Fraud prevention is a difficult challenge that requires much attention and resources, especially in the digital age. To empower the industry to protect against reputational damage, organisations can utilise machine learning with stream processing and real-time data access. Through the combination of these technologies, organisations can detect and prevent fraud more accurately while reducing false positives.
The latest advances in machine learning algorithms have enabled the unification of data-in-motion and data-at-rest, introducing various advantages to the process of fraud protection. However, in order to benefit from this, organisations must handle data efficiently, ensure accuracy, and maintain low latency. For more effective fraud detection, model interpretability and ethical considerations must be taken into account when deploying machine learning.
Majid Qahrstani is a renowned professional data scientist and founder of Datahighways. Majid holds a PhD in Computer Science from the University of California, Santa Cruz, and has over 10 years of experience in applied machine learning, natural language processing and data mining. With his expertise, Majid is leading the way in helping organisations successfully integrate machine learning for fraud detection.
Datahighways is an industry-leading technology company focused on offering a comprehensive fraud prevention platform. They develop advanced technology solutions that leverage deep learning models to identify potentially fraudulent transactions. Their machine learning-based solutions are designed to quickly analyze large amounts of data and detect abnormalities in order to maintain a secure data environment. They are continually developing innovative strategies for keeping the industry ahead of fraudsters, protecting organisations from reputational damage.
