Implementing AI and Machine Learning in Fraud Detection

Shopping in the malls was just a normal thing two years back. And now, people purchase everything online, be it food, furniture, clothes, or groceries. This resulted in the boom of the global e-commerce market and is estimated to reach $4.9 trillion by 2023. This indeed triggers the criminally minded people to find a path to the victim’s wallet and accounts through the web.

Government agencies and various private organizations reported approximately 3 million identity theft cases in 2021. Money was lost in about 28% of these fraud cases. According to a report IC3 (Internet Crime Complaint Center), financial frauds in 2019 were at their peak.

Fraud Detection with Artificial Intelligence and Machine Learning becomes possible only due to the ability of Machine Learning algorithms. These tools let the firms trace the history of any transaction, learn from their fraud patterns and recognize them in time to prevent future transactions. ML Algorithms appear to be more effective than humans when it comes to their speed of processing information.

If done accurately, Machine Learning and AI can clearly distinguish between legitimate and fraudulent behaviors while adapting new and previously undiscovered fraud tactics. This can indeed become quite complex as there is always a need to interpret patterns and data by applying Data Science to continually improving the ability to distinguish normal behavior from abnormal behavior. This requires millions of figures to be performed accurately in just milliseconds.

These methods can analyze these customer behaviors swiftly and more accurately than any human analysis report and as a result, it can very quickly identify the deviation, if any, from the normal behavior or normal track record. This allows the opportunities in real-time approval by the user, to stop any fraudulent activity before a transaction completes.

Machine learning also has benefited by increased accuracy by eliminating human error in analyzing or recording data from the equation. Moreover, better predictions can be achieved since machine learning models can process massive amounts of digital data in a significantly less amount of time. The more we supply data to a model, the more it learns from it and creates even better prediction analysis reports.

Benefits of Machine Learning Algorithm in Fraud Detection.

Machine learning for fraud detection in various domains and industries works by analyzing consumers’ current transaction patterns and financial methods. Finally, machine learning is a fairly cost-effective detection technique for companies. Data can be analyzed in milliseconds, and team members aren’t burdened with manual review and checks every time new data is acquired.

Use of Oodles in the fraud detection module. 

In today’s changing era, each of us is vulnerable to credit/debit card fraud. In a single day, worldwide, millions of transactions are being carried out, and it is possible that the person doesn’t carry out the transaction himself while it is done from his/her account.

AI-powered tools and technologies are becoming a panacea for detecting whether the transaction is a fraud or not.

At Oodles AI, we help organizations detect the transaction and invoice data based on the behavior of the customer payments. AI-powered predictive analysis helps us achieve this feat more conveniently and accurately. 

Join hands with our OCR services to implement these techniques in your organization.