The unhindered growth of e-commerce and its impact has seemingly increased the risks of online fraud. Moreover, the attacks have amplified in terms of magnitude as the technologies adopted by the hackers are getting persuasive and more powerful, with each passing day. The growing nature of anomalies associated with online payment fraud is hugely catastrophic and requires excellent strategies to be implemented by the companies, banking organizations and even individuals. It is here that technology comes to our rescue in the form of Machine Learning.
Analyzing the Threats
Threats like account takeovers, data breaches and identity thefts are turning into digital commonplaces. In addition to the financial repercussions, individuals also need to deal with issues like legal matters, higher tangential costs and a host of other troubles. Online payment frauds are quite taxing on the users and thus a few recent surveys showed a drop in the households making online purchases due to payment frauds.
Why Manual Reviews are bound to fail?
Phishing emails and malware can easily break into the confidential personal and security details of the customers. Once the details are anyhow procured, every login is considered legitimate by the manual systems. Human supervision, therefore, isn’t powerful enough to review online transactions. 26 percent of the e-commerce transactions are currently reviewed manually which validates the fact that fraudsters are still being able to sabotage the online transactions. However, with the adoption of Machine Learning in the online payment space, it won’t be long when we would be able to successfully defeat the payment fraudsters and make online frauds a thing of the past.
Implementing Identity Tracking Techniques
Credit and debit card information sets are elusive and highly confidential in nature. Majority of financial organizations prefer verifying the shoppers’ identities for validating authenticity of the transactions. With the use of Machine Learning and advanced predictive analytics, a customer’s behavior is tracked in detail. Digital identity tracking is an online prerequisite that allows companies to track the payments; thereby minimizing the risks of online frauds. Any kind of suspicious online activity can be instantly identified and flagged. Most identity tracking systems have the capabilities of processing massive data sets; thereby improving and refining the existing information for unmatched authenticity.
Deploying Advanced Analytics
It is a proven fact that retailers spend a sizeable 7 percent of their revenue on identifying and combating online frauds. With Machine Learning being an integral part of the protective framework, it finally comes down to three analytical techniques for fighting the online payment frauds.
1. Descriptive Analytics
This includes the aspects of unsupervised learning where descriptive analytics plays an important role for detecting transactional practices outside normal. This approach towards Machine Learning looks into the average customer behavior and combines the principles of association rules, clustering and peer group analysis.
2. Predictive Analytics
Machine Learning principles when combined with supervised learning are the pillars of predictive analytics. This approach assists in real-time threat detection but cannot identify the concluded fraudulent activities. However, predictive analytics is a great way to preempt the dangers of online frauds by putting linear regression and logistic insights to use. Predictive analytics also leverages larger data sets, neural networks and sophisticated threat detection models.
3. Social Analytics
One of the best fraud detection tools has to be the Social Network Analytics (SNA) that makes use of efficient analytical tools and community detection techniques. Social analytics also helps individuals gain specific insights regarding the financial actions and the existing connections between the concerned instances.
Machine Learning technologies extract relevant information from the datasets, help build specific models and provide training sets for predicting and preventing online payment frauds. At present, we, as businesses are quite close to deploying the basics of Machine Learning for staying ahead of the payment frauds. However, we still need to amplify the speed and accuracy of our decisions.
Evolution of Smarter Payment Solutions
Machine Learning strategies are best depicted by the innovative digital payment solutions. 73% of the financial trades are already making use of Artificial Intelligence algorithms for strengthening their perimeters. Additionally, IoT has also evolved as a path-breaking technology, allowing consumers to initiate transactions via secured digital assistants. Page Survey reveals that 24 percent of respondents believe that in the next few years, customers will start making majority of their online transactions via smart appliances. Thus, we can positively say that Machine Learning is the future of online payments. It has the power to defeat online payment frauds and turn the entire online payment ecosystem into a much secure and smart space.
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