EngEd Community

Section’s Engineering Education (EngEd) Program fosters a community of university students in Computer Science related fields of study to research and share topics that are relevant to engineers in the modern technology landscape. You can find more information and program guidelines in the GitHub repository. If you're currently enrolled in a Computer Science related field of study and are interested in participating in the program, please complete this form .

The Basics of Fraud Detection Analytics

December 5, 2021

Technology advancements have come with the risk of internet-related fraud. Fraud in organizations has led to the loss of revenue, which has increased the demand for fraud detection analytics.

Fraud detection analytics enable organizations to detect fraudulent activities early enough to prevent further occurrence of such activities or reduce the associated loss. This article will provide an overview of fraud detection analytics and explain why it is important in the tech industry. It will also take you through the fraud analytics methods and give you some tips for building a reliable fraud detection solution.

Overview of fraud detection analytics

Understanding the concept of fraud detection analytics requires knowledge of the definition of the terms fraud and fraud detection. Fraud is a crime or deceptive action done by a criminal to get unlawful gain or unlawful access to information and assets.

Fraud detection is the process of identifying this form of deceptive action. It can be done before fraud occurs, during the process of fraud, or after the fraud has taken place. Fraud detection analytics refers to a combination of techniques of fraud detection and data analytics that are employed to detect and prevent the occurrence of fraud. Some of the data analytics techniques that are used in fraud detection include data mining, clustering analysis, data pre-processing, and data matching.

Fraud detection analysis relies on data to identify the occurrence of fraud. For example, data pre-processing enhances the detection of missing data in a dataset. Missing data may show that there is a possibility of fraud.

Data matching enhances the comparison of two datasets to detect an abnormality. If there are significant differences, then analysts can conduct further investigations to detect fraud. Data mining allows the security team to cluster and segment data to identify whether the dataset shows patterns that correspond to fraud.

Why fraud detection analytics?

The following are the reasons why fraud detection analytics is important in organizations:

  • Reduced fraud exposure: With fraud detection analytics, the system seals any loopholes for conducting fraudulent activities. Fraudsters have limited opportunities for conducting fraud, which reduces fraud exposure.
  • Reliable fraud detection: Fraud detection analytics offers a reliable way of detecting fraudulent activity even before damage has been caused. This is in the case of early-detection systems. These systems can identify any attempt to undertake a fraudulent activity. This enhances control and security in the organization.
  • Increased customer trust: Fraud detection analytics ensures that the system of the organization is safe. When customers experience little or no security issues, they develop trust. Increased customer trust contributes towards customer loyalty, which is important for an organization’s growth.
  • Makes use of unstructured data: Many fraudsters conduct fraudulent actions when the data is unstructured. Fraud detection analytics is capable of reviewing unstructured data to identify and prevent fraudulent activities.
  • Supports data integration: This system collects data from diverse sources and combines it, which enhances the integration of data in the organization.
  • Detects hidden patterns: Some of the traditional techniques of identifying fraud may fail to detect hidden patterns. Fraud detection analytics is superior to these techniques because it can identify hidden trends, scenarios, and patterns.
  • Improved organizational performance: Fraud detection analytics minimizes fraudulent activities, which significantly reduces the loss of revenue as a result of fraud. Organizations achieve huge financial gains as a result of fraud analytics. These systems enhance efficiency and improve processes in financial transactions in organizations.

Fraud analytics process

The detection of fraud requires a systematic and strategic approach. Organizations that want to detect and prevent fraud can follow the following processes:

  1. Risk assessment: Perform a risk assessment of the probable frauds that may take place in the organization. Identify how these frauds may affect various parties related to the organization (e.g. employees, shareholders, creditors, suppliers, etc.) and the overall performance of the organization. It is also important to put these risks into different categories.
  2. Investing in reliable fraud detection software: After assessing the various risks associated with fraud, you should invest in reliable fraud detection software that offers security from the assessed fraud risks. Software that consists of analytic technology will provide superior features that identify hidden patterns.
  3. Communication with employees: All employees in the organization need to be aware that there are fraud analytics systems that monitor their activities. This helps in preventing fraudulent activities. This is because employees are aware that they will be caught if they engage in fraudulent activities.
  4. Testing data: Fraud detection analytics are then used to test data. This helps in identifying whether there are fraudulent activities in the organization. In the case of financial institutions, transactional data can be tested to detect any instances of fraud. When testing data, organizations should test the entire data and avoid testing a sample of the population. Using sample data may fail to uncover small anomalies in the entire dataset, which may cause significant losses to the organization.
  5. Continuous auditing: Continuous auditing can be done to test whether the controls are working effectively. The system should be monitored to identify any anomaly activities that are associated with the fraud.
  6. Further investigation and communication with management: If any form of fraud is detected, the organization conducts a further investigation to gain more information such as the parties involved and the loss realized as a result of the fraud. All the information collected should be communicated with the management.
  7. Amend anomalies in the control system: The IT team should identify any loopholes in the control system and fix them to improve the level of security.
  8. Increase the scope of fraud risk: After monitoring the system for some time, the management can notice that the fraud risk could be higher than the one described in the first process. In this case, you can consider increasing the scope of fraud risk to establish whether some fraud risks were ignored. The IT security team should also check whether the fraud analytics were effective in identifying and preventing activities that are categorized as fraudulent.

Fraud analytics methods

Analytics method

This method helps in establishing anomalies in a given dataset to detect fraud. It may involve statistical calculations to establish deviations from the mean, classification of data to identify anomaly patterns, or calculation of the maximum and minimum values to establish discrepancies in the data.


This is where a section of the entire dataset (population) is tested to establish whether there is fraud. The advantage of this is that it takes a short time to detect fraud. However, the untested data may consist of anomalies that may not be detected. This is the main reason why it is advisable to test all transactions to avoid ignoring small anomalies.

Continuous monitoring

This method involves developing scripts that are swifted through huge data volumes to establish whether there is fraud. In this method, fraudulent activities are identified as they occur. The continuous analysis method of fraud detection is efficient because all transactions are tested. If there is any anomaly detected in the transactions, the system issues periodic notifications.


This method involves identifying fraudulent processes using hypotheses. A hypothesis is a statement or assumption that needs to be tested. When transactions are conducted, a hypothesis can be formulated based on the results of the initial test. The nature of the results will determine whether a further investigation will be done.

Tips for building a reliable fraud detection framework

The following tips can help your organization build a reliable fraud detection framework:

1. Conduct SWOT analysis

SWOT is an abbreviation for strengths, weaknesses, opportunities, and threats. Conduct a SWOT analysis to establish the strengths of the organization as well as its weaknesses. This will enable the organization to devise an appropriate fraud detection system.

2. Select an effective fraud-detection team

Form a well-trained and skilled fraud-detection team. The most loyal and trusted employees should be selected to join this team. When any fraud is detected, the team should communicate with the management on the effective ways of preventing further occurrence.

3. Set business rules

There should be well-defined business rules that guide all employees in terms of operation procedures, transactions, and fraud. Adhering to these rules will play a significant role in reducing the level of fraud in the organization.

4. Data cleaning and integration

Unstructured data should be cleaned to remove redundancies and irrelevant information. Data collected from diverse sources can be integrated to increase speed and efficiency in fraud detection.

5. Set a reasonable threshold for fraud detection

Setting a threshold for fraud detection will help everyone in the organization to know when a scenario or event can be termed as ‘fraudulent’.

6. Utilize predictive models

Predictive models help in developing resourceful predictions that generate insights into the probability of a fraudulent event occurring. These models employ data mining techniques to achieve this purpose.

7. Use social network analysis (SNA)

You can enhance fraud detection in your organization by using social network analysis. This system enables you to evaluate the connection between various entities, which aids in the investigation of fraud.

Use cases of fraud analytics

Insurance industry

Fraudulent activities in the insurance industry lead to significant losses in revenue among insurance companies. Fraud analytics in this industry helps in the identification of accidents that have been stage-managed. They also help in investigating the embezzlement of resources in companies.

The evidence collected helps in formulating stiffer policies that prevent further fraud. Fraud detection in insurance firms is done using three techniques: social network analysis (SNA), predictive analytics, and customer relationship management (CRM).

Financial sector

Several transactions are done in the financial sector daily. These transactions are recorded and integrated with fraud analytics systems to help in identifying and preventing fraud. The dataset is used to show patterns and identify any behavior that does not correlate with normal business practices. Financial institutions use this technique to identify anomalies that may be categorized as fraud.


Fraud detection analytics entails the use of data analytics and other fraud detection techniques to identify or prevent the occurrence of fraud. This phenomenon is important in organizations because it reduces exposure to deceptive actions and enhances improved organizational performance.

Some of the industries that benefit from fraud detection analytics include finance and insurance. Organizations should choose fraud detection methods that are economical and convenient to enhance efficiency in fraud detection. The suggested tips for designing a reliable fraud detection framework can help in optimizing fraud detection in organizations.

Happy learning!

Peer Review Contributions by: Onesmus Mbaabu