AI for Private Equity and Investment Firms

The growth of Private Equity deals is propelling global mergers and acquisitions. At the end of 2021, global PE has seen deals worth $2.1trillion. In this highly competitive industry, every effort to stay ahead of the competition is as important as ever.


The adoption of AI for Private Equity and Investment firms is one of the key drivers to improving proposition and enterprise value. According to a PWC survey, AI development in PE and PE-backed firms are among the highest compared to other industries. And of those who have deployed AI, 31% are leading the pack.

AI has portfolio company applications from financial sector to consumer and retail. Speeding up legal, commercial and financial due diligence, providing time and cost efficiencies to investments. It is also driving back-office efficiencies in HR, IT support, cybersecurity, automation and data aggregation resulting in cost savings and faster, more effective decision making. Front-end functions include analysis and predicting customer trends.

In a survey conducted by PWC on PEs AI strategy, priority includes increasing productivity, creating stronger product and service innovation, and improving revenue growth.

Top Goals of adopting AI for Private Equity

AI Adoption Strategy Priority for Private Equity
Lifted from PWC Survey: AI Predictions, Private Equity

How PE-backed firms can utilise AI

PE firms often positively transform the companies that they acquire. PE Firms can help their acquired or interested portfolio companies with:

Machine Learning and Predictive Analytics slash forecasting time and effort by up to 80%, whilst improving accuracy and consistency and even facilitating out-of-cycle re-forecasting.


This is achieved by using historical data for autonomous predictive modelling, executive and SME overlays, and visualisation with adjustments, resulting in consistent forecast scenarios and demand for products with accurate seasonality and consideration for every important predictive factor.


Predictive models are customised to factor in elements internal and external to your organisation including demographics and global economic factors.


A personalised customer experience is an invaluable strategy to increase sales, improve customer experience, and boost loyalty. Enabling personalised marketing analytics allows data to empower marketing executives, sales team, and retail specialists to get the most out of customer information.


PE-backed firms can create customer profile, data-driven customer personas to cluster customers and understand their purchasing behaviours. With an ML-based predictive model, understand the customer’s most likely next action. Coupled with personalised marketing analytics, effectively push multi-channel offers and communications where and when the customer is most active.


Key to a PE’s success in transforming a business is to make sure it has a clear picture of the business strength, weaknesses and potentials. There is no better source of these insights other than customer feedback. Customer experience and engagement analytics provides PEs a platform to analyse these feedbacks and establish necessary strategies to turn around results and outcomes.


Enable marketing and customer engagement teams to analyse customer experience across all sites and all competitor’s sites in a single dashboard. Actively identify and address issues before they escalate and surface a new metric for customer experience and marketing effectiveness.


Transform data from these sites into useful insights and predictors. Apply Machine Learning and Text Analytics (NLP) to transform comments to understand sentiment and conversation topics (e.g. Price Rating, Staff Rating, or even topics specific to your organisation like security rating). Augment data with additional attributes for mapping, visualisation and additional insights. Combine with marketing data and understand cause and effect.


Organisations typically make errors between 0.5% and 2% on their accounts payable transactions due to financial leakage, fraud and more. Large individual transaction volumes and the permeation of fraud and processing errors demands secure, scalable solutions, able to handle growing transaction data estates and the need for unprecedented levels of monitoring and testing.


Modern transaction analytics augments operations with super-human memory, processing power and insights to generate timely, consistent, deliberate and reliable analytics for a greater chance at success. Monitoring can be run off-site (for separation of duties) or deployed internally to trusted teams.


It is extremely useful in analysing all transaction data; not just sample testing. Highlighting recoverable savings including duplicate invoices and financial leakage through fraud, overpayments, and GST. Identify areas for controls, training, and process improvement. Identify potential fraud (e.g.ghost employees, fictitious entities, conflict of interest, invoice splitting) whilst using a third party to eliminate the risk of internal data manipulation.


Improving safety ratings, minimising risk, and preventing incidents is an important factor affecting cost savings, employee safety and operational efficiency. Through AI, PEs can predict OH&S incidents and minimise risks of portfolio firms. Advanced predictive analytics can determine the likelihood of workplace incidents after significant overtime or surrounding sick and annual leave. This significantly improves the safety of employees and customers, especially in roles that involve physical labour, trades, travel, vehicle operators and all non-desk work.


Similarly, the likelihood of workplace incidents occurring during certain activities, weather events, environmental conditions, time of year and many other potentially predictable scenarios can be assessed by industry so that mitigation strategies can be put in place to reduce the likelihood of incidents. With predictive analytics and optimisation identify hazards and establish dynamic risk profiles to improve working conditions and immediately intervene potential risks.


By creating an integrated and holistic safety program using artificial intelligence and machine learning, have real-time and dynamic safety net to protect employees and assets.


Portfolio Monitoring and Proactive Evaluation

Day to day management of portfolio companies used to be a challenge for most Private Equity firms. Mostly due to lack of access to quality real-time data, delivered through traditional methods of reporting and manual analysis.


Data-driven portfolio monitoring is possible as PEs embrace data cloud platform and employed integration of analytics platform. With these platforms strong data governance, particularly with regard to how information is accrued, stored, analysed, and reported PEs have gained the confidence of managing data access.


Through AI, PEs now have digitized data management, reporting and analysis. This has substantially improved efficiency and accuracy of analysis of portfolio company business drivers, reporting financial performance, KPIs, environment, sustainability and governance (ESG) reporting, and automation of tax and financial statements.


Cost Reduction through Optimisation and RPA

Many PE firms have a 3-7 year horizon for owning the organization and would then look to monetize their investment thus the importance of reducing cost.  


By employing Robotic Process Automation (RPA) and Intelligent Process Automation (IPA), entities can automate traditional manual, repetitive processes. This helps organisations to scale the business in alignment with the PEs expectations.


RPA and IPA remove the redundancy of duplicate data entry into different systems. Using RPA, data processes become more accurate and predictable, and, as a result, able to allocate human intervention for more significant tasks such as analyzing and interpreting data.


RPA also reduces human error which is associated to high costs. PRA and IPA improve efficiency and increases productivity, providing employees available time and resources to focus on resolving higher priority concerns and looking for ways to innovate.


In addition, the benefits associated with RPA are very easy to quantify thus providing PE firms with a predictable, high value, low risk return for RPA based initiatives.


The innovation of AI technologies is becoming a differentiating value especially in private equity and investment firms. PE companies need to constantly reassess operation and opportunities, so as AI matures it continue to deliver solutions and support that will evolve with the equity and its portfolio’s requirements.


AI Consulting Group can help Private Equity Firms find AI solutions for every business level. Speak to our management consultant to learn more.

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