The construction industry is significant in the economy of any country, being both lucrative and costly. This concomitantly makes the sector immensely important to protect and incredibly profitable. Unfortunately, it is regarded by both public opinion and independent research as one of the most corrupt industries, particularly in the public sphere. To battle these emerging cases, there is a pressing need for anti-fraud initiatives and monitoring systems. Currently, there is a lacuna in our understanding of fraudulence detection within the construction industry. To deliver a cost-effective and precise solution, the researchers opt to employ a hybrid genetic algorithm—artificial neural network algorithm to serve as a first-pass mechanism to filter public road projects en masse in order to isolate projects that have outlying costs with regards to their scale and scope. A hybrid genetic algorithm-artificial neural network was initialized for a regression-based cost estimation model and a classification-based fraud detection model. Synthetic data generation techniques were applied to generate a well-behaved dataset of 1000 tuples each of fraudulent and non-fraudulent data, validated by histogram analysis, pairwise mutual information comparison, and a two-sample Kolmogorov–Smirnov test. The cross-validation results yield an MSE of 0.057 for the cost estimation model and a 99.50% accuracy for the corruption detection model for the best network architecture.