Fraud detection finds and stops fraudulent activities in finance, healthcare, e-commerce, and other fields. It uses technology, statistical analysis, and human knowledge to look for unusual or suspicious patterns in data, transactions, or behaviors that could be signs of fraud. If you’re in the business and financial sectors, you need to know how fraud detection tools work.
In this article, we’ll go over what fraud analysis is, how fraud detection works and why it’s an essential investment for businesses and financial institutions.
What is Fraud Detection?
Fraud detection is the process of identifying and preventing fraudulent activities within financial systems. Businesses incorporate fraud detection measures into their policies, security systems, and websites to determine unauthorized activities. Fraud detection systems also verify the authenticity of customers and their transactions.
Learning how fraud detection works requires a specific set of analyses and procedures. It uses machine learning and data analytics to spot different types of online fraud. Financial institutions and businesses can use this technology to protect themselves and their customers from future cybercrimes.
In the Philippines, the Bangko Sentral ng Pilipinas (BSP) already requires real-time, automated fraud monitoring and detection for financial institutions under its jurisdiction. It’s one of the central bank’s cyber fraud prevention measures.
This came from Circular No. 1140, an Information Technology Risk Management Framework amendment. The mandate required financial institutions to integrate their automated fraud monitoring and anti-money laundering systems to prevent financial crime. It also encouraged financial institutions to use interactive consumer education platforms.
The constant advancements in technology also contribute to the rising numbers of online fraud. Due to the development of Crimeware-as-a-Service (CaaS), committing digital fraud is now easier than ever. With the CaaS model, even those with no technical background can commit cybercrime because spyware and fraud tools are accessible everywhere. Some examples of malicious software as a service are spyware distribution, phishing toolkits, and money mule services. This type of fraudulent technology is why learning how fraud detection and its tools work is crucial.
If fraud detection is what finds and stops fraudulent actions, then what is fraud analytics?
Fraud analytics entails employing advanced data analysis techniques to proactively combat online financial fraud. It enables financial institutions to anticipate and identify potentially fraudulent behavior, facilitating swift, real-time detection and mitigation of fraudulent activities.
On the other hand, real-time fraud detection looks for and reports any suspicious activity immediately. It typically involves rule-based systems, machine learning algorithms, or a combination of both to analyze incoming data and identify suspicious patterns or behaviors that indicate fraud. Fraud analytics is a broader approach, whereas fraud detection is the specific process of detecting and flagging fraudulent activities as they occur.
How Does Fraud Detection Work?
Detecting fraud must be dynamic to keep up with fraudsters’ constantly shifting methods. Fraud detectors use machine learning, artificial intelligence (AI), statistical analysis, and behavioral monitoring to identify fraud patterns. Precursors to fraud allow the system to halt fraudulent activity before significant loss occurs.
With these, fraud detection systems can do the following:
- Start an automated response to either request additional verification or strengthen authentication security
- Shut down and block the transaction entirely, depending on the risk
Typically, fraud systems, like e-commerce fraud detection tools, operate in the background. This implies that unless the system flags a consumer’s action or behavior for further investigation or authorization, their experience will be unaffected.
Let’s look at the various fraud detection learning models to further understand what fraud analysis is and how it works.
Fraud Detection Learning Models
Systems use several learning techniques, including supervised and unsupervised classifications, to make fraud detection tools work. Here’s how they vary from each other:
Supervised Learning
AI-powered supervised learning techniques can detect fraud based on previously recorded patterns. Once a fraud pattern is identified, human operators manually enter fraud cases into the system. The AI will sort through the thousands of recorded fraud types and match fraudulent behaviors. Machine learning can then detect when these patterns repeat or reappear.
Researchers can quickly test and debug the fraud detection system within the straightforward framework supervised learning provides. Here are some applications of supervised learning data analysis in fraud detection:
- Bayesian Learning Neural Networks
Systems use this supervised learning to distinguish between legitimate and fraudulent actions by calculating the likelihood of an event occurring given specific data points. For instance, it’s valuable in assessing the validity of auto claims by determining the probability of their authenticity based on imputed datasets. It also effectively identifies credit card, medical insurance, and telecommunications fraud. - Supervised neural networks
These adaptable networks produce a fraud risk score based on expected behavior patterns. A fraud detection tool like this works when a user’s behavior is flagged and given a risk score when it deviates from a set of guidelines. - Link Analysis
This form of supervised learning connects individuals through social networking and record matching. Link analysis can uncover additional fraudulent activity using previously identified fraudulent users and transactions. - Hybrid Knowledge / Statistical-Based Systems
These systems combine knowledge-based techniques with statistical analysis by mining extensive customer transaction databases. They detect anomalies and identify potentially fraudulent behavior by integrating domain expertise and statistical power.
Unsupervised Learning
Unsupervised classification is a method that groups unlabeled data into clusters based on their inherent relationships, often revealing hidden connections and early signs of fraudulent activities. This approach eliminates the need for time-consuming and costly data labeling. However, the algorithm will likely learn irrelevant patterns that don’t aid fraud detection.
By employing various analytic models and statistical analyses, predictors of fraud can be identified based on historical criminal actions. This information allows fraud prevention systems to assign risk scores to specific behaviors.
Unlike supervised learning, unsupervised learning focuses on detecting new fraud patterns and identifying outliers that deviate from known fraudulent behaviors. This adaptive approach enables the AI to continually evolve and uncover novel types of fraud as malicious actors constantly refine their tactics.
Human analysts can combine unsupervised learning with supervised learning techniques to create more comprehensive fraud detection tools that work. For instance, link analysis can establish connections between known threat actors and other potential fraudsters. Additionally, link discovery can further identify groups and behaviors that initially appear unrelated to fraudulent activities.
Machine learning can effectively flag suspicious behaviors or potential fraud cases for further investigation by human analysts.
Why Financial Institutions Should Invest in Fraud Detection and Analytics Tools
It’s one thing to study and understand how fraud detection tools work; investing in one is another.
Because the threat landscape is always shifting, staying on top of the latest best practices is essential for preventing financial setbacks. And one of the best practices is to invest in fraud analytics software. Banks must invest in anti-fraud measures because they protect their assets, reputation, and customer satisfaction.
Here are other reasons why financial institutions must invest in fraud detection tools:
Proactive Fraud Prevention
Financial institutions can recognize and stop fraudulent activities before they cause significant harm using fraud detection and analytics tools. These tools can detect patterns, anomalies, and suspicious transactions by analyzing massive amounts of real-time data, allowing timely intervention. Additionally, fraudsters continuously evolve their tactics, making it crucial for financial institutions to stay one step ahead. Advanced analytics tools use machine learning algorithms to adapt to new fraud patterns and identify new threats, providing proactive fraud defense.
Cost-Effective and Streamlined Operations
Implementing robust fraud detection tools can save financial institutions significant money. They won’t have to worry about financial losses, legal liabilities, or the costs of investigating and recovering from fraud. They can also streamline fraud prevention with tools that monitor and analyze massive amounts of data. This reduces manual labor, improves operational efficiency, and lets staff focus on higher-value tasks.
Consumer Confidence
Fraud can damage a bank’s reputation and customer trust. By identifying and avoiding fraud, institutions demonstrate their commitment to protecting customers’ assets and data, building trust and loyalty.
Aside from these, banks must carefully select their tools, processes, and fraud detection partners. Fraud detection tools work most effectively when you know how to find the best. Also, investing in good fraud detection and analytics tools that use alternative data is essential.
Alternative data provides additional sources of information beyond traditional data sets. This is what Finscore does when providing top-notch fraud detection services to its clients. Finscore offers alternative fraud detection analytics and solutions, such as geolocation and social circle fraud tools, to help banks onboard more clients with fewer hurdles. Many Philippine lending institutions have already reduced fraudulent loans and credit card applications using this technology. It assisted them in distinguishing between fraudulent and legitimate transactions by analyzing factors such as customers’ online activity and geolocations.
Easily Detect Fraud with Finscore
Strong fraud detection and analytics tools are now necessary, not a luxury. It must be a critical component of a financial institution’s security infrastructure. These resources can help them proactively prevent fraud, reduce losses, comply with regulations, and retain customers’ confidence. What’s more, it will put you ahead of the competition.
Ready to know more about how fraud detection works and how it can benefit you? Get in touch with us today, and we’ll be happy to help.
