By Jinendra Khobare, Solution Architect, Sensfrx, Secure Layer7
Online fraud is a significant issue in India, with various scams such as phishing attacks, identity theft, and counterfeit e-commerce sites. Cybercrime in India has been on the rise, with the country recording over five thousand cases of online identity theft in 2022. Phishing attacks have also seen a surge, with around 83% of IT teams in Indian organisations reporting an increase in phishing emails targeting their employees in 2020. Furthermore, about 38% of consumers have received a counterfeit product from an e-commerce site in the past year.
According to a report, a significant portion of fraudulent transactions occur between 10 PM to 4 AM, with credit card holders over 60 years being the primary victims. From January 2020 to June 2023, 77.4% of cybercrimes were reported in India. The number of cybercrime cases in a city in India rose from 2,888 in 2020 to over 6,000 in 2023.
Machine learning is instrumental in fraud prevention, enabling organisations to detect and prevent suspicious activities in real-time. Traditional fraud prevention methods often struggle to keep up with the evolving tactics of scammers. Machine learning algorithms can quickly analyse vast amounts of data, helping organisations identify patterns and anomalies that may indicate suspicious behavior. These algorithms learn from past fraud cases, continually enhancing their ability to detect suspicious activities. By integrating machine learning into their fraud prevention strategies, organisations can stay ahead of scams and safeguard their assets effectively.
A key advantage of machine learning in fraud prevention is its ability to detect suspicious activities at an early stage. By analysing historical data and identifying patterns of dubious behavior, machine learning algorithms can spot suspicious transactions in real-time, enabling organisations to act swiftly and prevent financial losses.
Graph databases, alongside machine learning, have come out as a strong tool in fraud detection. Graph databases record and analyse network interactions at high rates, making them useful for a variety of applications, including fraud detection. They can identify patterns and relationships in big data, reducing the level of complexity so that detection algorithms can effectively discover fraud attempts within a network.
In conclusion, as scammers evolve their tactics, organisations must adapt their fraud prevention strategies to counter these threats effectively. Machine learning and graph databases are powerful weapons in this ongoing battle. With their ability to analyse countless data points rapidly, these technologies can detect suspicious activities accurately, surpassing human capabilities. It’s akin to having a team of superhuman fraud detectives working tirelessly around the clock. As quickly as organisations detect and prevent suspicious activities, scammers are equally fast at devising new deception methods.