Predicting company performance

This content delves into the prediction of company performance through the MPAI approach to standardisation, focusing on AI modules, governance assessment, financial features, default probabilities, business continuity, and risk assessment. The MPAI-CUI workflow details the inputs, processing, and outputs involved in predicting default probabilities and business continuity probabilities based on organizational model index and perturbations. An AI-based standard prediction using neural networks is also discussed with back-testing results on a sample of 160,000 companies.

• Company Performance
• Prediction
• Standardisation
• Business Continuity
• Risk Assessment

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1. Predicting company performance Guido Perboli, 2023 March 31 T09:00 & 19:40 UTC 1 26-Feb-25

2. MPAI approach to standardisation AI AI Open market of components with standardises functions and Interfaces, competing in performance. Module (AIM) Module (AIM) Outputs Inputs User Agent AI Workflow (AIW) AI AIM Storage AI Module (AIM) Module (AIM) Controller Global Storage MPAI Store Communication Access MPAI-AIF enables independently sourced AI Modules having standardised interfaces to be executed in an environment with standardised APIs. 2 26-Feb-25

3. Company Performance Prediction Prediction Horizon Organisational Model Index Governance Governance Features Governance Assessment Default Probability Prediction User Agent Financial Statement Financial Assessment Financial Features Default Probability Business Discontinuity Probability Perturbation Risk Matrix Generation Risk Risk Matrix Assessment Controller Store Global Storage Communication 3 26-Feb-25

4. What does performance mean? Default probability: the probability the company will default (e.g., crisis, bankruptcy) in a specified number of future months dependent on financial features Organisational Model Index: the adequacy of the organisational model (e.g., board of directors, shareholders, familiarity, conflicts of interest) Business continuity Index: the probability of an interruption of the operations of the company for a period of time less than 2% of the prediction horizon. 4 26-Feb-25

5. MPAI-CUI Workflow 1. Input: 1. Prediction Horizon 2. Governance Data 3. Financial Data 4. Risk Assessment Data. 2. Processing AI Module Input data Output data Governance Assessment Governance and Financial Governance Features Financial Assessment Financial Statement Financial Features Risk Matrix Generation Risk Assessment Risk Matrix Prediction Governance Features, Financial Features Organizational Model Index, Default Probability Perturbation Default Probability, Risk Matrix Business Discontinuity Probability 3. Output 1. Organizational Model Index 2. Default Probability 3. Business Discontinuity Probability 5 26-Feb-25

6. MPAI-CUI An AI-based standard Prediction AIM is a neural network. Back testing on a sample of 160.000 companies both active and bankrupted Prediction Accuracy: 85% vs 37% of traditional techniques Reviewed by the scientific community See further details in G. Perboli and E. Arabnezhad. A Machine Learning- based DSS for mid and long-term company crisis prediction. Expert Systems with Applications, 174, 114758, 2021 6 26-Feb-25

7. AI-based standard Novelties of MPAI-CUI Extracts the most relevant data with controlled information analysing the large amount of data required by regulation. loss by Extends prediction horizon up to 60 months, using AI. 7 26-Feb-25

8. How is MPAI-CUI going to be used? PUBLIC AUTHORITIES BANKS/FINANCIAL INSTITUTIONS COMPANY BOARDS + Develop efficient strategies. + Assess public policies in advance + Assess the financial health of companies + Identify clues to crisis or bankruptcy years in advance. + Evaluate scenarios of public interventions + Aids the financial institution to make the right decision in: funding or not funding that company, having a broad vision of its situation. + Identify proactive actions to increase resiliency of industrial sectors. + Help to: Decide path to recovery, Conduct what-if analysis, Devise efficient strategies. 8 26-Feb-25

9. A real example: Evaluate the effects of a public policy Evaluate the impact of funds made available to SMEs in the Piedmont region by analysing the probability of default before and after public intervention. The study confirmed the effectiveness of the public intervention put in place by showing that public intervention improved the default probability. See further details in G. Perboli et al. Using machine learning to assess public policies: a real case study for supporting SMEs development in Italy. TEMSCON 2021 9 26-Feb-25

10. Towards version 2.0 Inclusion of new AI modules for additional risks Cyber, climate, infrastructures & buildings Incorporates the different risks inside a more general framework: the Risk Control Tower Under testing for the EBA binding standards on Pillar 3 disclosures on ESG risks Buildings and climate 10 26-Feb-25

11. Risk Control Tower 11 26-Feb-25

12. Join MPAI Share the fun Build the future! We look forward to your participation in this exciting project! https://mpai.community/ 12 26-Feb-25