The Why & How of Combating Financial Risks with Fraud Data Analytics | Quantzig
Fraud detection and prevention is a growing challenge for the banking and financial industry. Though large-scale fraudulent transactions are rare, it’s crucial to note that a fraudulent activity even of a small scale can result in million-dollar losses. With the rise in technological advancements, traditional fraud management methods are now on the verge of becoming […]
Fraud detection and prevention is a growing challenge for the banking and financial industry. Though large-scale fraudulent transactions are rare, it’s crucial to note that a fraudulent activity even of a small scale can result in million-dollar losses. With the rise in technological advancements, traditional fraud management methods are now on the verge of becoming obsolete. But the good news is, innovation in fraud data analytics has uncovered emerging patterns in fraud detection.
Fraud analytics combines advanced data analytics, machine learning and artificial intelligence to detect fraudulent transactions before they occur. It revolves around gathering relevant information, analyzing the data, and identifying the discrepancies and anomalies in the extracted information. These fields are then converted into actionable insights for organizations to combat potential frauds and threats before they occur.
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How Fraud Data Analytics Helps in Fraud Detection & Prevention
Frauds are of different kinds and their effects vary from industry to industry. The sectors that deal with frequent fraudulent activities, use various techniques and fraud management analytics-based solutions to get to the root cause of the activity. What makes fraud management analysis crucial in today’s data intensive business environment is that it applies advanced analytics methodologies that can handle a massive chunk of data at once.
Fraud management tools are usually fine-tuned to be accustomed to what is normal within a set of information as per the organization’s rules for easy analysis and anomaly detection. Fraud management technology minimizes human intervention in the detection of fraud. It also helps businesses to track trends to predict fraud faster than the human brain and without any human interaction.
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Common Types of Banking Fraud
- Tax Fraud- Most taxpayers are usually worried about errors that might lead to an escalation while estimating taxes. On the contrary, few people engage in illegal activities to receive refunds on tax. Fraud analytics here plays a great role in assessing the reliability of individual taxpayers based on their transactions and stored data information.
- Credit card or banking fraud- Banks usually connect with their customers immediately when they come across suspicious activities. Today financial institutions are relying more on automated systems that leverage fraud analytics to detect fraud. To be more specific, fraud analytics and predictive analytics dashboards can be customized to send a notification to the concerned person when the system detects an illegal activity. As a result, it has now become easier for the banking sector to curb fraud.
Why is Fraud Data Analytics the Need of the Hour?
Fraudsters always leave a digital trail behind them. Hence organizations must leverage advanced technologies to utilize their internal data to identify fraud and potential chances of upcoming fraudulent activities. Fraud data analytics also helps in constant improvement of the business and puts data to better use.
Benefits of Fraud Data Analytics
- Identify hidden patterns and potential threats: Fraud analytics can uncover new trends and fraudulent schemes that conventional approaches miss or fail to identify.
- Enhance existing efforts: Every organization does its best to stay away from frauds. Fraud analytics can always add an extra layer driven by data to your efforts.
- Obtain a unified set of data: Fraud analytics helps integrate data to provide a single source of truth.
- Improve performance: Organizations that leverage fraud data analytics can automate processes such as data aggregation, pattern analysis, and fraud detection, saving time and resources that are otherwise required to analyze information manually.
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