Reliable and effective data access procedures are essential for data-driven fraud detection to be effective (Albrecht et al., 2006; Bao et al., 2022). Open Database Connectivity (ODBC), which enables direct access to accounting and enterprise databases, is one important strategy. By using ODBC, analysts can extract data in real-time or on a quarterly basis without depending on manual file exports, which lowers the possibility of errors and data manipulation. This direct connection guarantees that analyses are based on correct and comprehensive datasets, improves data integrity, and maintains audit trails.

Many fraud-relevant data sources are available in text-based forms like CSV, TXT, and system log files in addition to database access (Ashfaq et al., 2022). These files frequently include important data about user behavior, system events, and transactions that might not be properly recorded in conventional accounting systems. By integrating a variety of data sources into the analytical process, the capacity to import and analyze both structured and semi-structured text data broadens the reach of fraud detection.

Hosting a data warehouse, which unifies data from several systems into a single location, is a more sophisticated method of data access (Alogaiel and Alrwais, 2023). A data warehouse facilitates ongoing fraud surveillance as opposed to reactive, one-time investigations and supports historical analysis over long time periods. This is a strategic move in the direction of proactive fraud detection, which enables businesses to detect new threats early and bolster their overall efforts to avoid fraud.

References:

  • Albrecht WS, Albrecht CO, Albrecht CC, et al. (2006) Fraud examination: Thomson South-Western New York, NY.
  • Alogaiel NF and Alrwais OA. (2023) An Assessment of the Quality of Open Government Data in Saudi Arabia. Ieee Access 11: 61560-61599.
  • Ashfaq T, Khalid R, Yahaya AS, et al. (2022) A machine learning and blockchain based efficient fraud detection mechanism. Sensors 22: 7162.
  • 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.