Machine learning is a branch of artificial intelligence (AI) and computer science. It focuses on using data and algorithms to imitate how humans learn, gradually improving its accuracy.
Artificial intelligence (AI) refers to computer systems that can perform human-like tasks. It is about creating a neural network that intakes large quantities of data and, on its own, builds algorithms that help it determine the right way to perform a task.
Machine learning solutions learn from experience without being explicitly programmed. You need to select the models and feed them with data. The model then automatically adjusts its parameters to improve outcomes. In general, the more data you provide, the more accurate the results are. Data scientists train machine learning models with existing datasets. Then, they apply well-trained models to real-life situations.
Machine learning will automate jobs that most people thought could only be done by people.
You can apply Machine learning in various fields, from social media and product recommendations to virtual personal assistants and self-driving cars.
Read on to find out how you can implement it in economics.
“A breakthrough in machine learning would be worth ten Microsofts.”-Bill Gates.
Machine learning and taxation
Governments can use machine learning to collect taxes faster, reduce tax evasion, and provide enhanced tax services.
Taxpayers can use machine learning to make tax compliance faster and cheaper (in the long run).
You can use Machine learning in taxation in many areas, such as:
- automating repetitive tax processes
- extracting tax sensitive data
- identifying tax deductions and credits
- applying to tax classification (expense classification, depreciation, tax status classifications)
- forecasting tax burden (using predictive algorithms, considering all relevant factors)
And many other fields.
Machine learning and finance
Many financial companies have already taken advantage of this technology. It can reduce operational costs, increase revenues, and enhance user experiences.
Process automation allows us to replace manual work, automate repetitive tasks, and increase productivity.
As a result, companies can optimise costs, improve customer experiences, and scale-up services.
Although still in its infancy, machine learning will be a game-changer in the supply chain.
Machine learning algorithms are excellent at detecting frauds. Banks, for instance, can use this technology to track thousands of transaction parameters for every account in real-time.
Financial monitoring is another security use for machine learning in finance. For example, data scientists can train the system to detect many micropayments and flag such money laundering techniques as smurfing.
A well-trained system performs the same underwriting and credit-scoring tasks in real-life environments. As a result, such scoring engines can help human employees work much faster and more accurately.
Machine learning and exportation
Since trade affects employment and wages, machine learning and exportation, predicting future trade patterns is a high priority for policymakers worldwide. Machine learning techniques can explain and forecast these patterns better than traditional economic models.
Not all firms have the same ability to sell their goods and services abroad. Machine learning can train an algorithm to predict a firm’s ability to start exporting and assess how far it is from becoming an exporter. Also, the algorithm can help predict which industries have the potential for export and how competitive they can be.
Machine learning and artificial intelligence have already found their place in economics, but their role is constantly increasing. If you want your business to keep up with the competition, you need to invest in the skills and resources necessary to implement this new technology.
As Nick Bostrom said, “Machine intelligence is the last invention that humanity will ever need to make.”
If you want to find out more helpful information about similar topics, read our blog section.