Online Commerce is a boom, and it revolutionizes Online Transaction. Online Commerce provides convenience, but it also opens the gate for fraudsters in the electronic commerce space. Due to sharp increase in the fraudulent activities, it has now become apparent for Online vendors to have an advanced fraud detecting system into their businesses. Traditional Fraud Detection systems are rule based models and they tend to take decisions based on the pre-programmed rules. Every new threat is programmed as a rule and feed into the system. A Human Analyst analyzes the threat pattern and activates the rules accordingly. The Traditional Fraud Detection systems are effective to a certain extent based on the predefined fraud rules , but they lack instant adaptability towards evolving threats requiring a human intervention to keep the Fraud rules updated or to act when there is a conflict in the fraud detection to identify the false positives in fraud. As fraudsters try to use sophisticated rules and patterns to perform fraudulent activities, the rule-based fraud detection systems struggles to keep pace with the growth in fraud techniques.
Moreover, the traditional fraud detecting systems generate a high number of false alarms potentially blocking valid transactions. This severely impacts loss of revenue and genuine customers to the business.
Artificial Intelligence(AI) algorithms offers a more brilliant and systematic approach towards fraud detection. Leveraging machine learning techniques, AI models can analyze vast amount of data and identify patterns that are not apparent to rule-based systems. This enables AI-powered fraud detection systems to continuously adapt and improve by staying one step ahead of fraud techniques . AI-powered fraud detecting systems can self-learn by processing enormous amount of data and accustom themselves towards evolving fraud patterns. AI algorithms can detect anomalies and unusual patterns in transaction data, flagging potentially fraudulent activities instantly. By analyzing historical data, AI models can learn to differentiate between actual and fraudulent transactions with greater accuracy, greater speed, significantly reducing the number of false positives and improving business revenues.
Pros of AI-Powered Fraud Detection Systems
Precise Fraud Pattern Check: AI algorithms can identify fraud patterns and their relationships by analyzing huge amount of historical data and improving detection accuracy.
Instant Detection: AI Models can monitor transactions instantly and blocks suspicious activities immediately, preventing fraudulent transactions from occurring. This speed is necessary in blocking fraudulent transactions from going through.
Faster Transactions: AI Models can significantly speed-up online transactions by enabling instant fraud detection and approval, thus reducing the need for human analyst involvement, manual review, and approval process.
Scalable: AI Models handles enormous amount data, and they are developed to be scalable. The AI Powered Fraud Detection systems can be scaled without compromising the speed and accuracy of the existing Online Transactions.
Return on Investment (ROI): Implementing AI Fraud detection model is a huge initial investment, but they slowly tend to show profits by lowering the operational costs with automated fraud checks and reducing manual efforts.
Lessen False-Positives: AI Models self-learn from past data and customer patterns, and they accurately differentiate between actual and fraudulent transactions and thus leading to fewer false alarms.
Self-Learning: AI Models continue to self-learn from new data patterns, newer anomalies, adapting to evolving fraud tactics and accurately use them to stay ahead of the fraudsters.
Challenges of AI-Powered Fraud Detection Systems
Cost and Complexity: The cost and complexity involved in setting up an AI Fraud Detection Model is huge especially for smaller business. The initial investment and complexity may deter certain businesses from adopting them.
Training and Self-Learning: AI Models require continuous learning to keep them efficient, adaptive, and productive. Skilled Data Engineers are needed to manage these complex systems and keep them trained.
Data Quality: AI Models need quality data to self-learn, self-train and to make accurate decisions. If data is flawed, it will impact its decisions and lose credibility of its accuracy.
Common Threats Tackled by Fraud Detection Systems
- Identity Threats or Credential Stealing
- Phishing Attacks
- Credit Card Theft
- Document Forgery
- Wire Fraud
- Money Laundering
- Account Takeover
- Accounting Fraud
Examples of Fraud Detection Systems using AI Models
There are many Fraud Detection Systems available in the market. Listing down some well-known fraud detection systems using AI and Machine Learning Algos for Fraud Analysis:
- Forter (https://www.forter.com/)
- Kount (https://kount.com/)
- IBM Trusteer (https://www.ibm.com/trusteer)
- Signifyd (https://www.signifyd.com/)
- Seon (https://seon.io/)
- ClearSale (https://www.clear.sale/)
- Sift (https://sift.com/)
Conclusion
Artificial Intelligence (AI) is emerging, and it is transforming online transactions. AI renovates Fraud detection by introducing superfast, efficient, and accurate way of detecting threats. With Precise Fraud Pattern Check, Instant Detection, Fast Transaction, Self-Learning technique, reduced false positives, and scalability, the AI powered fraud detecting systems offers an efficient solution to combat fraudulent activities. AI is advancing, the advanced AI models can analyze not only financial data, but it can analyze customer patterns, customer activities leading to even more accurate decisions.
Note: While analyzing different Fraud detection systems for our clients, the availability of AI Technology and how it streamlines the fraud processing should be considered before finalizing the Fraud detection system.
