Polish Companies Urged to Boost Fraud Detection Methods

Introduction

Warsaw - As businesses in Poland continue to face the threat of fraud, a comprehensive approach to detecting fraudulent activities has become increasingly important.

The Importance of Identifying Potential Fraud

According to Marcin Zaboj, Manager at KPMG’s Actuarial Services in Poland and CEE, identifying potential fraud requires a thorough analysis of economic aspects, including the estimated amount recovered and the cost of verifying the fraud. A comprehensive approach is necessary to stay ahead of fraudulent activities.

Benford’s Law: A Key Method for Identifying Fraud

One of the key methods used by KPMG’s actuarial team is Benford’s Law, which describes the frequency of the distribution of the first digit in many real data sets. This law can be applied to identify suspicious or manipulated data, particularly in areas such as state aid analysis where there is a risk of data manipulation.

The Origins of Benford’s Law

Benford’s Law was developed by physicist Frank Benford, who found that certain digits occur more frequently than others when analyzing natural populations. By applying this law, fraud analysts can detect irregularities and potential manipulation of numbers.

A Comprehensive Approach to Detecting Fraud

However, even if the data set complies with Benford’s Law, there is still a risk of fraud. Therefore, KPMG’s approach to detecting fraudulent activities involves a comprehensive process that includes:

Descriptive analysis: defining what fraud means for the organization
Customer segmentation: identifying high-risk customers
Community analysis: recognizing patterns and trends in fraudulent behavior
Predictive analysis: using machine learning methods to identify fraud patterns

The Process of Detecting Fraud

The process begins by defining what fraud means for the organization, followed by the use of machine learning methods to identify fraud patterns based on historical data collected within the organization and external data from a wider context. Advanced analytics techniques such as:

Linear regression
Logistic regression
Decision trees
Neural networks
Multi-class classification

are used to build predictive models.

The search for fraud patterns also includes social and geographical contexts, recognizing that the propensity to cheat can be influenced by the social environment or geographical area. The approach helps draw attention to the fact that fraud often depends on the simultaneous occurrence of many factors.

Assessing the Quality of the Model

To assess the quality of the model, KPMG determines the prediction performance of the analytical model by:

Deciding on the partitioning of the dataset
Using metrics to evaluate performance
Testing for stability and calibration, taking into account aspects specific to the organization and current legislation such as the General Data Protection Regulation.

Conclusion

In today’s business environment, advanced analytics play a key role in fraud management. Every organization should identify areas at risk of fraud and implement comprehensive fraud detection and verification processes to effectively respond to fraud challenges. The process implemented by KPMG considers every aspect of the organization at risk of fraud, including specifics of the business.