The research emphasises on the significance of the use of big statistics analytics which will fight towards financial fraud among corporate businesses. Sophisticated analytical strategies can shield groups’ monetary well-being, popularity and potential to discover and prevent frauds. For instance, there are numerous impediments to the usage of Big Data analytics in forensic Computing consisting of negative pleasant facts, privacy concerns, complicated technology base and shortage of professional manpower. This paper is a presentation of the Big Data Analytics Framework (BDAF) that turned into advanced especially for detecting economic fraud in Corporate corporations. The framework is an all-inclusive one and it has been designed for handiest use through corporate corporations. In its flip this system employs modern day algorithms used in facts processing, analysis and gadget mastering as a way to understand feasible signs and symptoms of fraud. The BDAF has a couple of programs in forensic Computing which encompass facilitating hazard evaluation, compliance monitoring and proactive identity of fraudulent sports. This method complements their potential to discover fraud instances promptly consequently lowering dangers related to it thru reading specific monetary statistics assets like transaction records, economic reviews or market tendencies. In depth simulation analyses are performed with real economic datasets from Corporate enterprises with a view to examine the effectiveness of BDAF. These simulations investigate whether or not the framework can be able to detect corruption, extortion among different kinds of financial assertion fraud in addition to its accuracy, performance and scalability. Organizations can enhance their structures for detecting deception whilst safeguarding their monetary pastimes within an intricate enterprise environment with the aid of scaling boundaries and employing innovative techniques.