Authors

Alexander Soldatkin¹;  ¹ University of Oxford, UK

Discussion

Since the Soviet era, Russia’s banking sector has woven itself into a complex network of political factions and familial ties. While the influence of these connections in Russia’s political and economic spheres has been recognised [1, 2], empirical research on their impact on bank performance and survival remains limited. This study investigates the role of factional and familial networks within Russia’s banking sector through two primary questions: (1) What measures of network influence affect the performance and survival of banks and companies linked to factions? (2) Do these familial and factional connections matter in the modern Russian banking sector, and what are their business implications?

Russian factions form an intricate landscape of power, with the Duma, Federation Council, intelligence agencies, oligarchs, and regional groups influencing the sector through legislative, regulatory, and informal means [3, 4]. Previous studies highlight the pervasive role of corruption and informal networks in Russia’s economic development, particularly within banking [5, 6, 7]. Influential individuals and groups are often implicated in preferential practices and oligarchic entrenchment [1, 8]. Despite these findings, few studies offer systematic, quantitative analyses of how such networks impact bank survival and performance.

This study uses social network analysis (SNA) to quantify factional and familial influence. By constructing a graph database encompassing banks, shareholders, directors, and their political and familial links, it creates network influence measures relevant to bank outcomes. Data sources include the Unified State Register of Legal Entities (EGRUL), the Federal Tax Service (FNS), and disclosures by the Central Bank of Russia. Supplementary sources include media and sanctions reports. Centrality metrics—degree, betweenness, and eigenvector—will assess node prominence within the network [9]. Community detection algorithms will identify clusters tied to specific factions or familial groups, while analysis of cross-ownership and overlapping directorships will highlight their effects on bank outcomes [10].

By quantifying the influence of factional and familial networks, this research will offer empirical insights into their impact on bank performance and survival. A clearer understanding of these dynamics will contribute to the study of Russia’s political economy and inform policymakers and investors navigating this landscape. Methodologically, this work integrates large-scale government and compliance data into social network models using graph databases, advancing empirical research on informal networks in Russia’s banking sector [11]. Using social network analysis, it aims to identify critical measures of network influence and assess their impact on bank performance and survival, thereby uncovering key structures within the Russian financial system.