No-code wizard-driven software applying AI to automate the fraud detection process
AI Fraud Detection or Anomaly detection is built using tools in the Azure cloud which capture suspicious events that differ significantly from most of the data. Detecting anomalies can help automate auditing transactions for fraud, support reclaims efforts, and enable preventive actions going forward. This technology is getting more and more popular as fraud remains a serious issue across industries and the traditional methods can not adapt to the changes over time. Product Features Wizard-driven, no-code solution: Mantis is a wizard-driven, no-code software solution that leverages state of art machine learning models using a simple, guided user interface developed using Micorosft Azure Cloud Technologies. This solution abstraction away from coding enables Citizen Data Scientists in organizations to automate audits with machine learning.
Actor & Actor Flagging: Fraud is usually committed by multiple actors and factors at the same time, and we are not only able to catch a single fraudulent actor, but also the collusion between actors, such as repair billing fraud, inventory return abuse, non-rendered services, duplicate charges, collusion of doctor-patient/doctor pharmacies, fake shops/vendors conspiracy.
Flag the Root Cause of Fraud: We flag the root cause of fraud in addition to identifying anomalous patterns for an actor or transaction level. Stop recurring fraudulent transactions at a batch level in the data flood. Prevent blind spots and guide users with interpretable, quantified results to further investigation.
Human in The Loop: Employees can easily change the underlying assumption based on different business scenarios and needs after building the model. The interpretable real-time reports with visualization generated by AI will guide users through the investigation process, empowering employees with unrevealed insights and taking further actions. Leverage AI to enhance the quality of process and decision making and stop revenue leakages.