Getting to Know About the Fraud Detection System and its Types
The rise of fraud in recent years can make you worry when running a business. The reason is, fraudulent actions can be carried out by consumers who regularly buy products or use your services. However, you don’t need to worry because a particular system known as a fraud detection system has been invented to detect all forms of fraud in financial transactions.
But before using it, you need to know the definition of a fraud detection system before implementing it in business. Here is our full explanation of how essential this system is for your business.
What is Fraud Detection System?
The fraud detection system (FDS) is a system specifically designed to detect fraud or fraud that occurs in various business activities or financial transactions. This system can be used in various fields involving financial transactions such as banking, insurance, credit cards, e-commerce, and so on.
This system uses various methods to detect fraud such as statistical analysis, machine learning, or rule-based algorithms. This system can be used to analyze transaction data and look for suspicious patterns ranging from transactions made from unusual geographic locations to transactions made with amounts of money that do not match the customer’s profile.
You can integrate this detection system with other systems such as the Anti Money Laundering (AML) system and the Know Your Customer (KYC) system. All of these systems can be integrated to create a singular system that can detect fraud in real time and accurately and provide solutions to address it.
Types of FDS
FDS is designed with various types of technologies that lead to the existence of several different types of FDS depending on the technology. Despite these differences, all of these types of systems are designed to detect fraudulent acts that can also come in various ways. Several types of detection systems are commonly used, such as:
1. Anomaly Detection
This system relies on statistical analysis to detect various forms of anomalies from normal transaction data. Here, anomaly detection will detect various forms of transactions that are not usually carried out by the account owner. Examples of detectable transaction anomalies are transactions from unusual geographic locations and transactions with large amounts of money that do not match the customer profile.
2. Supervised Learning
Next, there is the supervised learning model that uses machine learning supplemented with historical transaction data that has been classified as fraud or not. This system can be used to identify similar patterns of fraudulent transactions at any given time.
Apart from supervised learning, there is also the unsupervised learning model not equipped with historical data like the system above. The advantage of this system is the ability to find unknown patterns from all past transactions.
3. Hybrid System
The hybrid system is the combination of several types of FDS systems. Even though it’s costlier than the other types, the hybrid system can dramatically increase accuracy in detecting all frauds in any financial transaction.
4. Rule-based System
The rule-based system is an FDS system that works using predetermined rules to detect fraud. You can design your own rules so that the way this system works is in accordance with the company’s standard operating procedures (SOP). When detecting fraud, the system will immediately match the fraud with the rules that have been made so that the fraud can be addressed immediately.
5. Behavioral Analytics
Behavioral analytics is a system that works by collecting data from transactions and customer behavior. If there is an unusual behavior pattern that does not match the customer’s profile, the system will immediately analyze it and block it if it has been verified as a fraudulent act.
6. Real-time Monitoring
As the term implies, real-time monitoring performs live transaction monitoring. This system can detect fraud as soon as possible after a suspicious transaction occurs.
7. Biometrics-based System
This fraud detection system uses biometric recognition technology such as face recognition, irises, or fingerprints to verify customer identity before making a transaction. A customer’s iris and fingerprint were chosen as verification methods because of their foolproof nature and impossibility to be forged.
8. Geolocation-based System
This geolocation-based fraud detection system utilizes the physical location of the device used to identify transactions made from unusual locations. This system is often used to detect the possibility that the account owner’s account has been hacked by an external party.
9. Machine-learning System
The last FDS system on this list is a machine learning system that relies on algorithms and artificial intelligence (AI) in its system. This AI can identify suspicious transaction patterns based on past transaction data made by the account owner.
Examples of the Implementation of FDS
Many companies have implemented various types of FDS in recent years. One of the factors behind installing a fraud detection system in a company is the increasing number of digital financial transactions. Even though these digital transactions are undoubtedly sophisticated, digital financial transactions have the potential to cause fraud that can harm the account owner or your company.
One company that is now frequently using a fraud detection system is an online transportation company which has become one of the favorite choices of Indonesians in recent years. The application is equipped with a fraud detection system with the latest AI technology to detect fake orders. If detected, the system will send a message to partner drivers not to take the order due to potential losses.
Therefore, it’s a no-brainer if your company also needs a reliable FDS. Now, you won’t have any trouble finding the best FDS service provider in Indonesia, and that’s AdIns! AdIns has developed the Liveness Detection application that can detect all kinds of fraud committed through spoofing.
This application uses an algorithm based on deep learning that can assess videos based on certain parameters such as brightness, shadows, micro-motions, and pulses. Potential fraud committed through spoofing can also be overcome by using this application.
Interested? Just AdIns!