How AI helps detect and prevent fraudulent activities on ecommerce platforms

Combining advanced AI algorithms, identity verification, continuous monitoring, and behavioural analysis in today’s time enhances the overall security posture of ecommerce platforms and contributes to providing a safer online environment.

How AI helps detect and prevent fraudulent activities on ecommerce platforms

Friday January 26, 2024,

4 min Read

The Indian ecommerce sector, fueled by digitisation, a younger demographic, and increased disposable income, is experiencing remarkable growth that surpasses expectations. According to Redseer, the Indian ecommerce industry is expected to grow at a Compound Annual Growth Rate (CAGR) of 27% to reach $163 billion by 2026, which is almost 3X the growth of the overall retail market.


The dominance of digital-first brands is reshaping the landscape, leading to a notable shift of transactions to online platforms. While this competitive environment offers substantial growth prospects, the key challenge lies in sustaining trust and credibility. The prevalence of fraudulent activities, with fake product reviews and account takeovers posing significant threats, calls for addressing these challenges head-on by leveraging advanced data analytics, artificial intelligence, and machine learning algorithms.


As per a report by Statistica, more than 50% of ecommerce merchants worldwide indicated experiencing more account takeover attacks and promotion abuse. In the face of these challenges, the role of AI-powered identity verification solutions becomes pivotal in preventing and mitigating fraudulent activities in the ecommerce landscape.

User authentication against various parameters

Fake accounts and fabricated user profiles pose a significant threat to online platforms, from social media networks to ecommerce websites. Unauthorised individuals gaining illegal access to a user's account by stealing or obtaining their personal information (usernames, passwords, and other authentication details) can cause consequences for users and ecommerce platforms, including financial loss, identity theft, and reputational damage.


AI-powered due diligence solutions help identify users and ensure that only genuine users pass the verification process and post reviews. By cross-validating user-provided information with government and proprietary databases in real time, identity verification solutions help flag potential discrepancies that might pose future challenges.


Multi-factor biometric authentication methods, such as face match, voice recognition, and liveness detection, can verify the user's identity instantaneously and make it harder to create fake accounts or impersonate identities. This prevents the creation of duplicate accounts and restricts users from manipulating reviews by creating fake positive reviews to boost product ratings or negative reviews to harm a competitor's reputation.

Continuous monitoring and risk assessment

Sellers are another big stakeholder in the ecommerce platforms. Thus, the advanced due diligence framework extends beyond customer verification and needs to be extended to these third parties as well. However, due diligence while onboarding a new seller is not enough, it is advisable to run continuous monitoring post onboarding as well to proactively identify and mitigate risks.


As for the customers, ecommerce businesses can conduct ongoing risk assessments by analysing various parameters related to user behaviour, identity verification, and account history. In case of any suspicious activity or risk triggers, additional security measures can be taken by implementing a robust authentication framework to prevent potential account takeovers.

fraud prevention
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Contextual and historical data analysis

Nowadays, AI algorithms are leveraged to analyse the context of reviews, and sentiment analysis, identify patterns or inconsistencies, and determine what type of products are being reviewed at what frequency and pattern. Notably, businesses strategically leverage Natural Language Processing (NLP) algorithms to dissect fake reviews and identify anomalies in language, sentence structures, and sentiment analysis. This serves the purpose of dismissing attempts to manipulate brand reputation through fake reviews. 


By delving into historical data, the AI system continuously refines its capabilities over time, detecting discrepancies more effectively and empowering businesses to maintain the integrity of their reputation in the digital landscape.

Anomaly detection for fraud prevention

With AI-powered solutions, ecommerce businesses can ensure anomaly detection and identify deviations from normal user behaviour or transaction patterns. Unusual activities associated with large-scale purchases, random and repetitive changing shipping addresses, or other patterns, can be possible red flags that require prompt attention when detected by AI. The more data exposed to an AI system, the more adept the output will be as the algorithms evolve continuously. AI analysing diverse sets of data enables collective intelligence that boosts the effectiveness of fraud detection mechanisms.


Beyond just addressing account takeovers and fraudulent product reviews, background verification solutions assume a crucial role in bolstering ecommerce platforms against a spectrum of frauds. These include third-party frauds, concealed criminal and court records, fake/forged documents, etc., which can pose significant hurdles in the compliance journey.


Combining advanced AI algorithms, identity verification, continuous monitoring, and behavioural analysis in today’s time enhances the overall security posture of ecommerce platforms and contributes to providing a safer online environment for users, vendors, and partners coexisting within the same ecosystem.

(Ajay Trehan is Founder and CEO of AuthBridge, which delivers business impact risk mitigation solutions.)


Edited by Kanishk Singh

(Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the views of YourStory.)

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