Machine learning is being deployed by organizations in finance and retail to prevent fraud, but the deployment of ML is not an instant fix.
Online fraud continues to increase as more of financial lives are brought online, opening up new avenues for fraudsters to crack. According to the Federal Trade Commission, consumer said they lost $3.3 billion to fraud in 2020, double the $1.5 billion figure reported in 2019.
That is only expected to increase in the coming years, which has led organizations and developers to try and curb the threat through machine learning, a type of artificial intelligence that tries to teach a model.
This is important for fraud prevention, as the goal of deploying machine learning is that it will start to learn how to detect fraudulent transactions and get ahead of the curve, potentially spotting new types of fraud.
“Machine learning for fraud detection works by analyzing consumers’ current patterns and transaction methods,” said Megan Quinn of 3Cloud, a data and analytics consulting firm. “It can analyze these behaviors faster and more efficient than any human analysis and as a result, it can quickly identify if there is a deviation from normal behavior. This allows for opportunities in real-time approval by the user before a transaction can be complete.”
However, the deployment of a machine learning solution should not be considered an instant fix for fraud, and developers will still need to continuously tweak the model to ensure that it doesn’t fall adrift.
“While machine learning can save consumers and businesses exponential amounts of time and money when implemented correctly, it can come with some initial startup challenges,” said Quinn. “The key to any accurate machine learning model is the input data. Not only does enough historical data need to exist for the model to derive an accurate representation but the data also needs to be accessible. If transaction information, consumer details, and purchase activities are dispersed among detached data sources, getting the data in a viable format for modeling could be difficult.”
This pipeline of quality data is important for any artificial intelligence project, and as fraud requires an ML model to recognize discrepancies that may only occur in less than 0.001% of transactions, it is even more critical that there is an accurate representation for the AI to follow.
Organizations also need to recognize inherent biases which may be embedded into historical data, and ensure that ML models are not making prejudiced assumptions. “When an ML algorithm makes erroneous assumptions, it can create a domino effect in which the system is consistently learning the wrong thing,” said Christina Luttrell of GBG Americas in a VentureBeat article.
Ultimately, machine learning is required in today’s world, where an organization may complete several millions transactions in one day. The focus on data engineers should be on ensuring that the base model being deployed is as accurate and unbiased as possible, to avoid further issues down the road.