The Human Element in Machine Learning
July 27, 2017
Few technologies have been growing as quickly or as substantially as artificial intelligence and machine learning. It's expected that spending within the AI and machine learning sector may approach $47 billion by 2020, as organizations struggle to take advantage of increasingly large data sets. But even in the wake of this new technology, it becomes important for organizations to remember the human element. An over-reliance on machine learning can ultimately lead to more complications than solutions.
The Principle Advantages of Machine LearningMachine learning has increased in popularity in the wake of big data. Companies and their applications now collect large volumes of data that can then be analyzed for patterns. Through pattern analysis, machine learning algorithms are able to draw important and actionable conclusions. Without machine learning, many organizations find themselves with vast amounts of data that they have no hope of ever analyzing or compiling.
Machine learning algorithms are designed to get better at recognizing certain patterns with time and training. They absorb sample data sets and are trained on how to treat these data sets. From there, they are able to analyze live data with a certain level of confidence. The more similar the sample data sets are to the live data sets, the more likely there are to be positive data outcomes.
Leveraging Machine Learning for Fraud and Risk AnalysisFraud and risk assessments have a lot to do with patterns. There are patterns that are considered to be natural within the financial sectors and there are patterns that tend to denote tampering. Traditionally, fraud has often been detected by recognizing unusual patterns in data, which do not appropriately correlate to averages. It's understandable, then, that machine learning can be used for superior fraud and risk analysis.
Algorithms can be trained to identify fraud, risk, and other negative patterns -- and can then be used on live data to detect this automatically. As the system learns and grows, fraud analysis can become quite accurate. Because it is an automated system, it can theoretically catch more instances of fraud than a human department could -- and it can theoretically catch these instances faster. But just because artificial intelligence is uniquely suited to some forms of fraud and risk detection doesn't mean that it's all that is necessary.
The Human Element of Machine LearningMachine learning algorithms have to be trained based on sample sets. Human trainers will go through these sample sets and analyze them for suspicious activity; when suspicious activity is found, it will be marked as relevant. From there, the machine learning algorithm will learn to associate this relevant data with suspicious behavior. The most important thing to consider here is the fact that all of the sample data must be derived from human trainers. The accuracy of this data is going to control the accuracy of the machine learning algorithm.
In order to continue improving, the machine learning AI will need constant human input. People will need to validate the results of the machine learning AI, especially as it encounters behaviors and data that it has not encountered before. The further the live data strays from the sample data, the less likely the machine learning AI is to be accurate. In fact, if left to its own devices, the machine learning model may steadily become less and less accurate, especially if the data sets are shifting.
Though artificial intelligence and machine learning are excellent all-around tools, they are only tools. They cannot be used as replacements for the human element, which is what is used to balance and to correct them. Machine learning requires a human teacher; it's highly dependent on the training of accomplished and skilled individuals. But when properly managed and maintained by humans, AI and machine learning become powerful tools for the streamlining and automation of fraud prevention and risk assessment. Together with a professional staff, AI and machine learning can be leveraged to vastly reduce the amount of work hours spent identifying and mitigating fraud -- and can be used as a first line of defense to catch suspicious activity before there's a need for human eyes.