Asset Management Challenges

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One of our customers is a leading U.S. based Asset Management service provider dealing with various businesses such as banks, brokerage firms, trust companies, insurance providers and credit unions which involves different data sources like the Investment Accounting System, Trading Systems, Compliance and Billing systems etc. The customer’s traditional and alternative investment services cover various equities such as Global Equity, International Equity, Emerging Markets Equity, etc., and fixed income investments in a wide range of global, regional and country specific strategies for various clients.

For an asset management firm, business insights are much more important to make better investment decisions. As the firm deals with enormous amount of data that comes from different data sources, it is very difficult to manage large amount of data and get insights from it. Data quality and better data management plays a vital role to derive reliable business insights from the data. To make the data management process simpler, the firm has decided to procure an effective data management platform that is available in the market today.



The day to day challenge of the leading U.S based Asset Management Provider is that they must run specific data quality routines and data preparation on the top of their enterprise systems and ingest lots of custodial data from different businesses that they are dealing with. Preparing reports and reconciliation was a tedious process for the firm as it required enormous amount of time and manual work. They had to manually search and collect the information from different systems to prepare weekly/monthly/annual reports and tracing the historical data was major pain point. Further they were challenged with fixing the data across tallying in all sources to keep accurate information and track.

We implemented DQLabs AI-augmented data management platform to scale the process using AI decisioning on data quality and automatically configure business rules across the enterprise.



Initially, we made a comprehensive study of the systems that were used by the customer. Various data sources included systems such as Investment Accounting System, Trading Systems, Compliance and Billing systems and vendor systems such as QuickBase, Advent were involved. Further this process was complicated with the lack of automated integration with portfolio management systems websites in which all we could extract is the web data by logging or the statements that could be downloaded and extracted manually. With the use of built-in AI driven DataSense™ module, DQLabs connected with all data sources and aggregated, modernized the meta data, catalog and taxonomy management and automated rules and learning capabilities. Further in case of PDF and web statement extraction – the platform used cognitive services with both supervised and unsupervised learning to train and pull the data across various types of statement. Upon centralized, simple transformations were configured with ease and no technical guidance by business and data analyst to reconcile and push the data to different sources. This helped and eased the management of reconciliation and overall data quality , health of the underlying source systems.

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