Publicação
Business Intelligence In A Data Driven Bank: Potentiating Compliance
| Resumo: | Nowadays the whole business panorama is becoming more and more competitive, which means that they need to use all the possible competitive advantages that are available. Business Intelligence is a powerful tool that may be used to enhance companies' decisionmaking processes and help them make the best strategic decisions. This master thesis addresses a research gap in the integration of business intelligence systems within the compliance departments of private banking companies. Compliance departments are often overwhelmed with vast amounts of data from various sources, making it challenging to monitor and manage compliance activities effectively. This specific Compliance department never had a framework that cleaned, stored and outputted data inherent to their tasks, operations and key metrics. The compliance department is now able to make decisions based on accurate and timely information, which is crucial for an organization to be compliant regarding laws and regulations. The data mart was constructed using the necessary tables from the different raw sources, which was transformed and used to build all the dimensions and factual tables, using an ETL process (Extract; Transform; Load) to populate tables daily. A dashboard was built to provide utility,sustain the department's needs, and enhance the company's performance, more specifically of the compliance department with the impact BI (Business Intelligence) tools have on the compliance spectrum, leaving users more open to add value through other manners as they have key insights which can lead to optimized performance, AML risk being diminished and identifying possible problems within the compliance scope. Ensuring data quality and consistency across all sources was a significant challenge. Inconsistencies in data formats, completeness, and accuracy could potentially compromise the reliability of the Data Mart (DM) and Data Warehouse (DW). Extensive testing activities were conducted in a quality (QA) environment to verify the correct creation of surrogate keys (SKs) in the fact tables and to ensure that no duplicates were present. Secondly, the technical complexities associated with processes such as Extract, Transform, Load (ETL) in SQL Server Integration Services (SSIS), DM construction, and dashboard development posed significant challenges. Future work could explore integrating machine learning applications with the DM. Leveraging machine learning tools can enhance decision-making processes, provide deeper insights, and offer various scenarios within the compliance scope. Implementing detection algorithms to identify unusual patterns and significant deviations would be an effective way to flag suspicious Anti-Money Laundering (AML) transfers for further investigation. |
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| Autores principais: | Coelho, José Guilherme Nóbua |
| Assunto: | Business Analytics Business Intelligence Data Warehousing Data Visualization Compliance Banking SDG 8 - Decent work and economic growth SDG 9 - Industry, innovation and infrastructure |
| 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: | Nowadays the whole business panorama is becoming more and more competitive, which means that they need to use all the possible competitive advantages that are available. Business Intelligence is a powerful tool that may be used to enhance companies' decisionmaking processes and help them make the best strategic decisions. This master thesis addresses a research gap in the integration of business intelligence systems within the compliance departments of private banking companies. Compliance departments are often overwhelmed with vast amounts of data from various sources, making it challenging to monitor and manage compliance activities effectively. This specific Compliance department never had a framework that cleaned, stored and outputted data inherent to their tasks, operations and key metrics. The compliance department is now able to make decisions based on accurate and timely information, which is crucial for an organization to be compliant regarding laws and regulations. The data mart was constructed using the necessary tables from the different raw sources, which was transformed and used to build all the dimensions and factual tables, using an ETL process (Extract; Transform; Load) to populate tables daily. A dashboard was built to provide utility,sustain the department's needs, and enhance the company's performance, more specifically of the compliance department with the impact BI (Business Intelligence) tools have on the compliance spectrum, leaving users more open to add value through other manners as they have key insights which can lead to optimized performance, AML risk being diminished and identifying possible problems within the compliance scope. Ensuring data quality and consistency across all sources was a significant challenge. Inconsistencies in data formats, completeness, and accuracy could potentially compromise the reliability of the Data Mart (DM) and Data Warehouse (DW). Extensive testing activities were conducted in a quality (QA) environment to verify the correct creation of surrogate keys (SKs) in the fact tables and to ensure that no duplicates were present. Secondly, the technical complexities associated with processes such as Extract, Transform, Load (ETL) in SQL Server Integration Services (SSIS), DM construction, and dashboard development posed significant challenges. Future work could explore integrating machine learning applications with the DM. Leveraging machine learning tools can enhance decision-making processes, provide deeper insights, and offer various scenarios within the compliance scope. Implementing detection algorithms to identify unusual patterns and significant deviations would be an effective way to flag suspicious Anti-Money Laundering (AML) transfers for further investigation. |
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