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How Artificial Intelligence Helps in Development of Accounting Systems: Developed and Developing Countries perspective Syed Moudud-Ul-Huq 1 , Issa Ahammad 2 & Mostafa Monsur 3 1. Department of Business Administration, Mawlana Bhashani Science and Technology University 2. Department of Business Administration, World University of Bangladesh. 3. Department of ICT, Mawlana Bhashani Science and Technology University How Artificial Intelligence Helps in Development of Accounting Systems: Developed and Developing Countries perspective Abstract: Artificial intelligence is the most advanced technology in the world. We will demonstrate how the artificial intelligence is helping the development of accounting system. According to Perrow's sociological framework as a basis for a comparative organization analysis of the impact of expert systems on organizational issues. The study analyses the relative impact of expert systems on two different types of accounting work, auditing and tax. The results indicate an impact on factors that ultimately improve productivity. The aggregate results indicate that expert systems are found to allow the user substantial control of search for solutions and discretion on whether to follow system recommendations, increased access to top management, and a decrease in the need for supervision. Accounting tasks involve a wide range of structured, semi-structured and unstructured decisions. The heart of auditing and assurance involves the less-structured decisions and analysis that include much uncertainty, caused by risks and lack of information. Keywords: Accounting, Artificial Intelligence, Knowledge-based Systems, Taxation, Organization, Auditing. 1. Introduction Accounting information systems moved out of the arena of paper journals and ledgers and into computerbased formats with the advent of computers. Unfortunately, in many cases all that was done was to develop computerized systems that the computer used as a more efficient type of paper processor or calculator. Consequently, in many cases, accounting databases have become vast storehouses of limited information about specific accounting transactions. As a result, these systems do not meet the needs of decision makers. One approach to this problem is to integrate Artificial Intelligence (AI) into accounting databases to try to develop systems that mitigate the difficulties of traditional systems. Although, accounting database theory has received substantial attention, little work has been done on the application of AI/expert systems (ES) to accounting. Literature review: Artificial intelligence is one of the advanced techniques in computer science. A.I is the study of ideas that enable computers to be intelligent .The problem of analyzing spatially and time dependent data occurs in many different fields: economics, sociology, ecology and environment management, agriculture, hydrology, engineering and finally architecture. As a matter of fact time .Artificial Intelligence has been used in a wide range of fields including medical diagnosis, accounting, stock trading, robot control, law, remote sensing, scientific discovery and toys. Accounting is no longer limited to pen and paper now a days. Artificial Intelligence has been used in a wide range of fields including medical diagnosis, stock trading, robot control, law, scientific discovery and toys. However, many AI applications are not perceived as AI: "A lot of cutting edge AI has filtered into general applications, often without being called AI because once something becomes useful enough and common enough it's not labeled AI anymore. Many thousands of AI applications are deeply embedded in the infrastructure of every industry.“ In the late 90s and early 21st century, AI technology became widely used as elements of larger systems, but the field is rarely credited for these successes. Now the computer technology is widely used in accounting system. We will demonstrate how the Artificial intelligence is being used in accounting system. The reality in many business organizations is that some of the important complex events cannot be used in process management, .because they are not detected from the work- flow data and the decision makers can not be informed about them. Detection of events is one of the critical factors for the event-driven systems and business process management. Semantic models of events can improve event processing quality by using event meta-data in combination with ontologies and rules (Artificial Intelligence). As we can see Japan are using robotic technology which far more cost-effective than a human resource. A robot does not need rest so they can deliver service 24/7 but for human its totally impossible to work 24/7.To deploy robot in industry still now is costly but after the deploy they don't require any wages or any facilities like human which is cost effective off-course. Although in the developing country don't have the ability to deploy such AI empowered robot in the industry but now a day those country are focused on digitalizing their industrial system which is also powered by AI. Objectives: To better understand the Artificial Intelligence and how its helping the accounting sector as well as the impact of AI in our socio economic life. To encourage research in artificial intelligence by identifying areas of accounting in need of exploration with the methods of these two disciplines. To provide opportunities for interchange of ideas in Artificial Intelligence among accounting academics and businesses practitioners. To identify the present development of accounting using the Artificial Intelligence. Methodology: The term Artificial Intelligence stands for a large number of algorithms, models and techniques derived from the osmosis of statistics, machine learning, databases and visualization. Several of these methods have been applied for examining financial data. Popular DM methods that will be mentioned in this study are Neural Networks, knowledge base system. This study is based on secondary study with the help of various papers from Internet. The study of expert system is extracted from the paper “Artificial Intelligence and Expert Systems in Accounting databases: Survey and Extensions. Weka tools has been used to analysis the data in expert system. Weka is a software tools that is vastly used for finding the hidden data from a set of scattered data. It follows the machine learning approaches that is it takes some previous data as input and then analysis this data. Then this tool can make prediction for future financial transaction. A common example is predicting the stock market. Accounting: Accounting is the art of recording, summarizing, reporting, and analyzing financial transactions. An accounting system can be a simple, utilitarian check register, or, as with Microsoft Office Accounting, it can be a complete record of all the activities of a business, providing details of every aspect of the business, allowing the analysis of business trends, and providing insight into future prospects. Artificial Intelligence: Artificial Intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of the activities computers with Artificial Intelligence are designed for include speech recognition, learning, planning and problem solving. Machines can often act and react like humans only if they have abundant information relating to the world. Importance of Artificial Intelligence in business: Artificial Intelligence aims to improve machine behavior in tackling complex tasks. Human have an interesting approaches to problem solving based on abstract thought high-label deliberative reasoning and pattern recognition. Artificial Intelligence can help us understand this process by recreating it, then potentially enabling us to enhance it beyond our current capabilities. Artificial Intelligence Applications: A number of types of decision-making theory and AI technology have been applied to auditing and assurance problems. However, that application has been largely sparse and mostly only at the theoretical level. Some expert systems have been in use at public accounting firms, such as ADAPT (Gillett, 1993), Deloitte Touche’s Audit Planning Advisor, Price Waterhouse’s Planet, Arthur Andersen’s WinProcess and KPMG’s KRisk (Brown, 1991; Bell et al., 2002; Zhao et al., 2004).Most of these systems address risk assessment (Zhao et al., 2004).Not all applications of AI to audit problems have proven successful in the long run. In 1995, Arthur Andersen was reported to have developed a system to help in assessing the litigation risk associated with audit clients (Berton, 1995). History suggests that it was not ultimately beneficial. Conversely, AI has mostly been applied successfully only to the more, structured, programmable and repetitive tasks in which gathering human expertise is not an extreme difficulty. See, for example, the extensive literature on expert systems for audit tasks that dates from the mid 1980s (Abdolmohammadi, 1987; Gal and Steinbart, 1987; Hansen and Messier, 1987; Brown and Murphy, 1990; Denna et al., 1991; Brown and Coakley, 2000). In auditing in particular, the uncertainly issue has driven the development of new areas of research, such as Dempster–Shafer theory and belief functions. However, progress in applying intelligent systems to auditing problems has not been impressive. Therefore, this section of the paper reviews the literature and identifies auditing tasks for which working AI applications should be developed. Abdolmohammadi (1991) studied 332 tasks that auditors perform. Although the number of potential tasks is high, not all are suitable for AI application. Some are very structured and fairly routine, such as computation of inventory ratios. Others are much less structured and rely on uncertain and incomplete information, such as a going-concern determination. Audit Task: Audit tasks elicit a wide range of characteristics. Over 400 individual audit tasks have been identified. Though the study of audit decision aids has been going on for years, no systematic model identifies audit tasks for decision-aid development (Abdolmohammadi, 1991). Some of the major tasks are discussed here. Analytical review procedures: Analytical review procedures are undertaken by auditors for the purpose of obtaining audit evidence. They may use a wide variety of techniques. Koskivaara (2004) reviews the use of neural networks for these purposes. Classification: Some audit tasks are largely classification problems: Is this a collectible debt or a bad debt? Is this a legitimate transaction or a questionable one? Welch et al. (1998) studied auditor decision behavior in a fraud setting and suggest that genetic algorithms are an appropriate approach to solving these problems. Internal control evaluation. With the onset of Sarbanes–Oxley, the evaluation of internal controls has become even more important to audit. Meservey et al. (1986) developed a computational model of the internal control review process of one auditor and implemented it as an expert system. Changchit and Holsapple (2001) developed an expert system to support managers’ internal control evaluations and describe the managers’ reluctance to use it. Risk assessment: Many audit tasks boil down to risk assessment. Risk assessment involves pattern matching and identifying deviations or variations (Ramamoorti et al., 1999). Chiu and Scott (1994) suggested the use of neural networks to assist in risk assessment. Lin et al. (2003) evaluated an integrated fuzzy neural network for financial fraud detection and found that it outperformed most statistical models and prior artificial neural networks. Davis et al. (1997) describe a prototype system for risk assessment that combines both neural network and expert systems technology. Hwang et al. (2004) apply case-based reasoning to internal control risk assessment. Going-concern decisions: A going-concern uncertainty decision is given by an auditor when the client is at risk of failure or otherwise is in distress that threatens its continuance. This decision is an unstructured audit task that can benefit from the use of decision models. Often, the decision involves both qualitative judgment and quantitative analysis. Artificial Intelligence Technologies and Techniques: With all the research on audit expert systems, their use ought to be widespread now. However, they have not lived up to their potential because they have a problem with a lack of user neutrality (O’Leary, 2003). Therefore, other, more complex AI approaches need to be investigated for audit tasks. Genetic algorithms are proposed by Welch et al. (1998) as a potentially useful application for modeling auditor behavior in fraud decisions. Lensberg et al. (2006) applied genetic programming to bankruptcy prediction. This may also be useful in going-concern decisions. Neural networks have been proposed as a good application for a range of audit tasks (Calderon and Cheh, 2002). Because of their ability to model non-linear relationships and to handle incomplete data, neural networks may be particularly helpful for risk assessment tasks. Koh (2004) suggests the use of neural networks and data mining for going-concern predictions. Koh discovered that neural networks and decision trees are powerful tools in analyzing the complex, non-linear and interactive relationships involved in going-concern analysis. Fuzzy systems may be particularly useful for some audit tasks because of their inherent allowance of qualitative factors. For materiality decisions, this may be much better than typical quantitative rules of thumb (Comunale and Sexton, 2005). Hybrid systems. Because some audit tasks involve the use of both quantitative analysis and qualitative judgment, hybrid systems may be appropriate. Lenard et al. (1998) developed a hybrid system combining a statistical model with an expert system to suppose going-concern judgments. Other audit tasks may benefit from this approach (Lenard, 2001). May et al. (1993) applied a similar approach to claims auditing at Blue Cross, in a commercial application. Relationship to Artificial Intelligence Systems: Unfortunately, the current structure of the events approach suffers from being limited by the technology in which it was conceived: at the time of its development, AI-based systems that could assist the decision-making process did not exist. The development of AI and ES provides an opportunity to build intelligence or expertise into the database in order to assist users. Such models could assist users by sorting through large quantities of data without the user's direct participation, assist the decision maker under time constraints, suggest alternative models to evaluate or search for data, etc. In addition, the development of AI would suggest that rather than just numeric data, symbolic information also be captured to additionally characterize the transaction. Further, it suggests the use of natural language processes and expert models be developed in the systems to facilitate interaction of the user with the system. Unfortunately, use of AI/ES in accounting database systems is not straightforward. Thus, this paper addresses the extraction, organization, storage, and application of intelligence to accounting databases. Neural network techniques: knowledge-based system: Usage of background knowledge about events and their relations to other concepts in the application domain, can improve the quality of event processing and decision making. This system is used by the stock market and to make a forecast of the demand of a product. Neural Network Technology And Its Investment Applications: The human brain is made up of a web of billions of cells called neurons, and understanding its complexities is seen as one of the last frontiers in scientific research. It is the aim of AI researchers who prefer this bottom-up approach to construct electronic circuits that act as neurons do in the human brain. Although much of the working of the brain remains unknown, the complex network of neurons is what gives humans intelligent characteristics. By itself, a neuron is Research has shown that a signal received by a neuron travels through the dendrite region, and down the axon. Separating nerve cells is a gap called the synapse. In order for the signal to be transferred to the next neuron, the signal must be converted from ……? The experiment consisted of three phases (Figure A). In the first phase a genetic algorithm (GA) searched the space of NNs with different structures and resulted a generation with the fittest of all networks searched based on a metric which was either: TheilA or TheilB or TheilC or MAE. The GA search was repeated three times for each metric.Then the best three networks were selected from each repetition of the GA search and for each one of the metrics. The output of the first phase was a set of thirty six network structure .In the second phase for each one of the thirty-six resulting network structures we applied the following procedure. We trained (on Training1 set) and validated (on Validation1 set) the network. Then we used the indicated number of epochs from the validation procedure and based on it we retrained the network on the Training1 plus the Validation1 set. Finally we tested the performance of the network on unseen data (Validation2 set). This procedure was repeated 50 times for each network structure for random initializations of its weights. From the nine networks for each performance statistic, we selected the most stable in terms of standard deviation of their performance.Thus the output of the second phase was a set of four network structures. During the third phase for each one of these four networks we applied the following procedure 50 times. We trained each network on the first half of the Training Set and we used the remaining half for validation. Then, using the indicated epochs by the validation procedure, we retrained the network on the complete Training Set. Finally we tested the network on the Test Set calculating all four metrics. The performance for each network on each metric was measured again in terms of standard deviation and mean of its performance over 50 times that it was trained, validated and tested. Artificial neural network are simple processors connected in complex networks of paths to other neurons or external sensors. Neurons receive input signals, or values, from other neurons, and produce an output signal which is communicated to other neurons. Each connection between nodes in a neural network is weighted. The strength of a connection between any two neurons in a network is indicated by the value of this weight. Since all the weights in a network can be different, each connection in an artificial neural network thus has its own strength, or relative importance. The “knowledge” or learning of the network is stored as the value of the weights between nodes, and the system learns new information by adjusting the weights. Robotic technology: Robot is built with complete the AI system. All AI technique is used to make a robot that include Artificial neural network, knowledge-based system and all possible decision making system. Only for AI system it is now possible to make decision for a machine called ROBOT. Robot technology is a growth driver with exponential progression and is therefore of high relevance to a broad range of branches and application domains. DTI is committed to create positive impact in as many branches as possible where some of the most dominant are: Manufacturing & Food industry: An industrial robot is defined by as an automatically controlled, reprogrammable, multipurpose manipulator programmable in three or more axes. The field of robotics may be more practically defined as the study, design and use of robot systems for manufacturing (a top-level definition relying on the prior definition of robot). Cost effective, reliable and fast industrial robot in the food industry have been the key factor in the sector’s automation revolution. Just as the automotive sector became heavily automated in the 1970s, so the first decade of this century brought with it a scramble amongst food manufacturers to invest in the most efficient and future proofed industrial robots on the market. Health Care & Welfare: The Japanese government is shooting for low-cost nursing care robots around the 100,000 yen (1,022 dollars) price range, so that it would be easy to commercialize them via mass production. The Economy, Trade and Industry Ministry is expecting the market for such products to boom, valued at upwards of 400 billion yen (4.09 billion dollars) by 2035. And also according to the Health, Labor and Welfare Ministry, Japan has a chronic shortage of nursing care workers, falling short by at least 700,000 of the target number of workers in 2010. The ministry predicts a need for 4 million such workers in 2025. With such low-cost nursing home robotic implements in place, the government is hoping to at least help out the nursing care workers who are in place to do their jobs easier and more efficiently. Education & Entertainment: The automated process revolution in educational and entertainment robots promises strong growth that extends beyond the direct markets. Once students learn how to use robots, they move into industry and make functional robots for business automated process and for communications and entertainment. Robots are automating systems, leaving more time for leisure activities. The educational kits are designed for pure fun and for educational competitions where students put together modules in innovative ways to create designs that work. Although the Korean government has deployed teacher robot to teach English to their student Energy & Climate: Japan, Germany, USA and also South-Korea and many other reach nations are trying to deploy robotic technology in the Energy sector which will be benefit for themselves. Robots are tired less product that can work 24 hours a day without any problem. So the energy company will be able to make a huge profit for sure. A report says Japan are nearly deploying robot technology to protect the civilization form Tsunsmi. We know Tsunami for which caused a-lot of people died, after that disaster Japan government working on for the advance tsunami notification using robotic technology. They have developed a system in the coastal area which can identify the shake of earth-quake and then if it is tsunami like thing then the system will alert to people for the tsunami, which can cut off the damage. Buildings & Urban Performative spaces: Just consider the safety benefit of using robots in construction. We could greatly reduce the deaths and injuries on construction sites through using Advanced Robotics .This kind of robot providing a huge cost cut. Rescue management job: Human has a lot of limitation. Robot can perform Rescue management job like fire, nuclear plant disaster, earth-quake , rescue form the damage building.For example Japan has used robot to rescued and turn off the recently damaged Fukushima nuclear plant because it was impossible to for human to work in that highly radio-active place. But robot was perfectly done the job. Knowledge-based system: A knowledge-based system is a computer program that reasons and uses knowledge to solve complex problems. Traditionally, computers have solved complex problems using arithmetic algorithms created by programmers. With knowledge-based systems, human knowledge is captured and embedded explicitly within a program in a symbolic format. Knowledge-based systems usually contain three components: a human-computer interface , a knowledge base, and an inference engine program. The human-computer interface is where the user formulates queries, which the knowledge-based system uses to solicit further information from the user and explain to the user the reasoning process employed to arrive at an answer. The knowledge of one or more human experts in a specific field or task is stored in the knowledge base. The knowledge base is set up as an intelligent database-it can usually manipulate the stored information in a logical, natural, or easy-to-find way. It can conduct searches based on predetermined rules of defined associations and relationships as well as by the more traditional data search techniques. The primary goal of knowledge-based systems is to make expertise available to decision-makers who need answers quickly. Expertise is often unavailable at the right place and the right time. Portable computers loaded with in-depth knowledge of specific subjects can bring years' worth of knowledge to a specific problem. The first knowledge-based or expert system, Dendral, was developed in 1965 by Edward Feigenbaum (1936-) and Joshua Lederberg of Stanford University in California and was used to analyze chemical compounds. Since 1965, knowledge-based systems have enhanced productivity in business, science, engineering, and the military. They also attempt to predict the weather, stock market values, and mineral deposit locations; give a medical diagnosis; dispense medication; and evaluate applications and transaction patterns. Knowledge-based system in stock market: Knowledge-based system plays a great role in stock market analysis. knowledge-based system uses the previous knowledge and based on the input knowledge the system gives a decision. If we give input the data of the previous one year to the knowledge-based system it can give us the present situation by analyzing those input so that we can make quick and almost perfect decision. The perspective of Artificial Intelligence in developed and developing countries: AI research is conducted by a range of scientists and technologists with varying perspectives, interests, and motivations. Scientists tend to be interested in understanding the underlying basis of intelligence and cognition, some with an emphasis on unraveling the mysteries of human thought and others examining intelligence more broadly. Engineering-oriented researchers, by contrast, are interested in building systems that behave intelligently. Some attempt to build systems using techniques analogous to those used by humans, whereas others apply a range of techniques adopted from fields such as information theory, electrical engineering, statistics, and pattern recognition. Those in the latter category often do not necessarily consider themselves AI researchers, but rather fall into a broader category of researchers interested in machine intelligence. There are a lot of companies who are working on the development of accounting using Artificial Intelligence. In developed country like United States of America they are using advanced system for stock market analysis, analysis of demand of a product. They are vastly using the artificial intelligence for: Number Subject Developed country 1. Credit authorizing screening 2. Mortgage risk analysis 3. Financial analysis and Developing country and Developed country tackle payment card fraud is implementing a holistic, multi-pronged strategy based on knowledge discovery at every stage of the card life-cycle. By moving away from an existing one-size-fits-all model and applying a custom or network model that is based on detailed views of global transactions, companies can cost-effectively reduce risk and prevent losses. While the developing country are not using this techniques. As a result the credit card fault ratio is higher .Card fraud costs the U.S. card payments industry an estimated USD 8.6 billion per year. Although just 0.4% of the USD 2.1 trillion in card volume per year, this number remains a troubling area for the industry due to the volatile nature of fraud. Data mining techniques used for the mortgage risk analysis. The company looks after the client's previous financial then based on this data the company gets idea of the characteristics of that client and then the company take decision whether mortgage loan to allow or not. In developed country they are using only paper document which could be false. That’s why the company attacked by fraud client and face a huge loss. economic Matching algorithms are used for detecting incongruities in the behavior of users or transactions when compared to earlier known profiles or models.The main steps followed in this data analysis The developing country are not using this kind of techniques to analysis of financial and economic events. technique are – collecting data, preparation of data, analyzing data and reporting. 4. Risk rating of exchange The developed country are using While the developing country are traded knowledge-based system for risk far behind from risk analysis and analysis of exchange rating. for this reason they are facing huge losses in businesses. 5. Detection of regularities in Piecewise Linear Representations security price movement (PLR) and Artificial Neural Networks (ANNs)to analyze the nonlinear relationships between the stock closed price and various technical indexes, anduncovering the knowledge of trading signals hidden in historical data. As there is no such system in stock market in developing country that’s why its not possible to predict the regularities of security price. 6. Prediction of default and Bankruptcy prediction is one of bankruptcy the most important business decision-making problems. Intelligent techniques have been employed in order to develop models capable of predicting business failure cases. The developed country employed classification methods, performance metrics issues, the input data and data sets, feature selection and input vectors and finally, the interpretation of the models and the extraction of domain knowledge On the other hand developing country working on manual process to detect the bankruptcy which is caused them a huge losses. 7. Risk analysis of income investment fixed The developed country are using heuristics search to help you to avoid the most common pitfalls and evaluate investment information critically, as well as show you how to reduce risk and enhance returns. It will save you money and it will save you time. In the developing country, they are not using heuristics search. So there is huge risk for investor for business losses. 8. Detection of management Artificial Neural Network (ANN) fraud AutoNet in conjunction with standard statistical tools to investigate the usefulness of these publicly available predictors. Our study results in a model with a high probability of detecting fraudulent financial statements on one sample. On the other hand the management is able to fraud as they did not deploy such system to detect the management fraud. 9. Machine learning techniques Machine learning techniques to In developing country there is no to automatically identify automatically identify such system to identify the characteristics of fraud characteristics of fraud. Expert characteristics of fraud. systems to encode expertise for detecting fraud in the form of rules. 10. Artificial Intelligence Marketing in Artificial intelligence functions are made possible by computerized neural networks that simulate the same types of connections that are made in the human brain to generate thought.. It's seeing little use in CRM right now, though it has tremendous potential in the futureAI-enhanced analytics programs also provide survival modeling capabilities suggesting changes to products based on use. High-tech data mining can give companies a precise view of how particular segments of the customer base react to a product or service and propose changes consistent with those findings In developing country the marketing process is traditional. For this reason they are having huge loss. Credit card providers, telephone companies, mortgage lenders, banks, and the U.S. Government employ AI systems to detect fraud and expedite financial transactions, with daily transaction volumes in the billions. These systems first use learning algorithms to construct profiles of customer usage patterns, and then use the resulting profiles to detect unusual patterns and take the appropriate action (e.g., disable the credit card). Such automated oversight of financial transactions is an important component in achieving a viable basis for electronic commerce and so on. Developed country are using high-tech data mining that can give company a precise view of how particular segments of customer base react to a product. So the customer satisfaction which is very important for any company to increase their net-profit can be identified by artificial intelligence. Big tech giant like google, apple, microsoft are using this data mining system to identify the choice of their customer and then release their new product and they get a huge profit. But the developing country are not using the Artificial Intelligence in accounting. Those country are trying to use AI system in accounting ,business system .This is because of lack of researches and valuable resources. This is one of the big reasons for instability of the economics in developing country. Findings: In the discussion above , it is clear that, the developing country is far behind from taking advantage of Artificial Intelligence. The most advanced deployment of Artificial Intelligence is robotic technology. The McKinsey’s reports says that, the sales of industrial robot in 2012 in North America hit a record of 22598 pieces and each robot replace 10 jobs. So we can measure how cost-effective the robot is. But in developing countries still they have no such plan to deploy robot in businesses. Because the government of developing countries does not have any intention to research of Artificial Intelligence .Its seems that there are a good number of researchers from developing countries involved in researching of AI in the developed country like America, Japan, Germany, England. The main reason behind this is that, their governments are not interested to provide the costing of research as their vision and goal is not rich enough and also most of the people in developing country are still uneducated so that they are unaware of the power of AI. Though its a open debates about whether robots can ever create as many jobs as they destroy are misplaced for human. By itself, even the most powerful innovation cannot make or nullify a single job: it is human beings, and the rhythms of capital accumulation, that do that. And right now both the temper of human beings and the tempo of capitalism do not favor robots. Recommendation: Artificial Intelligence is helping accounting to be automated so that the businesses can be run so smoothly. The developed country are making huge progress using the Artificial Intelligence system. So we are strongly recommending that the developing country like Bangladesh needs to research on Artificial Intelligence mostly the neural network, knowledge-based, data mining techniques, experts system to be successful in businesses. Also there is a lot of scope for robotic technology. We need to do more research on robotic technology so that their perfection to the job can be increase and to decrease the deployment cost. Though we know its really a great tools for accounting but we need to be care-full about AI because; Artificial Intelligence is not creative (though lately this is open to debate), it is limited in the use of previous data (also subject to debate), it cannot make use of a very wide context of experiences (also subject to debate), and it does not use common sense (also subject to debate).lack the human touch. Human qualities are sometimes ignored, Artificial Intelligence can malfunction and do the opposite of what they are programmed to do. There is no filtering of information; this type technology can be miss-used to cause mass profit loss for the businesses. More over it requires a expert resources to handle a Artificial Intelligence system guided accounting software. The research on AI in accounting has almost exclusively been undertaken by accounting researchers. The vast majority of these authors are experts on one or more areas of accounting, but they lack an educational and experience background in AI. Many have come to AI through a general background in information systems. Others simply recognize the need for AI applications in the task domain they study and have educated themselves in the AI domain for the purpose of performing that research. Some have the goal of educating other accounting researchers about a specific AI technique; see Etheridge et al. (2000). Conclusion: AI can have a positive effect for accounting benefit and has lead to some very useful systems that have found their way into the heart of business activity. The Neural Networks, Genetic Algorithms, Knowledge base system, data-minig are really a power full tools for the success of running a business though It is difficult for business to see general relevance from AI. 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