1     Functions

2     Reference Architecture

3     Input/Output Data

4    Functions of AIMs

5     Input/Output Data of AIMs

6     AIW, AIMs, and JSON Metadata

1      Functions of AIW

The “AI-based Company Performance Prediction” AI Workflow measures the Performance of a Company by providing Default Probability, Organisational Model Index, and Business Discontinuity Probability of the Company within the given Prediction Horizon using its Governance, Financial and Risk data .

2      Reference Architecture

Figure 2 gives the normative Architecture of the “AI-based Company Performance Prediction” Use Case.

Figure 2 – Reference Model of Company Performance Prediction (MPAI-CUI)

In the “AI-based Company Performance Prediction” Use Case:

  1. User defines a Prediction Horizon and feeds Governance, Financial Statement and Risk Assessment data.
  2. Governance Assessment produces Governance Features by processing Governance and Finan­cial data.
  3. Financial Assessment produces Financial Features by processing Financial Stat­ement data.
  4. Risk Matrix Generation produces the Risk Matrix by processing Risk Assessment data.
  5. Prediction produces Organisational Model Index and Default Probability by processing Governance Features and Financial Features.
  6. Perturbation produces Business Discontinuity Probability by processing Default Probability and Risk Matrix.

3      Input/Output Data of AIW

Table 1 – Input/Output Data of AIW

Input Comments
Prediction Horizon Number of months of prediction.
Governance Governance data.
Financial Statement Full financial statement.
Risk Assessment The company assessment of the impact of vertical risks: cyber and seismic assessed according to ISO 31000 Risk Management [6], and ISO 27005 Infor­mation security risk management [7], specific for cyber risk management.
Output Comments
Default Probability The probability of the company default in the specified prediction horizon.
Organisational Model Index The adequacy of the organisational model expressed as a linear score in the 0 to 1 range in the specified prediction horizon.
Business Discontinuity Probability The probability of an interruption of the operations of the company for less than 2% of the specified prediction horizon.

4      Functions of AI Modules

The AI Modules in Figure 2 perform the Functions specified in Table 2.

Table 2 – Functions of AI Modules

AIM Function
Governance Data Assessment Computes the Governance Features.
Financial Data Assessment Computes the Financial Features.
Risk Matrix Generation Builds the Risk Matrix.
Discontinuity and Default Prediction Computes
1.     The Default Probability in the Prediction Horizon.
2.     The Organisational Model Index.
Prediction Result Perturbation Computes the Business Discontinuity Probability in the Pred­iction Horizon by perturbing the Governance Features and Financial Feat­ures.

5      Input/Output Data of AI Modules

Figure 3 -Input/Output Data of AI Modules

AIM Receives Produces
Governance Data Assessment Governance
Financial Statement		 Governance Features
Financial Data Assessment Financial Statement Financial Features
Risk Matrix Generation Risk Assessment Risk Matrix
Discontinuity and Default Prediction Prediction Horizon
Governance Features
Financial Features		 Organisational Model Index
Default Probability
Prediction Result Perturbation Default Probability
Risk Matrix		 Business Discontinuity Probability

6      AIW Metadata

AIW AIMs Names JSON
CUI-CPP Company Performance Prediction X
CUI-GDA Governance Data Assessment X
CUI-FDA Financial Data Assessment X
CUI-RMG Risk Matrix Generation X
CUI-DDP Discontinuity and Default Prediction X
CUI-PRP Prediction Result Perturbation X