Frauds online: a growing threat for many industries
The Banking and eCommerce industries are facing a great challenge since online payments are more and more rising: email phishing, financial fraud, identity theft, document forgery, and false accounts are just a few of the criminal attacks frauders are carrying out online.
According to McKinsey, card frauds have enormously grown in the past few years, Worldwide losses have almost doubled in ten years, and the number is expected to reach almost $44 billion by 2025.
The main challenge is that this threat is so pervasive it is very difficult to check every transaction in order to prevent fraudulent behavior: the amount of data involved is too huge to be manually managed, and that’s where AI and Machine Learning come to our aid.
How Artificial Intelligence and Machine Learning work
Machine learning is an analytic approach where a machine learns specific patterns in datasets without any human assistance, while AI refers to the use of particular types of analytics to complete tasks.
We could say machine learning is the method we use to create self -improving analytic models, while AI is the application of those models.
Thanks to Artificial Intelligence, Machine Learning perfectly fits the task of preventing frauds: for it is fast, scalable, efficient and accurate.
Last but not least: if crime doesn’t rest, nor does a machine, since AI is available 24/7 to help prevent frauds online.
How AI can help company prevent fraud behaviors
AI is able to analyze huge numbers of transactions in order to uncover fraud patterns, and eventually use them to detect fraud in real-time and even prevent it.
When fraud is suspected, AI models may be used to reject transactions altogether or flag them with specific cause codes for further investigation, as well as rate the likelihood of fraud, accelerating human investigations and allowing investigators to focus their efforts on the most likely cases.
In turn, AI also learns from investigators after they have evaluated questionable transactions, reinforcing its model’s knowledge and focusing on trends that actually lead to fraud.
Fraud Detection Machine Learning Algorithms usually uses Logistic Regression, a supervised learning technique providing a categorical output – either ‘fraud’ or ‘non-fraud’ – but since false positives require costly manual investigations, the most AI successful solutions are those that involve different techniques, such as:
- Data mining: to extract relevant data from a larger set of raw data, exploring and analyzing large blocks of information to find out meaningful patterns and trends.
- Neural networks: flexible computing systems applied to complex pattern recognition and prediction problems, clustering and forecasting behaviors.
- Pattern recognition: automated recognition of regularities in datasets through the use of computer algorithms, which are eventually classified into different categories.
AI can also help companies create purchase profiles, referring to the purchase characteristics and tendencies of a person or company when it comes to their transactions. This makes it easier to create predictions about their behaviors, timely spotting unusual and suspicious conduct.
FinScience has developed proprietary machine learning algorithms capable of collecting and managing a huge amount of data, find anomalies and monitor specific data, spot trends before they are trending, analyze topics or phenomena through Knowledge Graphs All this turned into an all-in solution to prevent frauds, where relevant information are provided through and intuitive dashboard.
