Publicação
Automation in the Banking Sector: Process Automation for Hedge Accounting
| Resumo: | This work revolves around the development of a tool made for the calculation of Hedge Ratio. This initiative came from a national bank, and it was our duty as KPMG workers to deliver a high standard tool. We spent 4 months in this bank developing this tool and using as baseline an older existing tool built in excel. The existing resource had a slow running time, the application crashed, and it had structural errors creating circular dependency. So, we created a solution, which we called the proposed tool, built in python and with an improvement ratio of over 20000% compared to the existing tool. This project built into 6 different modules of calculations proved, the inevitability of this digital transformation era. We rapidly understood that there is a market for automation in the finance sector due to the large data quantity that is being produced and due to the poor knowledge of the average worker in data structures and data engineering. With all this being said, our tool, not only shows how fast python can be compared to excel when handling complicated calculations, but also shows the need for investment in this area and creation for more tools like this one and creation for more packages and APIs connected to this line of work. |
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| Autores principais: | Silva, Tomás Côrte-Real Ferreira da |
| Assunto: | Hedge Accounting MS Excel Python Automation Pipeline Process Improvement SDG 4 - Quality education SDG 8 - Decent work and economic growth SDG 17 - Partnerships for the goals |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | This work revolves around the development of a tool made for the calculation of Hedge Ratio. This initiative came from a national bank, and it was our duty as KPMG workers to deliver a high standard tool. We spent 4 months in this bank developing this tool and using as baseline an older existing tool built in excel. The existing resource had a slow running time, the application crashed, and it had structural errors creating circular dependency. So, we created a solution, which we called the proposed tool, built in python and with an improvement ratio of over 20000% compared to the existing tool. This project built into 6 different modules of calculations proved, the inevitability of this digital transformation era. We rapidly understood that there is a market for automation in the finance sector due to the large data quantity that is being produced and due to the poor knowledge of the average worker in data structures and data engineering. With all this being said, our tool, not only shows how fast python can be compared to excel when handling complicated calculations, but also shows the need for investment in this area and creation for more tools like this one and creation for more packages and APIs connected to this line of work. |
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