Fraudsters are mushrooming in the market and they are making hard for the organizations to safeguard their data, so the companies should start adopting new techniques for fraud detection.
FREMONT, CA: The number of risks and frauds are increasing day by day, and also disruptive changes are happening everywhere in the industry. The fraudsters are getting more sophisticated with new tactics, so the companies should start changing their approach to fraud detection.
1. Make new accuracy and efficiency to fraud detection with artificial intelligence
Machine learning can be used to improve process efficiency as with machine learning systems can automatically do the following things:
• Make and update rules for detection and alert handling: Nowadays, with the help of machine learning, masses of data can be examined, which helps in establishing standards and keep them current. It is effortless for a machine to detect a typical branch of seven or eight nodes that points to a fraud-rich area in the data, but on the other hand, it is a difficult job for humans.
• Select the most accurate detection models: There are several detection models in the market, but it is necessary to choose the right one to streamline the process. For example, newer techniques like gradient boosting and support vector machines are used to improve proven methods such as neural networks. Some other interfaces can intelligently launch 10,000 iterations of strategies so that the process is much more automated with less human intervention.
• Automate processes for investigation: These technologies will automate the investigation process. Most of the time, invigilators spend their time collecting data about a subject, but machine learning can help to search and retrieve data automatically, run database queries and collect information from third-party data providers without human intervention.
2. Streamline investigations with intelligent case management
AI will reduce the costs of running a fraud and financial crimes front line, as it focuses on automating manual processes. Investigators can spend their valuable time on some other tasks by adopting machine for these tasks. An analytics-driven alert and case management solution can automatically do several jobs like:
• It can find priority cases; recommend steps for investigation, and also speed-up the straightforward facts.
• It can find and pull data for a case from internal databases or third-party data providers.
• Present data in easy-to-understand visualizations, appropriate for the type of activity under review.
• Enable automated prioritization of client contact strategies.
• It can prepare SARs for electronic filing if applicable.