Measuring Banks’ Participation in Payment Systems: Development of a Composite Index Using Indian Data

Abstract

The rapid advancement of payment technologies and potential disintermediation pressure make it important to monitor how actively commercial banks participate in payment and settlement systems. This study conceptualizes bank participation as a multidimensional construct and develops a Bank Payment Participation Index (BPPI or ANR BPPI) using publicly available Reserve Bank of India data for 2011–2012 to 2022–2023. BPPI integrates Financial Capacity (FC), Technological Readiness (TR), Payment Performance (PI), and a PPI-based Technological Advancement/Disintermediation proxy (TAD). TAD, measured as the share of PPI transactions in total payment volumes, enters the index as (1−TAD) because rising non-bank payment penetration reduces banks’ intermediation share; a higher TAD represents a structural drag on bank payment participation, and (1−TAD) converts this drag into a participation-compatible scale. The index applies min–max normalisation, equal-weighted sub-index aggregation, and geometric mean composition with lagged input dimensions. Computations show that the four-component BPPI rises from 230.794 in 2013–2014 to 797.453 in 2022–2023, indicating a strong long-run increase in banking-system participation. The BPPI remains strongly associated with GDP over the 2013–2014 to 2022–2023 sample, with R Square = 0.906 and adjusted R Square = 0.894. Diagnostic tests indicate that the validation is best interpreted as association-based evidence rather than causal proof. The BPPI is proposed as a decomposable monitoring and diagnostic framework that equips regulators and banks to track participation trends and detect structural vulnerabilities over time, subject to future refinement using fixed policy goalposts, bank-level data, and CBDC-specific transaction data. In its present form, the BPPI constitutes a model-stage prototype framework subject to future operationalisation with fixed expert-determined benchmarks and bank-level disaggregated data.