Banking book collateral transformation

Manan Shah
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Manan Shah: Pricewaterhouse-Coopers, USA

Journal of Securities Operations & Custody, 2019, vol. 11, issue 4, 363-370

Abstract: As banks strive to find ways to increase profits against headwinds that include continued low interest rates and intensive regulation, they are compelled to look within all areas of the organisation for efficiencies that result in cost savings. Fierce competition from traditional competitors and the emergence of financial technology (Fin-Tech) in traditional banking areas are further pushing organisations to reduce costs, eliminate credit risk loses and optimise capital utilisation. For too long, commercial and corporate lending (‘banking book’) businesses have been hampered by manual processes, fragmented systems and multiple data sources. As a result, banking book lending is exposed to increased operational and credit risk and inefficient business decision making — impeding their ability to scale efficiently and therefore eroding profits. Loan collateral mitigates credit risk while also providing the opportunity to mitigate pressure on banking book performance through efficient use. To achieve these goals, however, automated and holistic collateral management operations are mandatory: to, specifically, reduce counterparty and operational risk; eliminate manual processes; improve capital efficiency and provide the accurate datasets required for capital reporting and management. Transformation to an automated and holistic banking book collateral management operation also enables the deployment of digital technologies including machine learning (artificial intelligence) and distributed ledger technology, adding further to the potential to improve risk management, operational efficiency and scalability across the banking book.

Keywords: banking book collateral; collateral management; commercial lending; corporate lending; process automation; securities lending (search for similar items in EconPapers)
JEL-codes: E5 G2 K22 (search for similar items in EconPapers)
Date: 2019
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