Maximising private market returns through strategic portfolio monitoring

Due diligence checklist private equity

Synergising AI and human intelligence – the future of compliance management

Upholding Integrity, Transparency, and Investor Trust

Compliance has evolved from a back-office function into a strategic pillar of trust, transparency, and long-term value creation in the markets.
As global investors demand greater accountability and regulators impose stricter disclosure standards, maintaining robust compliance frameworks is now fundamental to operational excellence and capital stewardship.

Why ESG Matters in Private Markets: A Growing Imperative

The growing emphasis on Environmental, Social, and Governance (ESG) factors is no longer confined to public equities or listed corporations — it is rapidly transforming the private markets landscape as well. Between 2025 and 2030, ESG integration in private equity, venture capital, and private debt is expected to evolve from an optional feature to a core performance and risk management framework.

As investors demand more transparency and accountability, and as regulators strengthen sustainability disclosures, private market participants must align investment practices with ESG principles to remain competitive, attract capital, and unlock long-term value.

1. The Structural Shift in Private Capital

Private markets — historically focused on returns and growth metrics — are now being reshaped by a structural shift toward sustainability-driven value creation.
Institutional investors, sovereign wealth funds, and development finance institutions are setting ESG mandates for fund managers, linking capital deployment to measurable sustainability outcomes.

Key drivers include:

• Limited partners (LPs) demanding ESG reporting and impact measurement.

• Fundraising advantages for firms with credible ESG credentials.

• Policy alignment with global initiatives such as the UN Sustainable Development Goals (SDGs) and Paris Agreement.

This evolution signifies that ESG is no longer a compliance formality — it has become a decisive factor in capital formation and investor trust.

2. ESG as a Tool for Value Creation and Risk Mitigation

Integrating ESG into private market strategies enhances both value creation and risk management.
Environmental efficiency, social inclusivity, and strong governance directly correlate with operational resilience and exit valuations.

Key impacts include:

• Operational efficiency: Reducing energy use, waste, and carbon footprint improves cost structures and sustainability ratings.

• Regulatory readiness: ESG alignment mitigates risks related to policy shifts, carbon pricing, and compliance penalties.

• Talent and culture: Companies with inclusive practices and transparent governance attract better leadership and retain top talent.

• Exit premiums: Buyers increasingly favor ESG-compliant assets, commanding higher valuations during IPOs or secondary sales.

In essence, ESG not only safeguards investments but also enhances long-term financial performance.

3. Private Equity: From Screening to Active Stewardship

Private equity firms are evolving from passive ESG screening to active stewardship and transformation.
Leading general partners (GPs) now integrate sustainability across every stage of the investment lifecycle:

StageESG Integration FocusDue DiligenceAssess environmental risks, labor practices, governance quality, and climate exposure.OwnershipImplement decarbonization strategies, diversity initiatives, and board transparency.MonitoringTrack ESG KPIs through digital dashboards and annual sustainability reports.ExitEnhance value by demonstrating ESG impact and resilience.

Firms that embed ESG into their operational playbooks are not only future-proofing their portfolios but also differentiating themselves in an increasingly competitive fundraising environment.

4. Venture Capital and the Rise of Impact Innovation

The venture capital (VC) ecosystem is also undergoing a paradigm shift as impact-oriented startups attract increasing investor interest.
Between 2025 and 2030, the convergence of technology and sustainability — in fields such as climate tech, circular economy, and digital inclusion — will define the next wave of venture opportunities.

Emerging VC trends include:

• Climate tech funds focusing on carbon capture, renewable materials, and green data.

• Inclusive innovation addressing social equity and access to education or healthcare.

• AI and data transparency solutions enhancing ESG measurement accuracy.

This shift represents a dual opportunity — capturing financial upside while contributing to measurable global impact.

5. The Data and Reporting Challenge

Despite momentum, ESG integration in private markets faces structural challenges, particularly around data availability and standardization.
Unlike public companies, private firms often lack mandatory disclosure requirements, making it difficult to evaluate ESG performance consistently.

To address this gap, leading investors are:

• Adopting digital due diligence platforms to collect and analyze ESG data in real time.

• Collaborating on industry frameworks such as ILPA’s ESG Data Convergence Initiative.

• Leveraging AI-driven scoring systems to benchmark portfolio companies.

As these tools mature, private markets will achieve greater transparency, comparability, and accountability, unlocking deeper trust and investor participation.

6. Regional Perspectives: The Globalization of ESG Standards

The ESG imperative in private markets is spreading beyond developed economies to include emerging markets, where sustainability-linked investments are driving inclusive growth.

Examples include:

• Southeast Asia: Growth in green private equity and Islamic sustainable finance.

• Middle East: ESG integration in sovereign-backed venture and infrastructure funds.

• Africa: Focus on renewable energy access, fintech inclusion, and sustainable agriculture.

This globalization of ESG standards reinforces the notion that responsible investing is both a moral and economic necessity.

7. Strategic Outlook: ESG as a Source of Competitive Advantage

By 2030, ESG integration will distinguish market leaders from laggards in the private investment ecosystem.
Funds that embrace transparent reporting, digital ESG analytics, and purpose-driven strategies will attract superior deal flow, institutional partnerships, and long-term returns.

In this evolving paradigm, ESG is not merely a framework — it is a strategic asset class that defines credibility, resilience, and global relevance in private markets.

Digital Transformation in Private Equity: Leveraging AI, Data, and Automation

As competition intensifies and investor expectations rise, digital transformation has become a defining feature of the modern private equity (PE) ecosystem. Between 2025 and 2030, firms that successfully integrate artificial intelligence (AI), data analytics, and automation into their investment and operational frameworks will gain a decisive edge in speed, precision, and performance.

Digital tools are no longer just operational enhancements — they are strategic enablers that elevate due diligence, portfolio monitoring, and ESG integration to new levels of sophistication and reliability.

1. The New Digital Paradigm in Private Markets

Private equity has traditionally relied on manual processes, fragmented data sources, and personal networks. The next generation of PE firms is embracing digitization as a core driver of efficiency, transparency, and scalability.

Key priorities include:

• Automating due diligence using AI-powered research and data extraction tools.

• Enhancing decision-making through predictive analytics and risk modeling.

• Centralizing portfolio intelligence via cloud-based management systems.

• Improving LP (Limited Partner) reporting through real-time dashboards and digital compliance tracking.

This transformation enables firms to react faster, deploy smarter, and scale globally with greater agility.

2. AI-Powered Due Diligence and Deal Sourcing

Artificial intelligence is revolutionizing how private equity identifies and evaluates investment opportunities.

AI systems can now:

• Analyze vast datasets to detect market trends and target companies ahead of competitors.

• Assess financial health, governance, and ESG indicators from structured and unstructured data.

• Conduct sentiment analysis on management teams and media coverage to identify reputational risks.

• Predict future growth potential based on sectoral and behavioral analytics.

This data-driven approach reduces the reliance on intuition and manual research, allowing investors to make faster, evidence-based investment decisions.

3. Automation and Process Efficiency

Automation technologies are streamlining labor-intensive tasks that once consumed significant time and resources.
Back-office processes such as report generation, regulatory filings, investor onboarding, and data reconciliation are being automated, freeing human capital for higher-value strategic work.

For example:

• Robotic Process Automation (RPA) handles repetitive data management and compliance updates.

• AI chatbots assist with LP inquiries and fund documentation.

• Smart contract platforms enable secure, automated execution of capital calls and distributions.

The result is a leaner, faster, and more cost-efficient investment operation — crucial for scaling assets under management (AUM) while maintaining accuracy and compliance.

4. Data Analytics for Portfolio Optimization

Advanced analytics provide private equity firms with deep insights into portfolio performance, operational risks, and value-creation levers.
Through integration of machine learning models, firms can predict financial outcomes, identify underperforming assets early, and simulate various exit scenarios.

Key benefits include:

• Real-time monitoring of portfolio company KPIs.

• Enhanced forecasting accuracy for cash flow and returns.

• Benchmarking ESG and financial metrics against industry peers.

• Data-backed decision-making in restructuring or strategic repositioning.

In an increasingly data-rich world, analytics turn complexity into clarity, helping GPs deliver measurable value to LPs.

5. Digital ESG Monitoring and Impact Measurement

Digital transformation is also enabling transparent and continuous ESG tracking across private market portfolios.

Innovative platforms now use AI, IoT, and blockchain to:

• Collect real-time data on emissions, resource usage, and labor conditions.

• Automate ESG scoring using standardized taxonomies (e.g., EU SFDR, GRI).

• Record sustainability milestones in tamper-proof digital ledgers.

• Provide investors with verifiable ESG dashboards for reporting and audits.

This evolution ensures integrity, accuracy, and accountability in ESG performance measurement — essential for maintaining investor confidence and regulatory compliance.

6. Cybersecurity and Data Governance

As private markets become increasingly digital, data protection and cybersecurity have emerged as top priorities.
Sensitive financial, operational, and personal data must be safeguarded through advanced encryption, secure cloud infrastructure, and robust data governance frameworks.

PE firms are implementing:

• Zero-trust cybersecurity models.

• AI-driven threat detection to identify vulnerabilities.

• Regulatory compliance frameworks (GDPR, ISO 27001) to protect investor information.

Strong data governance not only mitigates risk but also enhances reputation and trustworthiness — vital assets in private markets.

7. Strategic Outlook: Technology as the Next Competitive Advantage

Between 2025 and 2030, the most successful private equity firms will be those that combine digital intelligence with human insight.
AI and automation will handle data-heavy operations, while human expertise will focus on strategic creativity, negotiation, and relationship-building.

This synergy will redefine competitive advantage, enabling firms to:

• Identify opportunities faster than traditional players.

• Deliver measurable ESG impact with digital precision.

• Scale operations globally without sacrificing governance or quality.

In this new digital era, technology is not replacing the investor — it is amplifying the investor’s intelligence.

AI and ESG Integration: The Future of Smart Investment Intelligence (2025–2030)

The convergence of artificial intelligence (AI) and ESG analytics marks a defining evolution in global investment strategy. Between 2025 and 2030, AI will become the core engine of sustainable decision-making, enabling investors to integrate financial performance, environmental responsibility, and social impact into a unified intelligence framework.

As regulatory expectations rise and data complexity increases, AI’s ability to process vast datasets, detect hidden patterns, and forecast outcomes transforms ESG from a static reporting obligation into a dynamic, predictive discipline that drives long-term value creation.

1. From Static Metrics to Dynamic ESG Intelligence

Traditional ESG assessments rely heavily on historical and self-reported data, often updated annually and prone to inconsistencies.
AI transforms this process by converting ESG into a real-time intelligence system, capable of continuous monitoring and predictive analysis.

AI-enabled platforms can now:

• Integrate data from satellites, IoT sensors, and corporate disclosures to measure real-world environmental performance.

• Detect supply chain violations or environmental risks through natural language processing (NLP) of news and regulatory data.

• Update ESG scores dynamically, reflecting current risk exposure rather than static averages.

This evolution allows investors to make timely, data-driven ESG decisions, reducing information asymmetry and enhancing portfolio resilience.

2. AI in Environmental Analytics

Environmental data is among the most complex to collect and interpret.
AI provides powerful tools for quantifying and predicting carbon emissions, resource consumption, and climate-related risks across industries and geographies.

Key applications include:

• Carbon footprint modeling using satellite imagery and energy usage data.

• Climate risk forecasting through machine learning models that simulate extreme weather impacts.

• Biodiversity and land-use monitoring for companies in agriculture, mining, and real estate.

These insights support more accurate valuation of transition and physical climate risks, enabling investors to price sustainability into their capital allocation models.

3. Social and Governance Insights through Machine Learning

AI is equally transformative in assessing the social and governance dimensions of ESG — areas often difficult to measure quantitatively.

Machine learning models analyze vast, unstructured data to detect signals such as:

• Labor practices and diversity metrics in recruitment and retention data.

• Corporate culture and leadership behavior through employee reviews and sentiment analysis.

• Governance strength by evaluating executive compensation patterns, shareholder rights, and board independence.

By linking behavioral data to governance performance, AI allows investors to quantify soft risks that were previously qualitative, improving early detection of reputational and compliance issues.

4. Predictive ESG Scoring and Risk Forecasting

AI systems are redefining ESG scoring by shifting from retrospective evaluation to forward-looking prediction.
Instead of merely ranking companies based on past actions, AI predicts future ESG performance trajectories based on behavioral, financial, and policy indicators.

For investors, this means:

• Early identification of improving or deteriorating ESG performers.

• Better portfolio diversification based on sustainability outlooks.

• Dynamic rebalancing aligned with evolving risk and opportunity profiles.

Predictive ESG models help investors capture alpha from transformation — investing early in companies positioned for sustainable growth.

5. Integration into Smart Investment Platforms

The future of investment intelligence lies in fully integrated AI-ESG ecosystems, where financial analytics, sustainability data, and market forecasts operate within a unified platform.

Such systems will:

• Merge ESG metrics with financial performance indicators to assess total enterprise value.

• Automate compliance reporting under global sustainability standards (e.g., ISSB, SFDR, CSRD).

• Generate custom ESG insights for each asset, sector, or geography.

• Provide real-time decision dashboards to fund managers and institutional investors.

These integrated platforms represent the evolution from manual analysis to smart investment ecosystems, enhancing agility and transparency.

6. Ethical AI and Governance Challenges

As AI assumes a larger role in investment decision-making, ensuring ethical governance and transparency becomes critical.
ESG-driven AI systems must adhere to clear standards on data integrity, algorithmic fairness, and explainability.

Core priorities include:

• Bias mitigation to ensure fair and inclusive decision-making.

• Algorithmic transparency in how ESG scores are generated.

• Data security and privacy in sensitive sustainability datasets.

• Regulatory alignment with AI governance frameworks emerging in the EU, U.S., and Asia.

Balancing automation with accountability will determine how trusted and effective AI-driven ESG systems become in institutional investment environments.

7. Strategic Outlook: The Rise of Smart Sustainable Capital

By 2030, AI-integrated ESG analytics will become the global standard for responsible and intelligent investing.
Investors will use unified data ecosystems to evaluate not only returns but also resilience, impact, and integrity — creating a new class of “smart sustainable capital.”

This next phase of financial evolution represents a shift from reactive compliance to proactive intelligence, where investment strategies are guided by data-driven sustainability foresight.
Firms that adopt these systems early will stand at the forefront of the AI-sustainability revolution, capturing both financial and ethical advantage in a rapidly transforming global economy.

Maximising Private Market Returns Through Strategic Portfolio Monitoring

The private markets landscape has undergone a structural transformation between 2024 and 2026. Investors are no longer satisfied with passive ownership or annual performance reviews — they demand continuous insight, active value creation, and strategic risk control. In this environment, portfolio monitoring has evolved from a compliance necessity into a competitive advantage that directly drives return optimisation.

Strategic portfolio monitoring enables private equity, venture capital, and infrastructure investors to extract greater value from their holdings, anticipate market shifts, and align capital deployment with evolving macroeconomic and sustainability trends.

1. From Static Reporting to Active Intelligence

Traditional portfolio monitoring relied on periodic financial statements, offering a lagging view of performance. Today, digital transformation and data integration have made it possible to achieve real-time portfolio visibility.

Modern portfolio monitoring platforms leverage:

• Automated data collection from portfolio companies and external sources.

• AI-driven analytics to track operational KPIs, liquidity positions, and ESG metrics.

• Predictive performance modeling to identify early signals of value erosion or growth acceleration.

This active intelligence empowers general partners (GPs) and limited partners (LPs) alike to make faster, data-backed strategic decisions, enhancing agility in volatile markets.

2. Value Creation Through Continuous Insight

Private market investors are increasingly judged not by capital deployment, but by their ability to create operational alpha.
Effective portfolio monitoring transforms data into action by revealing performance levers such as revenue growth drivers, margin efficiency, and capital structure optimization.

Examples include:

• Detecting underperforming assets early to initiate turnaround strategies.

• Identifying cross-portfolio synergies for cost reduction and innovation sharing.

• Aligning management incentives with real-time performance metrics.

This continuous insight model reduces the reaction time between problem identification and intervention, preserving and enhancing enterprise value throughout the investment lifecycle.

3. The Role of Technology and Automation

Digital monitoring tools are now integral to private market performance.
AI, machine learning, and cloud-based analytics are redefining how GPs and LPs collect, interpret, and act on data.

Key technological enablers include:

• Machine learning algorithms that forecast cash flows, IRR evolution, and exit potential.

• Automated anomaly detection in financial and operational data to highlight irregularities.

• Dashboards integrating ESG and financial KPIs, giving investors a holistic view of impact-adjusted returns.

These systems reduce manual reporting burdens while improving precision, transparency, and accountability — enabling investors to focus on strategic value creation rather than administrative oversight.

4. ESG and Risk Integration in Portfolio Oversight

In the new investment paradigm, sustainability performance is financial performance.
Integrating ESG metrics into portfolio monitoring ensures that returns are sustainable, compliant, and resilient to regulatory or reputational shocks.

Strategic ESG integration enables:

• Tracking of carbon exposure, diversity metrics, and governance standards.

• Identification of regulatory risks before they affect asset valuation.

• Alignment with LPs’ sustainable investment mandates.

By embedding ESG intelligence into the monitoring process, investors achieve a dual outcome — superior risk-adjusted returns and long-term value preservation.

5. Data Transparency and LP–GP Alignment

The next frontier of private market excellence lies in transparency and collaboration.
Limited partners increasingly demand near-real-time visibility into portfolio performance and risk exposures.
Advanced portfolio monitoring systems facilitate this through secure digital dashboards and customizable analytics, enabling GPs to maintain investor confidence and strengthen partnerships.

Such transparency also enhances fundraising capability, as investors favor managers who can demonstrate data-driven discipline and operational oversight.

6. Strategic Forecasting and Exit Optimization

Portfolio monitoring isn’t just about tracking performance — it’s about shaping exit outcomes.
By leveraging predictive analytics, investors can identify the optimal window for value realization, assess market timing, and design strategic exits.

AI-powered monitoring platforms enable:

• Scenario modeling to simulate exit valuations under varying market conditions.

• Liquidity forecasting to align distributions with fund commitments.

• Comparative benchmarking across sectors and regions to pinpoint outperforming assets.

This strategic foresight converts portfolio monitoring from a passive tool into a profit-maximizing engine.

7. The Road Ahead: Intelligent Portfolio Orchestration

As private markets expand in complexity, the future belongs to investors who combine data mastery, digital infrastructure, and active oversight.
Strategic portfolio monitoring represents the bridge between technology and human judgment — where analytics empower, but experience decides.

The next generation of investors will operate within intelligent portfolio ecosystems, where data flows seamlessly, risks are preempted, and returns are continuously optimized.
In this landscape, monitoring becomes orchestration — a discipline that harmonizes capital, performance, and purpose to achieve sustained outperformance in the private markets of tomorrow.

Due Diligence Checklist for Private Equity

Due diligence is the foundation of value creation and risk mitigation in private equity. It ensures that investment decisions are based on verified, comprehensive, and contextual intelligence rather than assumptions or incomplete data.
A robust due diligence framework covers financial, operational, legal, commercial, and ESG dimensions, aligning investment strategies with long-term value and compliance objectives.

The following checklist outlines key areas private equity firms should assess before finalizing any acquisition or investment.

1. Financial Due Diligence

Objective: Evaluate the target company’s financial health, historical performance, and forward-looking sustainability.

Key checkpoints:

• ✅ Historical financial statements: Review audited reports for the past 3–5 years.

• ✅ Revenue and profit trends: Analyze growth patterns, seasonality, and margin stability.

• ✅ Cash flow analysis: Assess liquidity, working capital cycles, and capital expenditure requirements.

• ✅ Debt and leverage structure: Examine existing liabilities, covenants, and repayment schedules.

• ✅ Tax compliance: Verify tax filings, potential liabilities, and pending audits.

• ✅ Forecast validation: Stress-test financial projections against market assumptions.

Digital tools: AI-driven financial modeling platforms and anomaly detection systems to identify inconsistencies or manipulation.

2. Commercial Due Diligence

Objective: Understand market dynamics, competitive positioning, and revenue scalability.

Key checkpoints:

• ✅ Market size and growth: Quantify total addressable market (TAM) and emerging demand drivers.

• ✅ Competitive landscape: Map competitors, pricing strategies, and differentiation factors.

• ✅ Customer analysis: Evaluate customer concentration, churn rate, and retention metrics.

• ✅ Sales pipeline and backlog: Assess revenue visibility and order quality.

• ✅ Supply chain resilience: Identify dependencies and potential disruption risks.

• ✅ Pricing power: Analyze ability to maintain margins under inflationary or competitive pressure.

Digital tools: AI-enabled market intelligence dashboards and predictive analytics for market share and pricing trends.

3. Operational Due Diligence

Objective: Evaluate internal capabilities, cost efficiency, and scalability of business operations.

Key checkpoints:

• ✅ Organizational structure: Review management hierarchy, decision-making flow, and key personnel.

• ✅ Operational KPIs: Benchmark productivity, utilization rates, and cost per output unit.

• ✅ IT infrastructure: Assess system integration, cybersecurity, and digital maturity.

• ✅ Supply chain and logistics: Review supplier dependencies, logistics costs, and resilience.

• ✅ Capital expenditures: Analyze asset maintenance, renewal needs, and efficiency potential.

• ✅ Integration readiness: Assess post-acquisition integration risks and synergies.

Digital tools: Digital due diligence platforms using data ingestion and process mining to identify operational bottlenecks.

4. Legal and Regulatory Due Diligence

Objective: Ensure compliance, mitigate legal exposure, and validate ownership rights.

Key checkpoints:

• ✅ Corporate structure: Verify ownership, subsidiaries, and cross-border entities.

• ✅ Contracts and obligations: Review supplier, customer, and employment agreements.

• ✅ Litigation and disputes: Assess active or potential legal cases.

• ✅ Regulatory compliance: Confirm licensing, permits, and sector-specific regulations.

• ✅ Intellectual property: Validate patents, trademarks, and ownership rights.

• ✅ Insurance coverage: Evaluate adequacy and claims history.

Digital tools: Document analytics and AI contract review platforms to accelerate legal screening and risk tagging.

5. Human Capital and Management Due Diligence

Objective: Assess leadership quality, talent retention, and organizational culture.

Key checkpoints:

• ✅ Leadership capability: Review management track record and governance style.

• ✅ Employee turnover and morale: Analyze HR metrics and engagement surveys.

• ✅ Compensation structures: Evaluate alignment with performance and retention goals.

• ✅ Succession planning: Identify leadership continuity and bench strength.

• ✅ Cultural alignment: Ensure fit with investor values and post-acquisition vision.

Digital tools: Sentiment analysis and talent analytics to evaluate organizational health and cultural risks.

6. ESG and Sustainability Due Diligence

Objective: Integrate environmental, social, and governance factors into investment assessment.

Key checkpoints:

• ✅ Environmental impact: Review carbon footprint, waste management, and resource efficiency.

• ✅ Social responsibility: Examine labor practices, diversity, and community engagement.

• ✅ Governance framework: Evaluate board structure, compliance, and transparency.

• ✅ ESG reporting and data quality: Verify disclosures and third-party certifications.

• ✅ Climate risk exposure: Model potential regulatory and physical risks.

Digital tools: ESG intelligence platforms that monitor sustainability metrics and benchmark performance.

7. Technology and Cybersecurity Due Diligence

Objective: Protect intellectual property, ensure business continuity, and evaluate digital maturity.

Key checkpoints:

• ✅ IT architecture: Assess scalability, interoperability, and technical debt.

• ✅ Data security: Review cybersecurity measures, vulnerabilities, and recovery plans.

• ✅ Digital strategy alignment: Evaluate technology’s role in achieving business objectives.

• ✅ Automation and AI adoption: Identify opportunities for digital value creation.

Digital tools: Cyber risk scoring systems and automated penetration testing platforms.

8. Exit Strategy and Value Realization

Objective: Define clear pathways for liquidity events and return maximization.

Key checkpoints:

• ✅ Exit options: IPO, secondary sale, strategic acquisition, or recapitalization.

• ✅ Timing strategy: Model market windows and optimal valuation conditions.

• ✅ Potential buyers: Map strategic and financial acquirers.

• ✅ Value creation roadmap: Quantify operational, digital, and ESG improvements.

Digital tools: Predictive valuation models and AI-driven scenario simulation for exit timing optimization.

9. Integration of Digital Due Diligence

Objective: Enable a data-driven, continuous due diligence process beyond pre-deal assessment.

Modern due diligence doesn’t end with the deal.
Post-investment monitoring, using digital due diligence platforms, ensures ongoing validation of assumptions and early identification of risks or opportunities.

Key features include:

• Automated data feeds from portfolio companies.

• Continuous ESG and risk tracking.

• AI-driven alerts for anomalies and compliance breaches.

• Dynamic dashboards for LP transparency.

This transforms due diligence from a static process into a living intelligence framework, continuously supporting value creation and risk management.