Cybersecurity

Anomaly Detection

Online Fraud Detection

Problem

  • The use of online payment mode such as online banking, debit card, credit card etc. has become hugely popular and important in day to day shopping and e-commerce activities. On the other hand, online fraud has become a serious issue in financial crime management for all online businesses.
  • The companies and financial institutions are losing huge amounts due to fraud while fraudsters continuously try to find new rules and tactics to commit illegal actions.
  • According to different studies, annual fraud costs approximately for U.S. retailers reached $32 billion in 2014. Retailers lost an estimated 1.3% of revenue in 2015, more than double the rate of 2014.
  • It also proved that up to 25% of declined sales transactions for e-commerce merchants were actually good sales to start, indicating that the most of current fraud detection systems have huge rates of false positives

Solution

  • The important fact is that the fraud is an adaptive crime, so it needs special methods of adaptive intelligent data analysis to detect and prevent it continually.
  • At Eris Innovation we have considered several supervised and unsupervised approaches toward implementation of novel AI based anomaly detection and pattern recognition methodologies for developing cutting-edge online fraud detection systems.
  • Thanks to the hybrid structure of these solutions which gets benefit from the learning capabilities of Artificial Neural Networks (ANNs) and reasoning abilities of Fuzzy Logic based models (FL), these systems are able to learn from cybersecurity experts and fraud attacks in the past and then making an updated bank of recognized fraud patterns as well as being able to constantly monitor and notify the suspicious online activities.