One of the most popular platforms in token-based banking is the flexible Stellar platform. The wide range of Stellar’s features allows companies to use it in modern cryptocurrency and token-based banking. This network charges a fee for each transaction. A percentage of the net amount is generated as the inflation rate of the network due to the increased number of tokens. These fees and inflationary amounts are aggregated into a general account and ultimately distributed among the network members on a collective-vote basis. In this mechanism, network users select an account as the destination to which they wish to transfer assets using their user interface, generally a wallet. This account could be the account of charities that need this help. The target distribution network is then determined based on the voting results of all members. One of the challenges in this network is the targeted and fair distribution of these funds between accounts. In this paper, the first step is a complete infrastructure of a Stellar financial network that will consist of three network-based segments of the core network, an off-chain server, and a wallet interface. In the second step, a context-aware recommendation system is implemented to solve the targeted management of payroll account selection. The results of this study concerning the importance of the targeted division of collective assets show a context-aware recommendation system as a solution to improve the process of Stellar users’ participation in the voting process.

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We propose a new ML model called Topological Forest that contains an ensemble of decision trees. Unlike a vanilla Random Forest, Topological Forest has a special training process that selects a smaller number of decision trees on a topological graph representation that TDA Mapper constructs. Compared to Vanilla Random Forest, Topological Forest significantly improves the computational efficiency of inference time due to the smaller ensemble size and selection of better decision trees while keeping the diversity of decision trees. Our experiments show that Topological Forest can speed up inference time by more than 100x on average while compromising at most 2% reduction in the AUC metric for the prediction quality.