What is Fraud Analytics?
Fraud analytics is an umbrella term that comprises numerous quantitative sciences like business intelligence and data analytics in order to better understand fraud. Furthermore, fraud analytics helps in developing effective fraud detection solutions through data science. Many organizations practice traditional anomaly detection methods for fraud prevention and detection. But unfortunately, they are not powerful and function under certain limits. When analytics is added to such traditional fraud prevention methods, it improves the capabilities of fraud prevention and gives a new dimension to the fraud prevention techniques. Additionally, fraud analytics empowers organizations to have proper control over every ongoing activity.
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Fraud analytics can prevent businesses from the cost of fraud which is equivalent to a financial iceberg, some of the direct losses are clearly visible but there is a huge mass of hidden harm that companies cannot see.
Why is Fraud Detection Important?
For an organization, fraud detection forms an integral part of the risk analysis process. It provides the ability to quickly filter out fraudulent sources that prevent an organization from any high-level risk in the future. Here are a few reasons why fraud detection is important:
- Decreases exposure to fraudulent activities
- Reduces costs associated with fraud
- Identifiesvulnerable employees at risk of fraud
- Improves organizational control
- Enhances the organizational result
- Boosts the confidence and trust of the shareholders
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Benefits of Fraud Analytics
Helps in Identifying Hidden Patterns
Fraud analytics along with machine learning has the potential to help organizations in identifying new trends, patterns, and scenarios under which frauds take place but traditional approaches to fraud prevention miss such things.
Aids in Integrating Data
Fraud analytics plays a crucial role in integrating data. It combines data from public records and multiple sources that can be integrated into a model.
Improves Existing Efforts
Fraud analytics instead of replacing traditional rule-based methods just adds up to existing efforts of the organization to provide improved results.
Harnesses Unstructured Data
Unstructured datasets are the most complex thing for companies to deal with. Fraud analytics helps in deriving the best value from such unstructured datasets. The Data warehouse of any organization consists mostly of structured data, but unstructured data is the source where more fraudulent activities take place. This is where fraud analytics plays an important role in reviewing the unstructured data and preventing fraud.
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Methods of Fraud Analytics
With the increasing importance of fraud analytics, organizations too have realized the important role it plays in the process of growth. But many of these organizations are opting for expensive fraud detection solutions that do not match with the strengths and weaknesses of the company. Therefore, it is essential for organizations to do SWOT analysis before starting with the fraud detection process in order to make it work to the fullest.
Sampling is an effective method of fraud detection that involves a lot of data. However, it has its own disadvantages too. Sampling may not be able to control the fraudulent activities completely as it takes only a specific population into consideration.
Ad-Hoc is nothing but identifying fraudulent activities by the means of a hypothesis. It offers the ability to explore and test the transactions in order to detect the fraud. Also, you can conduct a hypothesis to test and find out if there is any abnormal activity occurring and then you can investigate the same.
Repetitive or Continuous Analysis
Repetitive analysis generally refers to creating and setting up scripts to run against a huge volume of data to determine the frauds as they occur over a period of time. This method of fraud analytics helps in running the script every day to go through all the transactions. Furthermore, this provides periodic notification regarding the frauds. This method can help in enhancing the overall efficiency and consistency of your fraud detection processes.