Double entry bookkeeping is a widely used accounting method that provides a system for recording financial transactions. Recently, this method has been applied to the field of distributed consensus algorithms, particularly the raft consensus algorithm. Raft \cite{ongaro2014search} is an innovative algorithm that is used to achieve consensus in a distributed system. The application of double entry bookkeeping to this algorithm aims to provide a more efficient and reliable method for tracking transactions and ensuring consistency. The use of double entry bookkeeping allows for improved fault tolerance, as well as better detection and prevention of data inconsistencies. Additionally, it enables the detection of malicious activities, such as double-spending or tampering with the transaction history. This application of double entry bookkeeping to raft consensus algorithm shows promise for improving the overall security and reliability of distributed systems. This paper presents Raft’s application to distributed accounting ledgers with double entry accounting for a more transparent and fraud resistant transaction system. Double entry accounting is essential to modern commerce to rationalize transactions and prevent fraud. Using this concept of double entry with consensus can be used for preventing financial fraud. The killer ’feature’ in this paper is also the process of comparing research that is envisioned by the author and further improved or enhanced by AI or more commonly known language models. In this case this language model product is ChatGPT\cite{devlin2018bert} by the OpenAI Research and Development Organization. We will also compare the results to get interesting insights on generated results.