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Transforming private equity data operations with Intelligent Document Processing

Paul Quigley
August 31, 2022

Technological innovation is now crucial for private equity firms. A key driver is the need to improve the investment data pipeline, i.e. the gathering of accurate data, producing insights via data analysis, and delivering those insights both internally and externally.

Intelligent document processing uses artificial intelligence to extract data from complex PDF documents at scale. For PE firms, IDP can be applied to a wide variety of document types including Quarterly Reports, Capital Account Statements and Cash Flow Statements. Industry leaders are now implementing IDP solutions to deliver significant cost reductions and operational improvements. 

How is Intelligent Document Processing transforming the way PE firms work?

Before IDP, there was OCR (optical character recognition) technology. Unfortunately, OCR requires standardised templates and performs poorly when processing new document layouts. IDP is the scalable, future-proof solution to this problem. 

IDP is able to address a variety of core challenges for PE firms. These include (but are not limited to):

Improving data accuracy

IDP solutions extract accurate data from documents and export that data in a structured, accessible format. Accurate, structured data is essential for supporting a variety of PE uses cases, including risk-return analysis and ESG measurement.

These processes are typically time-consuming and labour intensive. However, with the support of AI-based technology, PE firms can increase productivity while freeing up time for their teams to focus on growth-driving strategies.

Adapting to industry changes

The PE sector is continuously impacted by shifting global trends and dynamics. A perfect example of this is the pressure now being faced by firms to report on ESG-related factors and demonstrate how their investments are supporting climate-friendly initiatives. 

As this change is relatively new and global standardisation is still a long way off, formats for ESG-related documentation vary greatly. IDP presents a significant advantage in this area, as it can process previously unseen documents without needing pre-configuration.

Supporting company-wide objectives

The financial industry is struggling to recruit talent, especially in tech-related fields. This is having a detrimental impact on PE firms’ abilities to implement and operate new digital solutions. It’s therefore a top priority for many companies to build capable teams quickly, whether internally or externally.

IDP vendors offering fully managed support are able to support this objective. The addition of human-in-the-loop (HITL) support on top of the AI automation means firms get guaranteed data accuracy without having to invest time in sourcing the best-fit talent.

Additionally, most IDP solutions can be integrated with Enterprise Resource Planning (ERP) systems and other existing platforms. This enables end-to-end automation of complex processes and ensures data is consistent company-wide.

Use Case: Quarterly Reports 

Finding a suitable automated data extraction solution is no easy task for PE firms. Quarterly reports are a perfect example of why automating processes can be so difficult. 

Quarterly reporting documents are highly complex and layouts vary from fund to fund. OCR technology, which has traditionally been used to carry out data extraction tasks, is not up to the challenge. OCR requires a custom template for each document layout, making operational costs too high.

This is precisely why global asset management firm Unigestion reached out to Evolution AI for IDP support. Unigestion had struggled with poor quality data extracted from private equity quarterly reports for some time. Poor data meant delays and wasted time. Reka La Roze, Change Manager at Unigestion, therefore sought an alternative solution. 

With the support of Evolution AI, the Unigestion team has now cut processing time in half and reduced operational costs by 75%. Importantly, the software can accurately extract data from previously unseen document layouts. 

Another critical difference that separates this AI from traditional OCR is its learning capabilities. True AI learns after being corrected, which means data accuracy increases over time and mistakes are never repeated.

“We saved 75% of our costs, the data quality is excellent and time to completion was halved. It's impressive.” — Cyril Kirschwing, Head of Change and IT Solution Architecture at Unigestion

Read our full quarterly reports case study to learn more. Or, if you’d like to see how our IDP technology works, you can book a demo here.