A fraud detection system can be deployed in the cloud or on-premises, making it a cost-effective option. You don't have to worry about privacy and security regulations with cloud services. Data is encrypted using a hash table, and subscription plans charge based on how often you use them.
Fremont, CA: According to a report, bank card fraud losses will hit $165.1 billion in a decade, so banks, online stores, insurance companies, telcos, and government agencies should use fraud detection systems. Here are some common concerns customers have when choosing a fraud detection platform.
What type of fraud prevention system should you choose?
There are two kinds of fraud detection systems: transactional and sessional.
● Transactional systems assess payment risks and safeguard financial systems from fraud by checking parameters, allowing or blocking actions, and forwarding information to analysts for further assessment, ensuring secure user transactions.
● Sessional systems monitor user actions based on device parameters, detecting abnormal behavior like different form-filling methods or typing speed as potentially fraudulent. In contrast, standard behavior is observed for its standardity.
Integrating fraud detection capabilities from different vendors can be complex and time-consuming, so getting data exchanged effectively for optimal protection is vital.
Cloud deployment vs. on-premises
A fraud detection system can be deployed in the cloud or on-premises, making it a cost-effective option. You don't have to worry about privacy and security regulations with cloud services. Data is encrypted using a hash table, and subscription plans charge based on how often you use them. In-house systems require infrastructure, tool configuration, and support staff but offer control over customer information, keeping it within the company's walls.
Working with external data sources
The effectiveness of an anti-fraud system depends on communication with external data sources to get transactional data and cross-referencing it with blacklists, ensuring efficiency and compliance with the bank's requirements. Fraud detection efforts can be boosted with private threat intelligence feeds, providing comprehensive coverage.
How does machine learning help?
Machine learning models are used to create user profiles and predict typical behavior from statistical data in modern fraud detection solutions. Machine learning reduces false positives for rules by identifying commonplace actions—atypical actions that flag transactions as potentially risky. In cases where the rules don't apply, operations stop.
Creating detection rules
Customers should be able to configure fraud detection systems themselves, reducing the time between incidents and rule implementation, and the vendor should offer prompt support for resolving issues. A user-friendly platform with an intuitive rule builder is crucial when working with small teams since non-programmers can do it without writing code.
