- Email: admin@sbwteamllc.com
Fraud detection implies the identification of expected or actual fraud in an organization. It needs a proper process and system to detect frauds earlier on and before such occurrences using either reactive and proactive methods or automated/ manual fraud prevention and detection analytics in its anti-fraud strategy.
Fraud detection and prevention analytics have a huge importance in subverting and controlling frauds, especially when the internal control systems handling data analytics may be prone to control weaknesses calling for every transaction to be tested and controlled in fraud detection analytics. Besides, it provides for continuous improvement, standardization and process control over transactions.
Fraud detection analytics is the ultimate fusion of a combination of fraud detection techniques and analytics techniques in superior analytic technology, which along with human intervention, makes it possible to detect bribery, frauds and improper transactions as soon as possible.
Rules-based methods and legacy anomaly detection techniques were used to prevent and detect fraud by several organizations like insurance firms, banks and other organizations. The addition of fraud detection analytics and security algorithms and improved technology allows fraud analytics to use improved fraud detection techniques to prevent frauds, flag down fraudulent transactions and provide organizations with secure systems.
Some of the fraud analytics benefits are that it reduces costs and exposure to frauds, uses organizational controls to secure the system, helps find fraud-vulnerable employees, gains external and internal customer trust and confidence and improves organizational security and performance. Some of what fraud detection analytics can do is identify patterns of fraudulent transactions, enhance existing security measures, integrate all organisation databases, harness raw data and use unstructured data to improve organizational processes and efficiency.
The 5 fraud detection analytics methods used frequently are given below:
1. A sampling of processes may be mandatory for fraud detection. Its localized data can help prevent frauds by early detection in that particular area. However, such limited approaches can fail and need all transactions to be tested for fraud-detection.
2. The ad-hoc analysis uses a testing hypothesis to find and explore frauds.
3. Competitive or Repetitive Analysis has scripts that use Big Data to identify fraud over a specified time period. Running such analysis daily helps the organizational safety and efficiency scores of fraud detection.
4. Analytic techniques help identify abnormal frauds using statistical parameters above the standard deviation average values, check both low and high-value transactions and are useful in geographical area data classifications.
5. Benford’s law can be used on fraudulent data to indicate a non-uniform distribution of smaller digits and aid testing numbers and points that are suspect as they appear more frequently.
Some data-mining tools and methods used for fraud detection analytics are
a. Sounds-like method for identifying valid names of company employees.
b. Data Matching with another data set.
c. Duplicates weed out errors and identify duplication of transactions.
d. Gaps in sequential data being missing.
The fraud detection analytics program critical steps of implementation are:
1. Profile creation of all kinds, types and areas where fraud has happened and is expected to occur.
2. Measure and prioritize the organization’s risks of fraud exposure.
3. Use Ad-hoc methods of testing to find the fraud indicators of organizational processes.
4. Make risk-assessment programs frequently to prioritize areas of fraud prevention.
5. Communicate and monitor fraud and risk management at all levels of the organization.
6. Post a fraud, troubleshoot the causes and fix broken loopholes and controls.
7. Assign duties and repeatedly implement the programs.