Business

5 Best Practices for Operational Risk Management

Published

3 years ago

June 14, 2023

Managing risk prevents procedural failures from becoming tangible losses, like regulatory fines, penalties, and reputational loss. Operational risk management (ORM) protects your organization from potential threats and lessens the impact of an event, should one occur. This process involves detecting, analyzing, and mitigating risks, along with improving outcomes through better decisions.

Since risk is an inherent part of doing business, and human error is unavoidable, it’s necessary to have a strong operational risk management strategy.

Here are the 5 best practices for managing operational risk in your company.

Use risk management software

Workiva highlights how an operational risk management tool is the first thing you need to successfully manage risk. It can be extremely difficult to thoroughly assess and mitigate risk manually because there are far too many nuances and details to track. Plus, some tools provide automation to support your needs. The right tool will provide you with a plethora of financial reporting options, compliance integrations, and will connect your data from multiple sources to make your risk-based decisions more accurate.

These days, manual data management is nearly impossible. When it comes to key risk indicators (KRIs), you can’t afford to make mistakes. By using an operational risk management tool, you’ll reduce preventable oversights and mistakes, which will help you better manage risk.

Accept risk only when the benefits outweigh the potential cost

Unnecessary risks don’t provide significant value to a goal. It’s never a good idea to take on unnecessary risk because the cost can be devastating. Unfortunately, many people, especially entrepreneurs, have a personal bias that distorts judgment and limits critical analysis.

What makes a risk unnecessary? It’s not the level of the risk that determines whether it’s worth taking, but rather, the potential benefits. Your organization might be fine taking on high risk if the benefits will outweigh the cost, both financially and otherwise.

Regardless, all major risks should be cleared by senior management and stakeholders first.

Address risk at the appropriate level

Decisions will be made at every level across your organization, so make sure risk decisions are made by the right people. For instance, employees shouldn’t be making decisions that have the potential to seriously impact the company, and managers need to ensure their employees have a strong understanding regarding how much risk they can bear and when to escalate a situation to a higher-up.

Plan ahead for remediation

Part of operational risk management involves planning. The decision makers in your organization should be incorporating ORM into business processes, which requires time and resources. However, this should be part of every planning and execution phase.

Categorize and prioritize your risks

You’ll need to categorize and prioritize your risks to get a good idea of what actions you should take and decisions you should make. This is done with a control matrix in five basic steps:

Identify your risks before conducting your assessments
Measure risk probability
Assess the potential impact
Calculate total risk
Update your control matrix accordingly

Within your risk control matrix, you’ll be prioritizing risks from the following categories:

People risk. These are risks caused by people and human resources management. For example, hiring the wrong people, improper training, unmotivated team members, and high turnover rates often result in errors, fraud, and other ethical actions that can harm your organization.
Systems risk. When internal systems fail, losses can be devastating. This can include the loss of backups, downtime for networks, and other technical errors.
Process risk. When internal business processes are inadequate, your business can suffer. This includes things like product design flaws and failure to meet project deadlines or deliver projects to a client’s specifications.
External events risk. These risks are out of your control, like storms, floods, hurricanes, fires, and even manmade problems like robberies, terrorist attacks, and wars.
Legal compliance risk. When your business fails to comply with internal and external compliance regulations, the risks are great. These issues often involve tax and financial accounting regulations, internal ethical codes of conduct, and any other regulations imposed by a regulatory body governing your industry.

Operational risk management is critical for success

There are many ways to make a business successful, but if you don’t manage risk, one error or incident can tear down all your hard work. The best way to manage risk is to avoid it whenever possible. However, you can’t avoid all risk, and that’s where strategic risk management comes into play. Choose the risk you’re willing to accept, mitigate the potential consequences, and continue fine-tuning your decision-making process to respond better to similar risks in the future.

Business

AI in Asset Management Explained: How Leading Firms Apply It

Published

2 days ago

April 16, 2026

Matthew Wiles

AI in asset management explained at its most basic level is this: using machine learning, data modeling, and automation to make faster and more accurate investment decisions. The applications vary widely across asset classes, fund strategies, and operational functions. Understanding where AI creates real value separates productive adoption from expensive experimentation.

Asset managers now face a data environment far larger than any human team can process manually. Market signals, company filings, macroeconomic indicators, alternative data sources, and portfolio monitoring all generate information continuously. AI tools process that information at scale. They surface patterns that traditional analysis would miss or find too late.

AI in Asset Management Explained Across Core Investment Functions

AI delivers the most measurable results when applied to specific investment functions rather than deployed as a general capability. The clearest applications sit in portfolio construction, risk management, and credit analysis.

Portfolio Construction and Factor Modeling With AI

Traditional portfolio construction relies on return and correlation assumptions built from historical data. AI-driven portfolio tools go further. They process real-time market data, alternative signals, and macroeconomic inputs simultaneously. This surfaces factor exposures that static models miss.

Machine learning models in portfolio construction can:

Identify non-linear relationships between asset classes that correlation matrices do not capture
Adjust factor weightings dynamically as market conditions shift rather than on a quarterly rebalancing schedule
Flag concentration risks before they appear in standard risk reports
Model tail scenarios using a broader range of historical stress periods than traditional value-at-risk models allow

James Zenni, founder and CEO of ZCG with over 30 years of capital markets experience, has built the platform’s investment approach around the principle that better data and faster analysis produce better outcomes. That view shapes how AI capabilities get deployed across ZCG’s private equity, credit, and direct lending strategies.

Credit Analysis and Private Markets AI Applications

Credit analysis in private markets has historically depended on periodic financial reporting and relationship-based deal intelligence. AI changes that model. Lenders using machine learning tools now monitor borrower health continuously rather than waiting for quarterly covenant tests.

Specific credit applications include:

Cash flow pattern analysis that identifies revenue deterioration weeks before it shows up in reported financials
Supplier and customer relationship mapping that flags single-source dependencies and concentration risks
Covenant monitoring automation that tracks hundreds of credit agreements simultaneously and alerts teams to early warning signs
Loan pricing models that incorporate current market spread data and comparable transaction history

These capabilities compress the time between identifying a problem and taking action. In credit, that time advantage directly affects loss rates and recovery outcomes.

AI in Asset Management Explained Through Risk and Compliance Applications

Risk management and regulatory compliance represent two of the highest-value AI applications in asset management. Both functions involve processing large volumes of structured and unstructured data under time pressure.

How AI Transforms Risk Monitoring in Asset Management

Traditional risk monitoring produces reports at set intervals. AI-powered risk systems run continuously. They flag anomalies in position data and monitor correlated exposures across a portfolio. Alerts fire when market conditions shift beyond defined thresholds.

The practical risk management applications include:

Real-time portfolio stress testing against live market inputs rather than end-of-day snapshots
Liquidity modeling that accounts for position size relative to market depth across multiple scenarios
Counterparty exposure monitoring that aggregates risk across instruments, custodians, and trading relationships
Regulatory reporting automation that reduces manual preparation time and lowers the risk of filing errors

ZCG applies these capabilities across its approximately $8 billion in AUM. The platform was founded 20 years ago. It built its investment infrastructure around systematic data analysis and operational discipline.

AI for Operational Efficiency in Asset Management Firms

Beyond investment decisions, AI delivers significant value in fund operations. Back-office functions like reconciliation, reporting, and compliance documentation consume substantial resources at most asset management firms.

AI tools applied to fund operations include document processing systems. These extract and verify data from offering documents, side letters, and subscription agreements automatically. Reconciliation tools flag breaks between custodian records and internal systems automatically. Investor reporting platforms generate customized materials from structured data inputs, reducing the manual production time significantly.

ZCG Consulting (“ZCGC”) advises operating companies across more than a dozen sectors on operational improvement programs, including technology-driven process redesign. Those operational efficiency principles translate directly to asset management back-office functions.

Applying AI to Asset Management: Limitations Firms Must Address

AI in asset management explained fully must include the limitations. Models trained on historical data perform poorly when market regimes change. Overfitting produces tools that work in backtests but fail in live environments. And AI outputs require experienced interpretation to avoid acting on statistically significant but economically meaningless signals.

The ZCG Team approaches AI adoption with the same discipline it applies to investment underwriting. Every tool requires a defined use case and a measurable success metric. A review process keeps experienced judgment in the decision chain. That framework prevents the common failure mode where AI adoption generates activity without improving outcomes.

Firms that treat AI as a capability layer on top of sound investment processes generate sustainable advantages. Those that treat AI as a replacement for process discipline find the technology amplifies existing weaknesses. It rarely corrects them.