Tag: AI agents

Fraud & Security Intelligence | Predictions
Jason DiNovi 0
Agentic AI is the fix for communication breakdowns in health care payment integrity

One of the lesser talked-about issues large organizations face is the siloes that their various business units operate in. It’s a peculiar situation born from the specificity of tasks and objectives assigned to hyper-specialized teams within an enterprise. This separation of tasks inevitably leads to critical dependencies between teams. However,

Advanced Analytics | Analytics | Artificial Intelligence | Data Management
Daniel Moïse 0
La IA puede tomar decisiones rápidas, pero ¿se puede confiar en ella?

Cada día, la IA toma decisiones que determinan vidas, industrias y el futuro. ¿Pero podemos confiar en esas decisiones? Las organizaciones están haciendo grandes inversiones en IA, y eso está cambiando la forma en la que se toman las decisiones. Pero sin resultados claros y un valor demostrado, invertir en

Analytics
Peter Williams 0
Why agentic AI Is the future of banking

The banking industry is at a crossroads. With rising customer expectations, intense competition, ever-evolving regulations and mounting cost pressures, financial institutions must transform – or risk falling behind. Enter agentic AI, an advancement in artificial intelligence that’s poised to redefine how banks operate, serve, and grow. What is agentic AI?

Advanced Analytics | Artificial Intelligence | Innovation
Albert Qian 0
What’s in a decision?: Four components needed to operationalize your analytics

Every organization collects data, but collecting it isn’t enough. For companies that want to make personalized offers, detect fraud and optimize supply chains, decision-making is the ultimate measure of analytics success. But despite massive investments in data infrastructure and AI, many companies still struggle to bridge the gap between insight and action. That’s

Artificial Intelligence | Fraud & Security Intelligence | Machine Learning
Josh Beck 0
Threat modeling for agentic systems

As agentic AI systems evolve through protocols like MCP and A2A, traditional security practices must be adapted to address new risks such as goal misalignment and tool instruction abuse. This article explores practical threat modeling strategies, including goal alignment cascades and distinguishing between parameter-only vs. instruction-enabled tool calls.

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