Risk Management Data Channel for Enterprise Risk Assessment
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Solution Overview
Problem
Conventional enterprise computing platforms are not well-suited to respond expeditiously to global or localized crises, requiring specialized developers to adapt applications, which increases response times to risk events.
Innovation Solution
A risk management engine that monitors enterprise data streams to detect and evaluate risk events, transforming data into actionable insights through a risk management data channel, enabling real-time risk assessment and response without requiring developer intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional enterprise applications are used, then developer specialization is required to adapt to risk events, but response time to risk events increases
Solution Approach 1:
The system enables self-service by automatically detecting risk events through monitored data streams and transforming enterprise data without requiring developer intervention. The risk management engine autonomously identifies risks, evaluates their impact, and presents findings through dashboards, allowing the enterprise system to serve itself in risk management rather than relying on specialized developers.
Solution Approach 2:
The system performs preliminary action by continuously monitoring enterprise data streams for risk indicators before actual risk events materialize. The risk management engine proactively identifies potential risks, evaluates their potential impact on enterprise functions, and prepares assessments in advance, enabling the enterprise to respond more quickly when risks materialize.
2Productivity
If traditional enterprise applications are used, then application flexibility is maintained, but expeditious response to global or localized crises is not achieved
Solution Approach 1:
The risk management engine serves as an intermediary between enterprise data streams and crisis response capabilities. It monitors data streams, detects risk events, transforms relevant enterprise data, and presents findings through dashboards, thereby bridging the gap between traditional applications and the need for expeditious crisis response without requiring fundamental changes to the underlying application architecture.
3Adaptability or versatility
If developer intervention is required to adapt applications, then application customization is achieved, but time to address risk events increases
Solution Approach 1:
The system enables self-service by automatically detecting risk events through monitored data streams and transforming enterprise data without requiring developer intervention. The risk management engine autonomously identifies risks, evaluates their impact, and presents findings through dashboards, allowing the enterprise system to serve itself in risk management rather than relying on specialized developers.
Data Source
AI summary
Various embodiments relate to data science and data analysis, computer software and systems, and computing architectures and data models configured to facilitate management of enterprise functions, and, more specifically, to an enterprise computing and data processing platform configured to activate risk management transformations of enterprise data in-situ, responsive to identifying a risk event, and further configured to implement a risk management data channel to facilitate analyses and responses associated with an enterprise computing device. In some examples, a method may include receiving a risk data signal, identifying a portion of the risk data signal, computing data representing a risk level, classifying data associated with a hierarchical business data object in accordance with a risk level, aggregating classified data with other data associated with other business data objects classified as a function of risk to form aggregated data, causing presentation of aggregated data as a function of risk.