September 25, 2023
Last updated: October 13, 2023
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We all have been a victim of online fraud at some point in our lives. It has been on the rise ever since eCommerce giants stepped in and the onset of COVID-19 pushed it further. The substantial growth in the last few years has given rise to online fraud in proportion to this growth. Experts hope that by AI and machine learning in fraud detection, we will be able to tackle the issues.
Here is a visualization from Statista on the latest statistics on the value of eCommerce losses to online payment fraud worldwide from 2020 to 2023.
Cybercriminals are getting more and more creative every day and that’s a hard pill to swallow. Fraudulent activities are a serious problem for organizations as it affects them on multiple levels. Coming up with ideas to prevent it seems to be the need of the hour as the number of transactions is increasing every day.
Fraud detection is the process of identifying and preventing fraudulent activities. Although it has been in practice for a long time, out-of-the-box ideas from fraudsters make it more challenging and complex. As their methods keep evolving over time, this is where AI and machine learning in fraud detection come into play.
By implementing these technologies, computers will be able to learn from data and perform necessary tasks that would require human intelligence or interference otherwise. The data fed into the system will be helpful in reasoning, decision-making, and problem-solving.
If you still wondering how it will benefit us, here are some of the use cases:
But how to implement it into our existing financial system?
Here are some of the common techniques in which AI and machine learning technologies can be used in fraudulent detection.
When it comes to using AI and machine learning in fraud detection, there are multiple techniques. Experts will choose the right one depending on the nature and scope of the problem.
Imagine you have a bunch of provisions that are already labeled as either fruit or vegetable. You can use this data to teach a computer how to tell the difference between the two types of provisions. This is called supervised learning because you give the computer some guidance or supervision. With this technique, the system can use what it learned to predict fraudulent transactions, spam emails, inappropriate content, etc.
This technique can help you in catching fraudsters before they do more damage. You can use different factors or features to train the computer, such as how much money was involved, where and when the transaction happened, what device was used, and so on.
Now imagine you have a bunch of provisions that are not labeled at all. You don’t know if all of them are fruits or not. How can you use this data to detect this condition? Well, you can use unsupervised learning, which means letting the computer figure out things on its own, without any guidance or supervision. The computer can try to find patterns or groups in the data based on how similar or different the provisions are.
For example, it can cluster the provisions based on their features, patterns, etc. Then it can look for any feature that doesn’t fit into any of the clusters, or that is very different from the rest of their cluster. These could be signs of fraud, suspicious activity, unusual authentications, etc.
Let’s assume, you have a pool of provisions, and some of them are labeled as fruits and vegetables. But, would it be enough to train a good model?
You will need more data to get it to work properly. Also, you will have some provisions that are not labeled at all. It will be too many to analyze manually. How can you use this data to find patterns?
You can use semi-supervised learning, which means combining supervised and unsupervised learning as it deals with a mix of labeled and unlabeled data. The labeled data can be used to train a basic model that can classify transactions as fraudulent or non-fraudulent. Then you can use the unlabeled data to improve or update the model by adding new patterns or features that the model might have missed. This way, you can make the most of both types of data and get a better model.
Imagine you have a computer that can learn from its own actions and consequences by interacting with an environment. For example, you have a computer that can accept or reject transactions based on some rules or criteria. The computer then gets feedback from the environment, such as rewards or penalties, depending on whether its actions were right or wrong. The computer can use this feedback to learn how to make better decisions in the future. This is called reinforcement learning because the computer is reinforcing its behavior based on the outcomes.
Reinforcement learning can be used for fraud detection by creating an agent that can learn how to detect fraud by trying different scenarios or strategies and getting feedback from the environment. For example, an agent can learn how to set its fraud detection threshold or parameters based on the balance between false positives and false negatives.
The above illustration is the typical framing of a Reinforcement Learning (RL) scenario where an agent takes actions in an environment. It will be further interpreted as a reward and a representation of the state and will be fed back to the agent.
Fraudulent activity is a serious issue that affects many industries and sectors. It gets more challenging and complex giving rise to many new problems to deal with. AI and machine learning are powerful technologies that can help improve fraud detection by automating the process, analyzing large and complex datasets, and detecting anomalies and outliers. They will be able to adapt to changing trends and conditions and enhance the accuracy and efficiency of fraud detection.
Although they require careful consideration and attention to data quality, data privacy, model interpretability, and model robustness, it is the safest bet we have on our hands right now. With constant monitoring and evaluation, we can ensure their effectiveness and reliability.
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