Christian Davis, associate partner at commercial data solutions provider JMAN Group, explains how data is transforming value creation and compliance in private equity.
In the past, the private equity (PE) value equation was relatively straightforward thanks to low interest rates and cheap capital. Investors typically wanted visibility into core financial trends.
However, the current economic climate has changed this. With soaring interest rates and M&A volumes down, valuations have tumbled, bringing uncertainty for the market. In turn, competition for desirable potential investment opportunities has intensified and it has become riskier for PE sponsors to depend solely on traditional methods to optimise their respective portfolios. Further, the speed of change that the latest AI developments are causing in markets is creating a far greater likelihood that a good investment could turn bad during a holding period.
This is redefining decision-making in investment as Investment Committees demand an even greater level of granularity before backing investment theses that might previously have been considered safe bets. As a result, it’s become increasingly important that managers – while still relying on their experience, network and intuition – are able to support their investment strategy with detailed insights and analytics. Here, the remit is to improve the robustness of their due diligence and enhance the equity narrative by prioritising and quantifying value creation and return opportunities.
This, in turn, is redefining the role of PE compliance. It means compliance and legal departments at PE sponsors will need the expertise to evaluate and generate insights delivered via data science and AI about their portfolio companies to inform their risk assessments and to support ongoing monitoring of their compliance efforts.
Compliance and risk mitigation
As to the technical impact of machine learning and AI on PE sponsors’ compliance and risk mitigation efforts, a host of interesting use cases are beginning to emerge for how data analytics and AI can mitigate risks faced by PE firms:
- Automated Due Diligence: AI-driven processes can review and compare a prospective investment’s company policy against existing and proposed regulations to identify potential red flags.
- Legal Research: Users can quickly use AI tools to identify relevant case law, statutes and legal precedents that can be used in compliance, litigation and dispute resolutions. Also, more advanced AI solutions will eventually even be able to analyse past case outcomes to predict the likely success of a legal strategy.
- Real-Time Compliance Tracking: PE firms can leverage machine learning to detect unusual patterns in transactions or operations to indicate non-compliance with, for example, anti-money laundering laws, data privacy regulations and industry specific rules.
- Investor Reporting: Sponsors may be able to eventually use AI tools to improve the accuracy and timeliness of reports delivered to their LPs, which could help improve overall transparency and accountability while also ensuring all reporting meets regulatory standards.
Broader efficiencies from AI – particularly from the automation of repetitive, administrative tasks – will more generally reduce the cost of legal services. That will enable more PE firms – particularly emerging managers – to avail themselves of legal services. Indeed, the general quality of legal services should improve as routine tasks (e.g., manual compliance reporting, or using AI to read compliance documentation and automatically generate reports to review) are handled without the risk of human error, which would afford legal counsel more time to focus on higher-value activities.
Getting the data foundations in place
But, as with all exciting disruptions, the increased reward is mirrored by increased risk; while the commercial opportunities to leverage data and analytics may be vast, privacy and ethics remain a big concern along with the scope for bias and misinformation
So, what is the best course of action for legal professionals within PE firms seeking to navigate a dynamic regulatory environment to ensure their firms’ AI models adhere to privacy laws and maintain an ethical approach?
GCs and other legal counsel may need to undertake significant upskilling and data education to enable them to, at a minimum, understand the fundamentals of how their firms’ AI models operate. Data education is also critical for being able to integrate results, recognise biases and more effectively apply the insights that AI produces.
Without these skills, legal and compliance professionals will find themselves entirely reliant on their data or information technology departments. Such a situation is inherently dangerous because it creates a scenario where a legal team has to accept the insights that are delivered to them at face value without having the ability to ascertain whether all relevant information has been taken into account.
The scope of upskilling should ensure a sound understanding of everything from how AI is changing compliance and legal services and what data needs to be collected through to general data management and analytics.
It is important to remember too that AI is constantly evolving and changing – so continued investment in training and upskilling is vital to keep pace.
Superior investment strategy
Against the digital backdrop and evolving market dynamics, we will continue to see investors demand more agile innovative approaches to value creation and compliance.
To achieve this, firms, and their legal and compliance teams, must ensure they have the data knowledge and foundations in place to keep pace with the rapid evolution of data analytics and the opportunity to enhance decision making and provide a superior investment strategy.
Christian Davis is an expert in advance data analytics/data science, engineering, data strategy and monetisation across private equity, pharmaceuticals, logistics, manufacture and software sectors.