The recent growth in data collection and management processes have made the financial or transactional data availability for every modelling process such as forecasting. The financial forecasting replies on data modelling on the transactional financial data and which again relies on traditional data mining processes. The data mining process, in spite of multiple advantages such as modelling flexibilities, the sub-processes must be customized for fitting into the financial modelling. A good number of research attempts were made to predict the financial data points based on time series transactional data. However, most of the parallel research outcomes are criticised for higher complexity. Also, the outcome of the predictive or forecasting processes rely on the data cleaning methods, thus, the search for the less complex and composite data cleaning method is continuing as a focus of recent researches. The data cleaning method primarily concerns the outlier or noise detection and reduction, anomaly detection and reduction, missing value detection and reduction with the scope for dimensionality reductions. The parallel research outcomes for data cleaning is highly criticised for higher complexity. Thus, this work proposes a novel method for data cleaning with the help of composite data cleaning coefficient method. As a result, this work demonstrates nearly 100% accurate for any modelling technique with the proposed cleaning method, which is nearly 6% improvement to each modelling technique. In the course of establishing the novel method, this work also showcases the mapping of traditional data mining process into the financial forecasting models.