The rule based approach is laborious and time consuming process

February 22, 2019 0 Comment

The rule based approach is laborious and time consuming process, rules are created
based on the experience and expectations (bias) of auditors. Whether a decision is
made, depends on the number of the rules ‘fired’. For example a rule based system
will decide whether a tax refund payment will be made to a taxpayer, if the number
of rules ‘fired’ does not exceed five (5).
1.4 Machine Learning
Machine Learning is a subset of artificial intelligence where computers change and
improve their algorithms themselves through learning from data and information
without being explicitly programmed.
Machine learning is more efficient compared to rules based approach since there
is no need to create detailed rules on each and every model, saving time for the data
analyst and the business expert. The model can be trained using previous results.
In the case of creating a model to predict whether the result of a tax audit will be
material, the results of previous audits can be used to train the model.
One approach to solve complex problems is ‘divide and conquer’. A complex problem
may be broken down to simpler bite size tasks and vice versa a collection of
many small components can be used to create a complex system (Bar Yam, 1997).
Networks can be used to solve complex problems by simply using a set of nodes,
and connections between them.
The nodes can be computational units, receiving input and giving an output after
processing. The process of the input can vary from very simple tasks like multiplication
to very complex if it contains another network.
The connections represent the information exchange between nodes. The flow can
be in one direction (unidirectional) and bidirectional when it flows in either direction.
The behavior of the entire network depends on the individual interactions of nodes
through the connections, which is not visible at the node level. The global behavior
is emergent since the characteristics of the entire network supersede the characteristics
of individual nodes resulting to a powerful tool.
As a consequence is common for networks to be used for modeling in many areas
including computer science.
Artificial Neural Network
An artificial neuron mimics biological neurons by performing computations, and an
Artificial Neural Network(ANN) sees the nodes as artificial neurons.
Natural neurons receive signals through the synapses which are on the dendrites
of the neuron. When a strong signal is received (exceeding a threshold), the neuron
activates and sends a signal through the axon to another synapse.