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A data analyst to perform the technical steps and write the queries, A team member with extensive experience of the processes and internal controls in the relevant area of the investigated company and; A forensic scientist who is familiar with patterns of fraudulent behaviour.
In many instances, bank fraud is a criminal offence. While the specific elements of particular banking fraud laws vary depending on jurisdictions, the term bank fraud applies to actions that employ a scheme or artifice, as opposed to bank robbery or theft. For this reason, bank fraud is sometimes considered a white-collar crime. [2]
Fraud detection is a knowledge-intensive activity. The main AI techniques used for fraud detection include: . Data mining to classify, cluster, and segment the data and automatically find associations and rules in the data that may signify interesting patterns, including those related to fraud.
A sell-side analyst's work is not used by its employer to invest directly, rather it is sold either for money or for other benefits by the employer to buy-side organizations. Sell-side research is often used as 'soft money' rather than sold directly, for example provided to preferred clients in return for business.
Forensic accounting, forensic accountancy or financial forensics is the specialty practice area of accounting that investigates whether firms engage in financial reporting misconduct, [1] or financial misconduct within the workplace by employees, officers or directors of the organization. [2]
Fraud and financial crime patterns have become more digital and faster changing, leveraging the underlying characteristics of the underlying digital payments infrastructures. This caused traditional rule based systems to be ineffective and led the way to machine learning and AI-based fraud detection techniques.