Banking and healthcare fraud account for tens of billions of dollars in losses annually, which results in compromised financial institutions, personal impact for bank clients, and higher premiums for patients. Fraud detection and prevention refers to the strategies undertaken to detect and prevent attempts to obtain money or property through deception.
What is Fraud Detection and Prevention?
Fraudulent activities can encompass a wide range of cases, including money laundering, cybersecurity threats, tax evasion, fraudulent insurance claims, forged bank checks, identity theft, and terrorist financing, and is prevalent throughout the financial institutions, government, healthcare, public sector, and insurance sectors.
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To combat this growing list of opportunities for fraudulent transactions, organizations are implementing modern fraud detection and prevention technologies and risk management strategies, which combine big data sources with real-time monitoring, and apply adaptive and predictive analytics techniques, such as Machine Learning, to create a risk of fraud score.
Detecting fraud with data analytics, fraud detection software and tools, and a fraud detection and prevention program enables organizations to predict conventional fraud tactics, cross-reference data through automation, manually and continually monitor transactions and crimes in real time, and decipher new and sophisticated schemes.
Fraud detection and prevention software is available in both proprietary and open source versions. Common features in fraud analytics software include: a dashboard, data import and export, data visualization, customer relationship management integration, calendar management, budgeting, scheduling, multi-user capabilities, password and access management, Application Programming Interfaces (API), two-factor authentication, billing, and customer database management.
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