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This article sets out to explore some of the essential tools required by organizations in the domain of data engineering to efficiently improve data quality and triage/analyze data for effective business-centric machine learning analytics, reporting, and anomaly detection.
Financial Analytics – Financial services and financial technology (FinTech) are increasingly turning to automation and artificial intelligence to fuel their decision making processes for investments. There are several AI/ML focused use cases to highlight.
Financial analysis with real-time analytics is used for predicting investments and drives the FinTech industry's needs for high-performance computing. This has given rise to a completely new set of computing workloads for Machine Learning which drives Artificial Intelligence applications.
P2P lending apps use algorithms and data analytics to evaluate interest rates and the creditworthiness of borrowers. Conclusion The term “fintech app development” refers to creating financial apps which can be used in various ways. Find the best eWallet app development company.
For example, an analytics application would work best with unstructured image files stored in a non-relational graph database. It’s well-suited for most online transaction processing (OLTP) workloads and works with some online analytical processing (OLAP) workloads. MongoDB also provides strong encryption and firewall security.
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