Most enterprises are undertaking a digital transformation initiative. Data and analytics modernization is an integral part of this journey. On-premise legacy systems like Hadoop clusters and data warehouses limit innovation and growth due to their old architectures. New cloud-based platforms are becoming an inevitable consideration for many such enterprises. However, they are seeking to reduce the risk and complexity of manual migration from their conventional ETL tools and data lakes to a modern, future state.

Insider threat is one of the biggest cybersecurity risks to banks today. Not taking appropriate measures to control insider threats, poses as much of a risk as an external factor such as hacking, especially in a highly regulated industry like banking. StreamAnalytix helped this large bank to identify and prevent insider information security threats through use of predictive analytics and machine learning, to automatically and effectively detect previously unknown threat scenarios and patterns, and raise appropriate alerts and actions to prevent predicted breaches.

Insider threats are one of the most significant cybersecurity risks to banks today. These threats are becoming more frequent, more difficult to detect, and more complicated to prevent. PwC's 2018 Global Economic Crime and Fraud Survey reveals that people inside the organization commit 52% of all frauds. Information security breaches originating within a bank can include employees mishandling user credentials and account data, lack of system controls, responding to phishing emails, or regulatory violations. Ignoring any internal security breach poses as much risk as an external threat such as hacking, especially in a highly regulated industry like banking.

Organizations are collecting massive amounts of data from disparate sources. However, they continuously face the challenge of identifying patterns, detecting anomalies, and projecting future trends based on large data sets. Machine learning for anomaly detection provides a promising alternative for the detection and classification of anomalies. Find out how you can implement machine learning to increase speed and effectiveness in identifying and reporting anomalies.