Employee Fraud Detection

Fraud detection by employees

Anomalia- Employee Fraud Detection harnesses the true power of Artificial Intelligence by using proprietary algorithms and connectors that help in automating several steps such as pre- processing, cleansing and harmonizing transactional data that enables the identification of employee fraud situation in nimbler and more economical manner. This software app identifies patterns related to all transactions and relationships, employee behaviour, segmentation of employee into cohorts, and various internal and external documents including KYC documents and emails.

Use Cases

  • Monitoring customer accounts for activity, and skimming funds from inactive or almost inactive accounts

  • Skimming funds from unmonitored accounts, such as elderly customer accounts

  • Reversing non-sufficient funds (NSF) fees, & transferring the refunded charges to their personal accounts

  • Opening fraudulent accounts with stolen or fictitious information, collecting the employee sales rewards, and then closing these accounts

  • Manually modifying sales numbers to increase employee sales rewards

Anomalia Serves

Anomalia®

AI-based conflict of interest monitoring clears all Hurdles

Conflict of Interest Monitoring by Scry Analytics ingests data from different sources and detects fraud and is able to process tremendous amount of records of more than 4 million/hour.

More Detail

Anomalia has been built using

  • Bayesian networks

  • Reinforcement learning

  • Generic rule-based machine learning

  • Heuristic algorithms

  • Data mining

  • Big data integration

  • Big data analytics

  • Reverse engineering

Key Differentiators &
Business Benefits

  • User Interface and APIs

    Pre-built graphical user interface (GUI) & APIs for quick deployment & integration with clients’ existing workflow.

  • Domain Ontologies

    Pre-built financial ontologies & business rules that are dynamically updated to improve the product accuracy over time.

  • Knowledge Graphs

    30+ probabilistic spatial and temporal graph algorithms to determine links and connected entities.