Businesses today increasingly rely on IT systems to manage and store their data. The role of technology in making business processes advanced and simpler is undeniable. On the contrary, technology is a significant factor in making a company vulnerable to hackers and fraudsters. Hence, it is essential for modern businesses to have an appropriate fraud analytics mechanism which would help them to detect illegal practices at the early stage and take precautionary measures to curb the same. If a company fails to employ appropriate fraud detection techniques, then it might result in substantial irreversible losses once the company’s data is hacked. So, what is fraud analytics? Fraud analytics is the combination of analytic technology and fraud detection techniques with human interaction which will help companies to detect the possible improper transactions like fraud or bribery either before or after the transactions are done.
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What are the Benefits of Fraud Analytics?
Given below are some of the key benefits that fraud analytics offer to companies:
Identify Human Patterns
Traditionally, businesses often fail to identify irregular or unusual patterns of activities that might result in fraud. With the help of fraud analytics companies can identify new patterns, trends, and scenarios that influence fraudulent practices.
Enhance Existing Methods
Fraud detection techniques add up to the enterprises’ existing efforts to bring you more improved results rather than completely replacing the traditional rules-based methods.
Combine Data
Fraud analytics plays a vital role in the integration of data. It combines data from various sources and other public records that can be integrated into a single model.
Deriving Value from Unstructured Data
Data warehouses are the central repositories that store the critical business data. In most cases, unstructured data results in fraudulent activities. Advanced fraud detection techniques facilitate in reviewing the unstructured data and preventing fraud from taking place.
Enhance Performance
Fraud analytics helps an organization to rightly identify what strategies best suit their business operations and what do not. In this manner, it allows companies to choose the right strategy and improve their performance.
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Steps in Implementing Analytics for Fraud Detection
Though several companies use different fraud detection techniques and tools to identify fraud, it is essential to have a more dependable framework to make the fraud detection process more successful. Below we have enlisted a few steps to implement fraud analytics successfully:
- SWOT – To ensure that the fraud detection techniques match with the company’s strengths and weaknesses a SWOT analysis is essential.
- Build a dedicated team for fraud management – Companies must employ a dedicated team with proper workflow and adequate fraud detecting and reporting system to find and prevent frauds in the organization at the right time.
- Build or buy option – Companies must decide if it is more feasible for them to build a fraud analytics system or buy one.
- Clean data – Integrate the company’s databases and remove all unwanted data elements from them.
- Define relevant business rules – Companies should formulate business rules after doing research that helps them identify different types of fraud they are vulnerable to and develop a robust fraud detection solution to suit their requirements.
- Set the threshold – In the next step, companies should provide boundary values for different anomalies. They must ensure that the boundaries are not set too high or too low. A right threshold ensures that frauds do not slip through and resources are not wasted.
- Forward-looking analytics system – Companies should continuously be on the lookout for any additional sources of data and should integrate them with the current fraud detection program. This will facilitate to eradicate any new frauds that might develop in the future.
