Abstract Financial fraud is an umbrella term including a vast number of illegal activities. These activities involve a significant fraction of the global economy. Traditional investigation techniques are labour-intensive and cannot scale to match the size of the issue. Machine learning has provided effective tools which deliver high accuracy in identifying transactions that could be involved in fraudulent activities. In this paper, we point out that the state-of-the-art in financial fraud detection has been applied to the unrealistic scenario of an omniscient centralized global authority which has access to all bank transactions globally. We propose a more realistic evaluation scenario, one made of two steps: first, the bank flags its own transactions using exclusively information it possesses; then only flagged transactions from all banks are analysed by the governmental authority for potential prosecution. We find that, in such a realistic scenario, the effectiveness of the state-of-the-art method for financial fraud detection decreases. Moreover, we show that in this decentralized scenario, it pays off to use simpler methods than the state-of-the-art, depending on the specific objective function the system wants to ensure.
Gige et al. (Wed,) studied this question.