Download How Artificial Intelligence Helps in Development of Accounting

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. Business should not lose sight of where AI could go because there are many
potential benefits to current and new businesses of future research. The idea of robotic domestic workers is still
far fetched but companies are making progress even here.
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