Business

From Startup to Success: How Venture Debt Can Help Your Business Grow

Published

3 years ago

August 1, 2023

A new kind of funding is on the upswing for startups — venture debt. According to the US Chamber of Commerce, now that venture capital is drying up, “companies of all sizes look to raise more expansion capital via this alternative form of financing.”

As success stories proliferate of entrepreneurs using this funding solution in their early stages, interest in it naturally increases. Yet, venture debt isn’t the right choice for every business.

“Venture debt can open up exciting opportunities, but the decision to take on these loans is complex,” says Jay Jung, founder and managing partner of Embarc Advisors, a corporate finance advisory firm. “Problems can crop up when startups take on debt, so it’s important to weigh all aspects of this approach carefully.”

Venture debt explained

Venture debt is similar to other types of loans in that a business founder borrows money from the lender (usually an institutional bank, private investor, or fund that specializes in venture debt) and pays it back with interest over time. Companies that have already raised venture capital but are looking for more money to fuel their growth in-between equity rounds i.e., runway extension, typically use it.

“Venture debt provides funds with a short payback period — usually between 18 months and three years,” Jung says. “Lenders work with companies based on what makes sense for them at any given point in time.”

Venture debt helps businesses bridge funding gaps. “Startups are expensive,” Jung explains. “In their early days, most businesses need to spend time building their products or services while figuring out their go-to-market motion, so they usually don’t have a lot of revenue coming in. At the same time, they still need to pay the bills: employee salaries, rent on space, and other overhead.”

Indeed, as one recent study has discovered, “47% of startup failures in 2022 were due to a lack of financing.” For this reason, successfully securing venture debt can mean the difference between a company’s success and failure.

Venture debt also offers startups the ability to grow their business. “It can be a great option for any business looking to expand its operations, hire more employees and make strategic investments in technology or marketing,” Jung says.

Traditional versus venture debt

“Venture debt differs from traditional loans in a number of critical ways,” Jung says. “Traditional lenders look at a business’s past performance when determining whether or not to approve a loan. But for many startups, there isn’t a track record of past revenue. Plenty of new businesses operate in the red for years.”

For this reason alone, a traditional loan may be out of the question for some businesses.

“With venture debt, business owners can leverage the startup’s profitable future,” Jung explains. “While a traditional bank usually makes founders guarantee repayment by staking their personal property as collateral, founders can give venture-debt investors the right to purchase shares in the future, which is called a ‘warrant.’ In this way, they can use equity stakes to entice investors and other possible lenders.”

According to Jung, venture debt attracts investors because these loans tend to have higher interest rates than traditional loans. “In my experience, interest rates for venture-debt loans usually fall between 9 and 20 percent,” he says.

Options for venture debt

Startups have three options when it comes to venture debt. The first of these is term loans. “These operate much like traditional loans,” Jung says. “The lender loans the startup funds that must be repaid with interest after a certain period.”

Another option is revenue-based financing, which is paid back through a percentage of future revenue. “These loans can either be short- or long-term,” Jung says. “The important thing is that these startups need to have an established track record of generating revenue.”

The third option is factoring. “With factoring, the lender buys your accounts receivables for less than their face value,” Jung explains. “This gives the startup immediate funds, while the investor reaps the difference between their purchase price and the full amount of the bill.”

However, Jung urges caution with this method. “I’ve seen businesses get mired in situations in which they are never able to finish loans based on factoring,” he says. “They fall into a vicious cycle of relying on the factoring company and never actually get ahead, so the true cost of this approach can be a lot higher than it might first appear.”

Maximizing your success

The benefits of venture debt are numerous. Not only can these loans help you get your startup off the ground, but they can also give you the funds needed to grow as a company and expand into new markets. In the current environment where valuations have declined, extending runway through the use of venture debt may allow a company to grow back into its valuation and avoid a down-round. Still, employing this kind of funding successfully requires care.

“If you are interested in pursuing venture debt for your business, then do your due diligence,” Jung advises. “In particular, success will depend on accurately assessing your business’s needs, choosing the exact right financing option, developing a solid plan for repayment, and following it ruthlessly.”

While these steps may seem daunting, entrepreneurs who appreciate their difficulty may well be on the right track. This is one domain in which overconfidence could prove disastrous, but the good news is that — according to Jung — there’s a way to mitigate this risk.

“If you don’t have a lot of experience with corporate finance in general and venture debt in particular, then consider getting advice from a specialist,” Jung says. “With the help of an experienced advisor, you can be confident in choosing the right option and moving your company forward with the maximum chances of success. It’s important to remember that obtaining financing is only the beginning. Managing the finance post-funding is just as important.”

Business

AI in Asset Management Explained: How Leading Firms Apply It

Published

1 day 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.