In a typical multi-attribute decision-making (MADM) problem, different alternatives are present for evaluation according to multiple criteria. By default, independence or slight interrelation of criteria is an essential prerequisite in most existing MADM techniques in order to generate appropriate and non-overrated discrimination scores. This research, applying a tailored version of factor analysis (FA) method, prepares an integrated algorithm for empowering MADM techniques to deal with the kinds of criteria carrying severe interrelation. Accordingly, guidelines for adjusting FA are proposed here to simultaneously eliminate the criteria interrelation and decrease the data volume, so that only the main aspects of data are taken into consideration for decision-making. In the end, the practical case of financial discrimination is investigated for companies listed in stock exchange applying the proposed algorithm, and the results are validated using ELECTRE and VIKOR techniques. Furthermore, the shortcomings of conventional adjustments for FA are explored through the case study. The proposed approach is also applicable for evaluating alternatives in portfolio management, supply chain management, credit scoring, ranking, etc. It is also helpful in boosting machine learning algorithms and digitization of sectors such as healthcare, manufacturing, marketing, IoT processing, and recommendation systems.