Many enterprises have achieved a novel, quick, and low-costtechnology to address the concerns of data storage and availabilityfor customers, which is referred to as Cloud Computing. More andmore financial service companies and organizations have shifted theiroffline services to cloud platforms to provide customers with moreconvenient and accurate services. Working with a cloud servicesprovider offers a wide variety of benefits for banks and financialinstitutions. This includes greater flexibility and scalability, lowercosts, and improved organizational efficiency. However, at themoment, they pose a certain level of data security risk to financialorganizations. For financial institutions, keeping data secure is of theutmost importance. Financial information is extremely sensitive,making it valuable and especially vulnerable. The cloud serviceproviders are making significant efforts to develop the cloud industryin order to maintain optimal security. After discussing cloudcomputing in the financial sector, this research outlined six majorsecurity concerns that financial institutions face in cloud computing.They are Information security Business securitySystem Security HostSecurity Data Security Network Security. We also discussed themajor strategies by cloud computing providers to tackle theseissues.Once the security challenges can be resolved properly, cloudcomputing will be further promoted in the finance sector. Financialinstitutions may leverage cloud computing in the future to developnovel business models and provide customers with an entirely newexperience with financial services.
Financial data volumes are increasing, and this appears to be a long-term trend, implying that data managementdevelopment will be crucial over the next few decades. Because financial data is sometimes real-time data, itis constantly generated, resulting in a massive amount of financial data produced in a short period of time.The volume, diversity, and velocity of Big Financial Data are highlighting the significant limitations oftraditional Data Warehouses (DWs). Their rigid relational model, high scalability costs, and sometimesinefficient performance pave the way for new methods and technologies. The majority of the technologiesused in background processing and storage research were previously the subject of research in their earlystages. The Apache Foundation and Google are the two most important initiatives. For dealing with largefinancial data, three techniques outperform relational databases and traditional ETL processing: NoSQL andNewSQL storage, and MapReduce processing.
As organizations' desire for data grows, so does their search for data sources that are both usable and reliable.Businesses can obtain and collect big data in a variety of locations, both inside and outside their own walls.This study aims to investigate the various data sources for business intelligence. For business intelligence,there are three types of data: internal data, external data, and personal data. Internal data is mostly kept indatabases, which serve as the backbone of an enterprise information system and are known as transactionalsystems or operational systems. This information, however, is not always sufficient. If the company wants toanswer market and industry questions or better understand future customers, the analytics team may need to look beyond the company's own data sources. Organizations must have access to a variety of data sources in order to answer the key questions that guide their initiatives. Internal sources, external public sources, andcollaboration with a big data expert could all be beneficial. Companies who are able to extract relevant datafrom their mountain of data acquire new perspectives on their business, allowing them to become morecompetitive
Budgeting in the traditional sense is simply too slow and rigid to keep pace with the swiftly changing business environment. At the moment, there is far too much volatility, complexity, and uncertainty. A driver-based planning and budgeting model is more data-driven than a traditional budget model. This budgeting strategy shortens the time it takes to create a budget. Most driver-based planning and budgeting models center on predictions. One of the most difficult aspects of using driver-based planning, however, is identifying appropriate business drivers and predicting the impact of these drivers. Machine learning can assist driver-based budgeting processes in identifying the key drivers and predicting the impacts of these drivers. This study discusses the building of predictive modeling using machine learning. It illustrates stages from quantifying the budgeting issues to determining the best predictive mode for driver-based budgeting.
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