Life has evolved drastically in the last two decades with increasing emphasis on the digitization of virtually every sector. In many ways, this is a win for environmentalists that are worried about human’s increasing carbon footprint. But, businesses are facing challenges with cybersecurity at unprecedented levels because of their growing digital footprint. This is because no system is absolutely foolproof. Here we will explain how AI is used in fraud detection.
Fraudsters are also getting more sophisticated by twisting the opportunities presented by different platforms for their selfish gains. In PwC’s Global Economic Crime and Fraud Survey 2022, over 50% of the surveyed organizations revealed they have fallen prey to fraud in the last two years.
This was the highest in the last two decades. A quarter of the organizations that fell to fraud lost over $1 million. Sadly, fraudulent acts often pass undetected through the watchful eyes of quality control in different organizations.
Perhaps, it is time for organizations to consider deploying artificial intelligence (AI) for fraud detection to assist human agents in detecting what is happening beneath the surface. How?
Let’s start with How we can use AI in Fraud Detection:
Types of AI Used in Fraud Detection
Unlike robbery which often happens under the cover of the dark, most online frauds happen in plain sight. Humans may miss the breadcrumbs left behind by fraudsters through ignorance, fatigue, or oversight.
However, when properly trained, AI can detect these breadcrumbs and either stop or alert appropriate authorities so that the fraudulent activity is nipped in the bud.
While AI simply means machines that can learn or intelligence displayed by machines, not all aspects of AI are valuable for fraud detection. Below are the different types of artificial intelligence that can be deployed for fraud detection.
Machine learning is the training of a network of AI algorithms using historical data to recommend risk guidelines. The guidelines can then be set to allow or block certain actions like fraudulent transactions, identity theft, and suspicious login attempts.
One of the key benefits of machine learning is that it allows the system to learn and adapt its decisions based on supplied data without having to be reprogrammed all the time. Therefore, the accuracy of the algorithm’s rule suggestion will continually improve with every extra data it processes.
Natural Language Processing
Natural language processing (NLP) is the process of equipping computers with the ability to understand speech and text in a way similar to humans. This is usually a tricky process because it has to deal with myriads of unstructured data.
However, the unique ways people write and speak allow the computer to differentiate one individual from another which can come in handy in fraud detection like when a fraudster is trying to impersonate another person or forge their signature. NLP can detect the subtlety in the speech or text and block the action.
This is a type of machine learning called deep learning where computers are taught to process information in a manner that is comparable to the human brain. A neural network creates an adaptive system where the computer can learn from its previous errors leading to continuous improvement.
A neural network can be deployed in biometric protection like facial recognition and speech recognition. It can also learn patterns of behavior and detect changes in those patterns which can be crucial in identifying fraudulent activities. Most importantly, this process is fast and decisions can happen quickly.
Steps in Fraud Detection
Every fraud detection system needs to be carefully planned to combat a type of fraud. For example, a fraud detection system deployed to an e-commerce platform will most likely be equipped with the ability to detect or stop fraudulent transactions. However, it may not have the ability to detect identity theft.
Therefore, the first step in building a fraud detection system is risk assessment. Identifying the vulnerable areas of the organization helps to build an efficient system that will plug the loopholes. Once the vulnerabilities are identified, the system goes through the following steps.
When it comes to AI, especially in the aspect of machine learning, the size of the dataset is essential to the efficiency of the model. Researchers have discovered that the higher the dataset, the better the performance, and vice versa.
Therefore, a fraud detection system will need lots of data relating to risk assessment. For example, if the goal of the model is to lower transaction fraud, the system will need different forms of data like credit card types, product stock-keeping units (SKUs), transaction values, device type, IP data, and so on.
Data analysis in fraud protection usually involves the classification and segmentation of the provided data to discover associations and patterns of interest, like those linked to fraud. The findings are encoded as rules which can be of single or complex parameters. An example of a single parameter rule is when the system is instructed to block a user if the IP is N.
In most systems, the names of rules are usually highly descriptive for easy understanding. Also, the accuracy thresholds can be loosened or tightened to fine-tune triggering situations.
The result of the data analysis is compiled into a software program, that will be further trained and deployed to the system where they will work to detect fraudulent actions. AI models are usually the pillar to which advanced intelligence methodologies like augmented analytics and predictive analytics are anchored. For instance, Lifelock, a popular identity theft detection software solution, often applies machine learning models to its database of past fraudulent activities, which is one of the most extensive collections of such incidents globally. Many advanced identity theft detection services do, in fact, so you really should look at the other options, instead of Lifelock alone.
The model can use neural networks, machine learning, and natural language processing to determine patterns. Arguably the most fascinating feature of good AI models is their ability to mimic logical decision-making by relying on available information.
This involves a set of activities and steps used to determine if the AI is working as required, meets its design aims, and is useful to the end user. Therefore, testing is an important part of validation. The accuracy of the system is determined during testing. Feedback from tests is used to refine the model.
Advantages of AI in Fraud Detection
Today, AI is deployed in virtually all major online platforms. This is because they are faster and don’t suffer from fatigue like humans. Millions of data and transactions can be processed in seconds—a feat that may be difficult to achieve with humans.
The need to deploy AI in fraud detection became direr after Juniper Research estimated that losses to online payment fraud will likely exceed $200 billion by 2024. Apart from speed and efficiency, AI models are usually scalable. They can learn and evolve to always be ahead of fraudsters rather than playing catchup.
Challenges of AI in Fraud Detection
While the benefits of AI in fraud detection are glaring, a few challenges can hinder its widespread adoption, especially in smaller businesses. AI is still not immune to mistakes. Therefore, there can be instances of false positives where a legitimate action may be marked as fraud. Failure to detect this can jeopardize the accuracy of the entire system.
Also, an AI model is only useful when it has access to lots of data. In an area where there is a paucity of data, AI becomes harder to train. In many cases, AI can only detect if an action is true or false (legitimate or fraudulent). Therefore, psychologists may still be called in if further investigation is required.
AI systems are all around us and we encounter their power in fraud prevention in our daily lives. From Google flagging some emails as spam to being denied access to a building because of fingerprint mismatch, AI is extremely useful in fraud detection. With AI, thousands of data can be processed in real-time making it easier to detect fraudulent activities faster and stop them before any real harm is done.