MindBridge’s Risk Segmentation dashboard combines 2 powerful capabilities to allow financial professionals to create custom risk assessments
Unbiased AI is a goal we all want to achieve, but it’s continuously difficult when dealing with human-centric technology.
This guide covers how the international standards addressing technology ethics and bias in auditing affect organizational tech use.
Read to discover a quick and efficient approach to identifying embedded leases using MindBridge risk analytics
Explore how the MindBridge API can be implemented, and sample business use cases
Crossover to clarity | Mindbridge’s new release delivers API support, expanded ratio capabilities, and platform enhancements
As the complexity of data analysis increases, preemptive vs. reactionary capabilities become paramount. Data anomaly detection can help.
Anomaly detection is critical for making the most of audits. Here’s how to use anomaly detection to identify risks and get the best outcomes.
Human-centric AI for anomaly detection is designed to use human input to help support and scale financial risk discovery objectives.
Anomaly detection is a powerful technique for detecting fraudulent transactions and behaviors, thanks to financial institutions’ ever-increasing amounts of data.
MindBridge’s Chief Technology Officer, Robin Grosset, and VP of Analytics and Data Science, Rachel Kirkham, hosted a virtual webinar on “Explainable AI, transparency, and trust.” The main focus of this webinar was to explore how business and audit professionals can rely on AI-produced data analytics results to make better decisions.
Leading next-generation data anomaly and risk detection for financial data. Read more in the Press Release published here. While it’s true that there’s nothing more constant than change, in today’s financial markets, this piece of wisdom could not be more accurate. With the growing volumes of data, increasing complexities of multinationals, and the ongoing march
