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Connected citizens

Introduce security solutions separating fraudsters from legitimate citizens

Successful fraud prevention in e-government means immediate detection of fraudulent activities, based on proven prediction models and data mining methods that are needed to uncover new and emerging types of fraud.

Collecting large datasets of citizens’ non-personal behavioral patterns makes it possible to detect suspicious behavior in e-government digital channels before any real financial or reputational damage is inflicted.

Investigate and analyze fraud cases, and quickly identify events and incidents signaling fraud

While striving for more efficient, productive and effective citizen interactions, governments also open the door to fraudsters who can take advantage of vulnerabilities in e-government digital channels.

Fraud-related issues in e-government pose a particular threat, because citizens tend to view government bodies as a single entity and do not differentiate between online services and the state. Any reputational damage to an e-service will therefore harm the image of the government body itself.

Prevent cyber-fraud before it happens

Prioritize legitimate citizens and detect suspicious ones

with

Kaspersky Advanced Authentication

Prioritize legitimate users and detect suspicious ones
Risk-based authentication continuously monitors numerous unique parameters
Real-time analysis of biometric, behavioral and environmental data
Legitimate users proceed to their digital accounts without any unnecessary verification steps, reducing two-factor authentication costs

Identify new account fraud and account takeover incidents

with

Kaspersky Automated Fraud Analytics

Advanced machine learning ensures all data and activity are monitored throughout the whole session
Continuous detection and analysis of in-session events including bots, malware, remote administration tools, new unknown devices, web injects and more
Identification of new account fraud and account takeover incidents
Global mapping, link building and device identification