class: center, middle, inverse, title-slide # Potentially Fraudulent Reporting is Not a Problem: ## Tracking the Actual Financial State of Affairs and Risks <br /> in Big Companies by Artificial Intelligence ### Alexander Shemetev ### Presentation ### 2022/04/03 (updated: 2022-04-08) --- class: inverse, center, middle # Main Purposes: -- 1) to describe the unique artificial intelligence algorithm and the possibility of its application -- 2) proove that classical financial analysis methods create bias due to the original data distortion -- 3) proove that bigger companies have higher economic incentives to deviate -- * This, classical financial analysis cannot deal with big companies without such AI algorithm -- 4) Present the unique method to overcome the black box principle in the AI and MLT systems --- # Topics for today: -- 1) Introduction: First Outputs of the AI -- 2) Methodology -- 4) The AI Algorithm Implementation -- 3) Black Box principle Elimination Method -- 5) Regionalism Context -- 6) Financial Context and Macroeconomics -- 7) IT Context -- 8) Conclusion --- background-image: url(data:image/png;base64,#PLOT_1.jpg) class: inverse, bottom, inverse background-size: contain --- background-image: url(data:image/png;base64,#PLOT_2.jpg) class: inverse, bottom, inverse background-size: contain --- background-image: url(data:image/png;base64,#PLOT_3.jpg) class: inverse, bottom, inverse background-size: contain -- # AI Reveals the Actual Risk Pictures by Implementing the A-Matrix Method --- background-image: url(data:image/png;base64,#PLOT_4.jpg) class: inverse, bottom, inverse background-size: contain -- # AI Reveals the Actual Trends and Sources of Big Company's Activity --- background-image: url(data:image/png;base64,#PLOT_5.jpg) class: inverse, bottom, inverse background-size: contain -- # The AI Algorithm Implementation Scheme --- background-image: url(data:image/png;base64,#PLOT_6.jpg) class: inverse, bottom, inverse background-size: contain -- # The Black Box Principle Problem --- background-image: url(data:image/png;base64,#PLOT_7.jpg) class: inverse, bottom, inverse background-size: contain -- # The Core Components of the B-Matrix to Remove the Black Box Processes in AI, MLT, and Neural Networks --- background-image: url(data:image/png;base64,#PLOT_8.jpg) class: inverse, bottom, inverse background-size: contain -- # The Regionalism Concept --- # The Economic Logic `$$\pi_{i,j,h,f,c,t}=Revenue_{i,j,h,f,c,t}-Costs_{i,j,h,f,c,t}-Taxes_{i,j,h,f,c,t}$$` -- Where: π is profit *) "i" is a specific firm -- *) "j" is a specific territory unit (like country) -- *) "h" is a holding component -- *) "f" is a fairness of the report -- *) "c" – is a competency parameter -- *) "t" – time component --- # The Economic Logic 2 `$$\pi_{i,j,h,f,c,t}>\pi_{i,j,h,uf,c,t}=>Costs_{i,j,h,f,c,t}<Costs_{i,j,h,uf,c,t}\cup Taxes_{i,j,h,f,c,t}\le Taxes_{i,j,h,uf,c,t}$$` -- Where: π is profit *) "f" is a fairness of the report -- *) "f" – means fair -- *) "uf" – means unfair -- Proof from the contrary: this equation is unlogical => -- => incentives to deviate and provide unfair reporting -- (profit maximizing principle) --- # Therefore, the Previous Equation Turns to: `$$\pi_{i,j,h,f,c,t}<\pi_{i,j,h,uf,c,t}=>Costs_{i,j,h,f,c,t}<Costs_{i,j,h,uf,c,t}\cup Taxes_{i,j,h,f,c,t} >> Taxes_{i,j,h,uf,c,t}$$` --- # The Size of Firms and their Desire to Deviate: `$$\pi_{i,j,h,f,uc,t}>\pi_{i,j,h,uf,uc,t}=>Costs_{i,j,h,f,uc,t}<Costs_{i,j,h,uf,uc,t}\cup Taxes_{i,j,h,f,uc,t} \le Taxes_{i,j,h,uf,uc,t}$$` -- Where: *) "c" – is a competency parameter -- *) "c" – means competent specialists -- *) "uc" – means insufficiently competent specialists -- `$$Costs_{uc}<Costs_{c}$$` -- => Less competent specialists motivate companies for fairer accounting practices --- # The Size of Firms and their Desire to Deviate: * But when the sums of taxes paid grow -- * the incentives to deviate from fair accounting practices also grow -- * Significant growth in taxes usually relates to the increase of the business scales -- * therefore, to the company growth --- # The Size of Firms and their Desire to Deviate: Therefore, the bigger the business is, the higher the incentives to deviate from their fair accounting practices. -- In addition, big companies have higher opportunities to hire maximum competent specialists -- capable of "hiding" substantial shares of profits from third-party observers --- # The Size of Firms and their Desire to Deviate: small firms will only apply unfair accounting if -- cheaper, less competent specialists have enough skills -- to reduce taxes and minimize any fines and penalties -- Or when the public tax and related control systems are inefficient -- so that cheaper incompetent specislists have smaller chances to be caught --- class: inverse, center, middle # => Deviations Should be a Common Practice in Big Companies -- By Pure Economic Logic -- => Classical Financial analysis Tools will not help -- The Suggested AI Algorithm Helps to Reveal the Actual Patterns -- By the A-Matrix Method --- class: inverse, center, middle # Discussion The link to the book ([click here](https://books.google.ru/books?id=HEXHDwAAQBAJ&pg=PA84&lpg=PA84&dq=financial+softs+alexander+shemetev&source=bl&ots=blkD8Dhohu&sig=ACfU3U39NJx1ce9WdUkFdVSNn4RZRj36eA&hl=cs&sa=X&ved=2ahUKEwi07sqC_4P3AhVIyKQKHd02BXQQ6AF6BAgXEAM#v=onepage&q=financial%20softs%20alexander%20shemetev&f=false))