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Modeling the operational risk assessment of retail banking business line in express lending

Vol. 24, Iss. 12, DECEMBER 2018

Received: 27 June 2018

Received in revised form: 12 July 2018

Accepted: 27 July 2018

Available online: 24 December 2018

Subject Heading: Banking

JEL Classification: C02, C65, G21

Pages: 2678–2694

https://doi.org/10.24891/fc.24.12.2678

Goremykina G.I. Plekhanov Russian University of Economics, Moscow, Russian Federation
g_iv.05@mail.ru

https://orcid.org/0000-0002-8047-5393

Shchukina N.A. Plekhanov Russian University of Economics, Moscow, Russian Federation
shchukinan@yandex.ru

https://orcid.org/0000-0002-5825-1051

Mastyaeva I.N. Plekhanov Russian University of Economics, Moscow, Russian Federation
imastyaeva@mail.ru

ORCID id: not available

Subject The upward trend in instant loans necessitates formalized tools to simulate and analyze the Retail Banking business line in express lending. The operational risk assessment is one of the main modeling objects of the said business line.
Objectives We aim to develop an aggregated model to assess risks in instant lending that correspond to two out of seven event type categories of operational risk associated with default on loans, which were standardized by the Basel Committee on Banking Supervision.
Methods The study draws on the simulation modeling methodology.
Results We built an aggregated economic and mathematical model to assess risks in express lending. The model considers the operational risk associated with opportunistic behavior by agents. Using the model, we developed a methodology and algorithm for assessment. The paper also provides a classification of retail outlets that takes into account the potential for opportunistic behavior by agents.
Conclusions Our simulation observations enable an aggregate assessment of external and internal operational risks inherent in the Retail Banking business line of banking outlets in the current period. The findings may be applied by banks and microlenders to assess operational risks in instant lending and create a system to manage them.

Keywords: simulation model, express loan, operational risk, aggregated model, contingent loss

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