Centralising Finance Data

Stories>Centralising Finance Data

To run financial predictive analytics models, large volumes of data are required. This data must be organised in complex hierarchies to make it meaningful. In addition, due to the sensitive nature of the data and the various levels of access requirements in banking and insurance institutions, it is essential that the data is supported by a robust security model.

Ideally, key financial data for predictive analytics modelling must therefore be centralised in a curated, access-controlled data repository. With sound knowledge of data and system landscapes, substantial industry experience, and deep technical skills, Monocle guarantees fast and agile delivery of comprehensive data management solutions that meet all regulatory and client-specific requirements. Our approach begins with the development of an integration pattern and a data transfer mechanism to migrate all data into a central data repository, with consideration of all data management requirements. Hierarchies are also flattened for use downstream. Once the data is sourced, a security model can then be developed to process access requests and track the use of data. In addition, a reconciliation engine and dashboards with drill-down capabilities are designed to produce reconciliation results.

With strong core financial predictive analytics capabilities, financial institutions are equipped to make better, more informed decisions and act quickly to take advantage of opportunities that arise, whilst mitigating the risks to their businesses.