Trusting Social vs. CPA Fraud Detection Service

Our organization delivers AI-powered solutions tailored for credit risk assessment, identity verification, and customer acquisition, specifically within the lending industry, with the intention of assisting the 3 billion people who lack adequate banking services. By leveraging a comprehensive range of Big Data technologies alongside an innovative AI-based alternative credit scoring system and robust fraud detection mechanisms, we are transforming financial services while targeting often overlooked market segments. We enable digital customer onboarding from virtually anywhere and at any time, utilizing advanced facial recognition, AI-driven fraud detection, and ID verification to ensure secure transactions. Our ambition is to connect millions of creditworthy but underserved borrowers by establishing a streamlined digital lending framework that boasts disbursal rates significantly higher than conventional approaches. It is crucial to note that all information is securely stored within the partner’s data center, granting them full control over data management processes. We only work with data that has been masked, anonymized, and aggregated to guarantee that individual personal information remains confidential. Additionally, we design and implement systems that comply with GDPR and other local data security and privacy regulations, ensuring that our operations reflect the highest standards of integrity and compliance. This unwavering commitment to security and privacy not only reinforces our dedication to building trust and transparency in financial services but also helps us create a more inclusive financial ecosystem for all. Ultimately, our innovative solutions are paving the way for a future where more individuals can access essential financial resources.

Benford's law is an effective method for revealing patterns that may indicate fraudulent disbursements. It requires the analysis of audit trail reports from accounting software such as QuickBooks to identify suspicious actions like voids and deletions. Moreover, it also includes the detection of multiple payments made for the same amount on a single day. A comprehensive examination of payroll runs is performed to find any discrepancies, particularly payments that exceed designated salary or hourly rates. Payments made on days when businesses are closed are given extra attention. By employing statistical analyses, outliers that could point to fraud are uncovered, and duplicate payments are verified for accuracy. Furthermore, vendor files in accounts payable are scrutinized for names that may appear strikingly similar, leading to investigations into potential fictitious vendors. Comparisons of vendor addresses and payroll information are assessed through Z-Scores and relative size factor tests. Although data monitoring and unexpected audits have proven to significantly mitigate fraud losses, merely 37% of organizations have implemented these vital controls. For smaller businesses with fewer than 100 employees, the average estimated loss attributed to fraud stands at $200,000, underscoring the fact that these enterprises typically lack sufficient resources to effectively identify and respond to fraudulent activities. Therefore, it becomes crucial for small businesses to embrace more robust fraud detection strategies to protect their financial health and ensure long-term sustainability. By doing so, they can not only detect irregularities but also foster a culture of transparency and accountability within their operations.