REDEFINING SYNDICATED LOAN PROCESSING: AN ORIGINAL STRAIGHT THROUGH PROCESSING FRAMEWORK FOR INVESTMENT BANKING MIDDLE OFFICES
DOI:
https://doi.org/10.65009/1vev5q10Keywords:
Syndicated Loans, Straight Through Processing (STP), Investment Banking Middle Office, Event-Driven Architecture, Loan Lifecycle Automation, Allocation Reconciliation, Operational Risk Management, Regulatory Auditability.,,Abstract
Syndicated loan processing is one of the least automated areas in the investment banking
middle office, in part due to the multi-party coordination, compound lifecycle events, and
fragmented data standards. The current automation efforts are aimed at optimization of tasks
on a more incremental level, which does not address structural inefficiencies or operational
risks. The paper introduces a unique Straight Through Processing (STP) framework that is
syndicated loan operations-specific and re-invents the concept of middle-data processing as an
event-driven lifecycle of data. The framework presents a standardized loan data model,
automated allocation synchronization with agent banks, and control mechanisms that guarantee
data consistency, auditability as well as regulatory transparency. The proposed framework
removes the manual handoffs and permits real-time processing of booking, servicing, and
settlement hence reducing systemic market failures and not institution-specific limits. The
strategy makes it scalable, eliminates settlement risk and matches a syndicated loan operation
with wider market modernization and shorter settlement expectations. All in all, the
architecture signifies one of the most critical changes in architecture towards the actual end-to
end automation of a historically manual asset class.
References
S Aramonte, W Huang, A Schrimpf. “DeFi risks and the decentralisation illusion BIS
Quarterly Review Posted: 2021
Fissler, T and J Ziegel (2016): “Higher order elicitability and Osband’s principle”, The
Annals of Statistics, August, vol 44, no 4, August, pp 1680–707.
P. Wiśniewski, M. Gajda, and M. Szydłowski, “Spreadsheets as interoperability solution
for business process representation,” Applied Sciences, vol. 9, no. 2, Art. no. 345, Feb.
T Berg, V Burg, A Gombovic, M Puri. “On the rise of fintechs -credit scoring using digital
footprints” Review of Financial Studies, volume 33, p. 2845 - 2289 Posted: 2020.
Singh, N., Srivastava, S., & Sinha, N. (2017). Consumer preference & satisfaction of M
wallets: A study on North Indian consumers. International Journal of Bank Marketing,
(6), 944–965. doi:10.1108/IJBM-06-2016-0086
H. S. Mamede, C. M. G. Martins, and M. Mira da Silva, “A lean approach to robotic
process automation in banking,” Heliyon, vol. 9, 2023, Art. no. e18041.
G Buchak, G Matvos, T Piskorski, A Seru. “Fintech, regulatory arbitrage, and the rise of
shadow banks”, Journal of Financial Economics, volume 130, p. 453 - 483 Posted: 2018.
Bjorheim, J, J Coche, A Joia and V Sahakyan (2018): “A macro-based process for actively
managing sovereign bond exposures”, Advances in the Practice of Public Investment
Management, pp 103–29, Palgrave Macmillan.
C Catalini, A De Gortari, N Shah. “Some simple economics of stablecoins”. Annual
Review of Financial Economics Posted: 2022.
T. Davenport and R. Ronanki, “Artificial intelligence for the real world,” Harvard Bus.
Rev., vol. 96, no. 1, pp. 108–116, Jan.–Feb. 2018.
Singh, N., Srivastava, S., & Sinha, N. (2017). Consumer preference & satisfaction of M
wallets: A study on North Indian consumers. International Journal of Bank Marketing,
(6), 944–965. doi:10.1108/IJBM-06-2016-0086.
L W Cong, Z He. “Blockchain disruption and smart contracts”, Review of Financial
Studies, volume 34, p. 1191 - 1235 Posted: 2019
Yuan, S., Liu, Y., Liu, J., & Yao, R. (2016). An investigation of user’s continuance intention
towards mobile banking in China. Journal of Information Development, 32(1), 20–34.
doi:10.1177/0266666914522140.
M. Andoni et al., “Blockchain technology in the energy sector: A systematic review,”
Renew. Sustain. Energy Rev., vol. 100, pp. 143–174, Feb. 2019.
Martinelli, L and L-S Vallee (2021): “Measuring and managing ESG risks in sovereign
bond portfolios and implications for sovereign debt investing”, Journal of Portfolio
Management, vol 47, pp 198–223.
Zhao, Q., Tsai, P. H., & Wang, J. L. (2019). Improving Financial Service Innovation
Strategies for Enhancing China’s Banking Industry Competitive Advantage during the
Fintech Revolution: A Hybrid MCDM Model. Sustainability, 11(5), 1419.
doi:10.3390u11051419.
H. C. Aksoy, “Is the syndicated loans market ready for distributed ledger technology?”
Law and Financial Markets Review, published online Sep. 18, 2023,
doi:10.1080/17521440.2023.2254523.
Campbell, J, C Pflueger and L Viceira (2020): “Macroeconomic drivers of bond and equity
risks”, Journal of Political Economy, vol 128, pp 3148–85.
D. Kotios, G. Moustakas, et al., “Deep learning enhancing banking services: A hybrid
transaction classification and cash flow prediction approach,” IEEE / open-access via
PubMed Central, 2022.
Zanjani, N., Edwards, S., Nykvist, S., & Geva, S. (2017). The Important Elements of LMS
design that Affect User Engagement with e-learning tools within LMSs in the higher
education sector. Australasian Journal of Educational Technology, 33(1)
