Fraud Detection with Machine Learning and AI
We are living in an era where everything is digital and done via the internet. All our activities, whether shopping or banking, are digitized. The growing need to use the internet and online applications has increased cyber attacks and fraudulent activities. These activities are in different forms, including fake emails to steal your information or online payment fraud.
According to CyberCrime Magazine, the cost of cybercrimes is expected to reach $10.5 trillion by 2025. It has become imperative to stay vigilant and take necessary steps to ensure robust security against fraud. The question here is: how is it possible to detect and prevent fraud detection?
Several technologies are emerging to address this challenge, one of which is the use of ML and AI in fraud detection. In this article, we will explore how ML and AI are used in the detection and prevention of online fraud.
Use of ML and AI in Fraud Detection
Machine learning is a subset of artificial intelligence that has the ability to analyze large volumes of data. ML models are trained to identify patterns and detect fraudulent activities or any anomalies within the data. How is it possible?
Machine learning models are provided with a standard dataset of transactions. They learn from that historical data. Then, they monitor and detect any change in patterns and anomalies in the data and mark them as potential fraud. Let’s take a hypothetical scenario.
Suppose you are the owner of a healthcare organization. You want to prevent any fraudulent activities to maintain the integrity of the organization. How can you achieve it? Yes, you guessed it right.
Innovative AI/ML solutions will be a game-changer for you. They are trained on standard patterns of billing amounts and medication prescriptions for various conditions. They detect any unusual patterns in billing, duplicate bills, and mismatched treatment and mark them as potential fraud.
One of the most significant advantages of AI in fraud detection is its ability to self-learn. It allows models to continuously learn from the new data and improve their accuracy over time.
Benefits of using AI and ML for Fraud Detection
If you are a business owner looking for a solution to prevent fraud, you must go for an AI solution. But why are we suggesting AI technology? Here are some benefits of having an AI solution to your exposure:
1. Instant Detection and Prevention
Is it possible for a human to monitor transactions round-the-clock and deeply investigate every activity? No, it requires a hefty amount of resources and time to ensure security and prevent suspicious activities.
Gone are the days when weeks or even months were spent to detect fraud. AI can potentially analyze vast datasets of information within a few seconds, not humanly possible. It empowers organizations to monitor each transaction and mitigate fraud risks by giving them risk scores.
2. Improved Accuracy
Intelligent machines using AI and ML algorithms are trained on a huge dataset. It reduces the risks of any potential errors in decision-making. There’s a direct relation between data set and model prediction capabilities, which means the more training data there is, the better the predictions are.
It is near to impossible for ML models to make mistakes or overlook information like humans. It promotes a more secure environment and accurately predicts anomalies. What’s more, these intelligent models can continuously learn and improve their predictions over time. It significantly reduces the false positives.
3. Cost-Effectiveness
Are businesses still willing to invest heavily in hiring human agents to manually analyze data and prevent fraud? With the advancement in technology, it is becoming obvious that most humans will be replaced with a more efficient solution at place.
These human agents can be used more efficiently for other strategic tasks. AI in fraud detection not only cuts the costs of hiring individuals but also does weeks of work within minutes. Moreover, ML solutions are scalable and have less chance of failing, thus saving costs by preventing financial losses in fraud.
3 Top Use Cases of ML and AI in Fraud Detection
AI fraud detection has helped hundreds of organizations deal with fraudulent activities and save massive amounts of money. Here are some applications of ML and AI in fraud detection across various business domains:
1. Healthcare
The healthcare sector is facing fraud challenges due to vast amounts of data being generated. According to the NHCAA report, financial losses due to healthcare fraud are in the tens of billions of dollars each year.
AI solutions make it easier to conveniently manage and analyze vast amounts of data. This data can include insurance claims, billing, false prescriptions, etc. Moreover, AI/ML solutions look for patterns in the healthcare data and define specific criteria to mark some transactions or billings as fraud.
2. Ecommerce
E-commerce is another sector prone to fraud due to the massive increase in online transactions. AI fraud detection software or tools help store owners identify discrepancies in real-time. This enhances security and improves users’ shopping experience.
Smart ML models detect some transactions as anomalies and allow owners to investigate in detail. Moreover, they assign risk scores to transactions based on several crucial factors. Those with high risk scores need to undergo extra authentication.
3.Banking
Banking and financial institutions are another industrial application of AI and ML technologies. ML algorithms continuously monitor accounts and detect any unusual transactions.
AI in fraud detection assesses the risk levels of transactions depending on user behavior, transaction history, and external data sources. It helps ensure the highest security standard and allocate resources more effectively. Moreover, deep learning models help identify any email phishing for stealing your personal information.
Conclusion
Cyber attackers are using different ways to steal your information and commit fraud. It has become essential to stay vigilant to detect and prevent any scams from happening. You must stay a step ahead of the attackers. It is possible with the integration of AI/ML solutions in your operations, thus improving fraud detection and identifying anomalies in data. These solutions not only save cost but also your valuable time, information, and hard-earned organizational integrity.
