and missed sales opportunities. ML and AI improve the accuracy of fraud detection because they take into account more factors and can adjust to new information.
🔗 Read more on our business and technology solutions here: https://lnkd.in/dykpa5NP
#FraudDetection #EcommerceFraud #OnlineFraud #CyberAttack #CyberSecurity #Security #ArtificialIntelligence #MachineLearning #AI #ML #IdentityTheft #CrediCardFraud #Phishing #FraudInvestigation #FraudulentActivity #AnomalyDetection #PredictiveAnalysis #GSPANN #ThursdayBytes
https://www.gspann.com/
Detect E-Commerce Fraud Using Artificial Intelligence and Machine Learning
Juniper Research predicts online retailers will lose $50.5B to fraud in 2024.
E-commerce businesses are responsible for protecting their customers from fraudulent activity, like identity theft, credit card fraud, phishing, etc., by securing their platforms. Any of the above frauds on an e-commerce website almost always ensures that the customer will never come back.
Businesses have been using methods like multi-factor authentication (MFA) or two-step verification to protect themselves and their customers from e-commerce threats.
With the advent of AI, we now have powerful tools at our disposal that can help detect potential fraud. AI can analyze data, identify patterns, and look at anomalies to predict and zero-in on possible frauds.
Here are some ways that ML and AI can be applied to e-commerce fraud detection and prevention:
1. Anomaly Detection
ML algorithms detect anomalies in transactional data and flag anomalies for investigation.
2. Scoring Risks
AI systems assign risk scores to transactions based on transaction history, user behavior, geolocation, and device information. High-risk transactions may be subject to a manual review or additional authentication.
3. Predictive Analytics
Predictive analytics uses historical data to predict fraud and help businesses reduce risks.
4. Behavior Analysis
AI systems can analyze user behavior to identify fraudulent activity or attempts to take over an existing account.
5. Real-time Monitoring
AI and ML make real-time monitoring possible, allowing immediate threat detection and response.
6. Adaptive Learning
AI can adapt to new trends and strategies used by fraudsters. The effectiveness of fraud detection systems can be maintained through continuous learning.
7. Reducing False Positives
Traditional fraud detection methods produce many false positives, which leads to dissatisfied customers and missed sales opportunities. ML and AI improve the accuracy of fraud detection because they take into account more factors and can adjust to new information.
🔗 Read more on our business and technology solutions here: https://lnkd.in/dykpa5NP
#FraudDetection #EcommerceFraud #OnlineFraud #CyberAttack #CyberSecurity #Security #ArtificialIntelligence #MachineLearning #AI #ML #IdentityTheft #CrediCardFraud #Phishing #FraudInvestigation #FraudulentActivity #AnomalyDetection #PredictiveAnalysis #GSPANN #ThursdayBytes
