Finding the kinds of fraud that could actually happen within the company is the next stage in the data analysis process after learning about the firm (Bao et al., 2022; Albrecht et al., 2006). In this step, which is a systematic fraud risk assessment, analysts and auditors take into account how the business’s operations, procedures, and control environment can present opportunities for wrongdoing. The goal is to predict real fraud schemes that demand critical examination, not to assume that fraud has already taken place.

Asset misappropriation, which involves the theft or improper use of organizational resources like money, inventories, or other assets, is one significant category (Jimenez Serrano et al., 2025). Fake vendors, payroll fraud, misuse of expense reimbursement, and inventory theft are examples of common scams. Due to their frequent occurrence and low individual value, these scams are especially appropriate for detection using pattern recognition and transaction-level data analysis.

Corruption, which includes conflicts of interest, bribery, and kickbacks, is another important category(Adomako et al., 2021; Afonso and de Sá Fortes Leitão Rodrigues, 2022). These schemes may be hidden by high expenses, frequent vendor selection, or unusual payment terms. They usually entail collusion between workers and outside parties, such as suppliers or contractors. Lastly, intentional manipulation of accounting records for the purpose of misleading users of financial information—for example, by increasing revenues or minimizing expenses—is known as financial statement fraud. Despite being less common, this kind of fraud frequently has a significant impact and requires rigorous risk assessment before using analytical techniques.

References:

  • Adomako S, Amankwah-Amoah J, Tarba SY, et al. (2021) Perceived corruption, business process digitization, and SMEs’ degree of internationalization in sub-Saharan Africa. Journal of Business Research 123: 196-207.
  • Afonso A and de Sá Fortes Leitão Rodrigues E. (2022) Corruption and economic growth: does the size of the government matter? Economic Change and Restructuring 55: 543-576.
  • Albrecht WS, Albrecht CO, Albrecht CC, et al. (2006) Fraud examination: Thomson South-Western New York, NY.
  • Bao Y, Hilary G and Ke B. (2022) Artificial intelligence and fraud detection. Innovative Technology at the Interface of Finance and Operations: Volume I: 223-247.
  • Jimenez Serrano J, Delgado Rodríguez MJ, Rodríguez Iglesias I, et al. (2025) Crime of misappropriation of assets in the business context. A systematic review. Accounting Research Journal 38: 122-140.