Rethinking Operating Models in the Age of AI
Venue 42, 5 Times Square, NYC
October 24 | 8:30 Onwards
Register NowDeal monitoring is a critical step in the portfolio management process. With a growing need to generate business insights in a timely manner and – more specifically – track whether borrowers are adhering to loan agreements, asset managers are now seeking a workflow tool for portfolio company data management to help standardize certain functions and increase operational efficiency throughout the deal life cycle.
However, most managers today are heavily dependent on spreadsheets and other manual processes that result in a number of operational challenges such as:
By leveraging a financial statement module that uses machine learning-based algorithms in conjunction with configurable data and process workflows, managers can overcome these aforementioned challenges and possess the ability to perform more robust data collection, processing and management while also reducing risks and costs.
Through natural language processing (NLP) technology, this module automates the processing of financial statements in different languages and converts data into a standard template. Once this data is further enriched with machine learning capabilities and internal classification/dictionaries, workflows are set up to ensure that the information is reviewed and approved to be used for final reporting purposes.
With these capabilities in mind, there are several key features that firms should look for before selecting a financial statement module:
Using the right solution, managers can save time and limit the manual efforts they put forth to achieve data accuracy with process robustness. With interactive reporting and analytics, IVP’s Financial Statement module empowers the front office to generate insights on their portfolio investments, allowing them to auto-calculate and track covenants that are specific to each deal.
To learn more, visit https://www.ivp.in/platforms/ivp-for-private-funds/ or contact sales@ivp.in.
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