Building a Bank Oversight System to Detect Risks

The Moviri Analytics team tests, tunes, and adapts models to reality, making network science more attractive for business applications at unprecedented speed and scale.

How network science modeling and graph analysis improve business performance.

We are living in the era of data. Yet, producing and storing massive amounts of data yields no competitive advantage without the means to understand it.

Using graph-based network maps, organizations can shed new light on their business. By identifying relationships among agents, you can easily uncover, and quantify, the interplay of those network connections, a nearly impossible exploration using traditional approaches. 

Download this case study to discover how the Moviri Analytics team built a network analytics engine capable of monitoring the Customer’s global IT system across its complex networks of applications, users, physical network, permissions, and accesses, detecting potential operational risks, anomalies, and points of failure in near real-time.



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Why does network science matter?

The most promising approach to leveraging graphs in managing business processes.

Graph analytics case study

Predict impact across the global technology system of one of Europe’s major banks.

Impact and benefits

System-wide visibility across enterprise layers, from physical IT systems to applications and organizational structures.

Moviri AI

The Moviri strategy and approach

The competencies that companies need to leverage network science modeling and graph analysis.



Automatic permission modifications

Proactive dynamic update of 50% of the permissions within the privileged access map in case of major changes.

Top 10

Incident risk prediction

Prioritization of incidents according to their relevance within the IT system and the whole IT stack.



Reduction in incidents

Prevented 30% of incidents thanks to proactive interventions on insights drawn from the root-cause analysis.

Our approach
Moviri Analytics

Graph technology for governance, compliance and risk.

We use graph technology to express the relationships between resources and digital data flows taking place within a business process, in real-time. We mapped the ontology in the graph model and developed ​​special investigations and Graph Data Science algorithms to, for example, detect failures, spot wrong system configurations, prevent data leakage, assess risk or detect fraud, and take automatic actions based on the data.


  • Real-time, dynamic monitoring and control of a complex system.
  • Incident impact prediction and reduction.
  • Automatic permission modifications.