Enterprise Financial Modeling With Dynamic Compute Allocation
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Solution Overview
Problem
Conventional systems are unable to efficiently model the effect of investments on the financial status of organizations due to variability in operations and financial goals, leading to inefficiencies in simulating potential outcomes.
Innovation Solution
A simulation system utilizing simulation compute devices with diverse compute resources, an orchestrator device for task assignment, and a model library server to dynamically allocate resources based on demand and suitability, simulating investment effects on financial health using rules-based and machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional systems are used to model investment effects, then the system structure is simple, but the ability to efficiently model variability in operations and financial goals is poor
Solution Approach 1:
The system is divided into separate functional modules: simulation compute devices for executing models, an orchestrator device for task management, and a model library server for storing models. This segmentation allows each component to be optimized independently while working together to handle complex financial modeling scenarios with varying organizational goals
Solution Approach 2:
The simulation compute devices are designed to execute multiple types of financial models (rules-based and machine learning) for different organizational types and financial goals. The system can adapt to model different scenarios including cash flow funding, asset-to-liability ratios, and other financial objectives through a universal platform
2Productivity
If more compute resources are allocated to simulate investment outcomes, then the simulation accuracy and speed improve, but the resource consumption increases
Solution Approach 1:
The system dynamically allocates compute resources based on the complexity of the simulation and the number of scenarios to be modeled. The orchestrator device assigns tasks to available compute devices, adjusting resource allocation in real-time to match computational demands while optimizing energy efficiency
Solution Approach 2:
The system uses multiple simulation compute devices to create parallel copies of the same simulation process, each handling different scenarios or time periods simultaneously. This parallel processing approach maintains high simulation accuracy and speed while distributing the computational load across multiple devices
3Adaptability or versatility
If the system models multiple organizations with different financial goals simultaneously, then the comprehensiveness of analysis improves, but the system complexity and resource management difficulty increase
Solution Approach 1:
The system segments the analysis work by organizing simulations according to organizational types and financial goals. The model library server stores models categorized by organization type, and the orchestrator device assigns specific organizational simulations to appropriate compute devices, managing complexity through structured organization
Solution Approach 2:
The system handles different organizational goals by changing simulation parameters and model selections rather than creating entirely separate systems. The same computational infrastructure supports diverse financial modeling scenarios by adjusting input parameters, model algorithms, and evaluation metrics according to the specific organizational objectives
Data Source
AI summary
Technologies for enterprise financial modeling include a system with circuitry configured obtain parameter data indicative of a financial status of an organization. The circuitry may be further configured to select, as a function of the parameter data and from a set of models for tracking key financial metrics of different types of organizations, a model. Additionally, the circuitry may be configured to simulate, using the selected model and by dynamically allocating compute resources across multiple compute devices as a function of a simulation compute load, an effect of a predicted performance of a set of investments on the financial status of the organization. Other embodiments are also described and claimed.