Financial fraud detection plays a crucial role in safeguarding the integrity of financial accounts and maintaining the trust of stakeholders. Traditional fraud detection methods often rely on structured data and rule-based approaches, which may struggle to detect complex and evolving fraudulent activities. In recent years, combining computer vision and pattern recognition methods has become an interesting way to improve the identification of fraud in bank accounts. So, these are the basics of computer vision and pattern recognition. They show how they can be used to look at visual data and find complex patterns that might be lost in structured data alone. The integration of these techniques offers a comprehensive approach to detecting fraud by incorporating visual cues present in financial documents and transactions. Data acquisition and preprocessing can be addressed as critical steps to ensure the quality and reliability of visual financial data. There is a need for future potential of advanced computer vision technologies in fraud detection, emphasizing the need for ongoing research and innovation.