Tariffs are a crucial element of international trade policy that have shaped economic relationships between nations for centuries. This primer will explore the nature of tariffs, their purposes, and their effects on global commerce.
What Are Tariffs?
Tariffs are taxes imposed on imported goods when they cross national borders. The most common type is an import tariff, which is levied on products brought into a country. While export tariffs exist, they are rare, and in the case of the United States, they are constitutionally prohibited.
Purpose of Tariffs
Tariffs serve several key functions:
1. Revenue Generation: Historically, tariffs were a significant source of government income, especially before the widespread adoption of income taxes.
2. Industry Protection: By making imported goods more expensive, tariffs can shield domestic industries from foreign competition.
3. Addressing Unfair Practices: Tariffs can be used to counteract subsidies or dumping by foreign countries that artificially lower the price of their exports.
4. National Security: Some tariffs are implemented to protect industries deemed crucial for national security.
Types of Tariffs
There are several types of tariffs:
- Ad Valorem Tariffs: Set as a percentage of the import's value
- Specific Tariffs: A fixed amount charged per unit of imported goods
- Tariff-Rate Quotas: Tariffs that increase significantly after a certain import quantity is reached.
Who Pays for Tariffs?
Contrary to popular belief, tariffs are primarily paid by the importing businesses and, ultimately, passed on to consumers in the form of higher prices. This means that when a country imposes tariffs, its own citizens often bear the cost.
Historical Context
The United States has a long history with tariffs, dating back to the country's founding. Tariffs were a primary source of government revenue until the 1930s when income taxes became more prominent. After World War II, there was a general trend towards reducing tariffs globally as part of efforts to promote international trade.
Recent Developments
In recent years, tariffs have regained prominence as a policy tool. For example:
- In 2018, the United States imposed tariffs on various goods, including steel, aluminum, solar panels, and washing machines.
- These actions led to retaliatory measures from other countries, including Canada and the European Union.
- More recently, in 2025, the U.S. implemented new tariffs on imports from Canada, Mexico, and China, citing national security concerns related to immigration and drug trafficking.
Economic Impact
The economic effects of tariffs are complex and often controversial:
- Consumer Costs: Tariffs typically lead to higher prices for consumers.
- Domestic Industry: While some industries may benefit from protection, others that rely on imported inputs may suffer.
- Global Trade: Extensive use of tariffs can lead to reduced international trade and potential economic slowdowns.
- Retaliation: Trading partners often respond with their own tariffs, potentially escalating into trade wars.
Conclusion
Tariffs remain a powerful but contentious tool in international trade policy. While they can protect domestic industries and generate revenue, they also risk increasing consumer prices and sparking international trade disputes. As global economic relationships continue to evolve, the debate over the use and effectiveness of tariffs is likely to persist.
Insolvency cases in Canada have seen a notable rise in 2024, reflecting the growing financial challenges facing both consumers and businesses across the country. The trends highlight mounting economic pressure, driven by a combination of high interest rates, inflation, and other external factors.
Rising Consumer Insolvencies
Consumer insolvencies have reached significant levels in 2024, with the third quarter of the year seeing 34,588 filings. This marks a 13.5% increase compared to the same period in 2023, a concerning sign of rising financial distress among Canadians. Ontario, in particular, has experienced the sharpest rise, with a 20.2% increase in consumer insolvencies year-over-year, totaling 13,140 filings in the third quarter alone.
Business Insolvencies at a 15-Year High
Business insolvencies have also surged in 2024, with 1,312 businesses filing for insolvency in the third quarter. This represents the highest third-quarter volume since the 2009 Great Recession, indicating that companies are facing severe financial challenges. The first quarter of 2024 saw an even more dramatic rise in business insolvencies, with an 87.2% increase compared to the same quarter in 2023, marking the sharpest jump in 37 years.
A total of 2,003 businesses filed for insolvency in the first quarter, the highest volume since the 2008 financial crisis. Over the 12-month period ending March 31, 2024, business insolvencies increased by 56.7% compared to the previous year, reflecting the tough economic climate.
Small and Medium-Sized Businesses Hit Hardest
Small and medium-sized businesses (SMBs) have been particularly affected by these rising insolvencies. Unlike larger corporations, many SMBs lack the resilience and access to capital necessary to weather economic downturns. As a result, these businesses are struggling to survive in an environment characterized by higher debt-carrying costs and declining consumer demand.
The Factors Behind the Surge
Several factors are contributing to the surge in insolvencies in both the consumer and business sectors. High interest rates and inflation have placed significant financial strain on individuals and companies alike, making it more difficult to manage existing debt and navigate increased living costs. The end of pandemic-related government support programs has also left many without the safety net they relied on during the height of the COVID-19 crisis.
Additionally, new debt obligations, such as the repayment of CEBA (Canada Emergency Business Account) loans, have added further financial pressure on businesses, pushing many over the edge into insolvency.
A Continuing Trend of Financial Struggles
As 2024 continues, the financial strain on both individuals and businesses in Canada shows no signs of abating. With the combination of rising interest rates, higher inflation, and the loss of government support, many Canadians and businesses are finding it increasingly difficult to manage their financial obligations. This trend paints a picture of ongoing financial challenges for the foreseeable future.
Operational and Strategic Red Flags: A Closer Look at CVS and Lessons for Credit Professionals
Operational and strategic decisions made by a company can offer critical insights into its financial health. While changes in business direction can reflect innovation and adaptability, they often mask underlying distress. This section focuses on two key red flags observed in CVS’s operations—store closures and aggressive cost-cutting measures—and their implications for creditors and analysts.
In recent years, CVS announced the closure of hundreds of retail locations as part of its strategy to pivot away from traditional retail operations and focus on health services, such as walk-in clinics and pharmacy benefits management. This move was framed as a way to "streamline operations" and adapt to changing consumer behaviors.
Large-scale store closures can signal more than just strategic realignment. For creditors, this raises red flags about potential overextension or failure to sustain profitable operations in certain markets. Restructuring often reflects deeper financial or operational inefficiencies, and such closures may not immediately lead to cost savings due to associated expenses like lease terminations, severance payments, and asset write-offs.
· Announcements of closures: Regular press releases or earnings calls discussing closures, divestitures, or layoffs.
· Strategic pivot rationale: Examine whether restructuring aligns with long-term strategy or is a reactive measure to cash flow constraints.
· Impact on revenues: Monitor subsequent earnings reports to gauge whether closures result in improved margins or continued revenue decline.
Other Examples
Companies like Sears and J.C. Penney pursued similar closure strategies in their decline. While their aim was cost-saving, it highlighted weakening market positions, strained resources, and a failure to compete effectively with e-commerce competitors like Amazon.
CVS implemented several cost-saving programs across its operations, including layoffs, reduced capital expenditures, and operational efficiency initiatives. While these actions were part of a broader restructuring, they also hinted at potential liquidity concerns.
For a company of CVS’s scale, drastic cost-cutting can signify immediate financial pressures or attempts to meet short-term targets at the expense of long-term value creation. This may erode employee morale, hinder innovation, and compromise service quality, potentially damaging brand equity and customer loyalty.
· Public filings: Look for restructuring charges, asset sales, or deferred investments in SEC filings and earnings reports.
· Operational capacity: Assess whether cost-cutting leads to reduced customer satisfaction or operational inefficiencies.
· Competitor analysis: Compare CVS’s strategies with peers like Walgreens or Amazon Pharmacy to understand market dynamics and whether CVS’s approach is defensive or proactive.
· General Electric (GE) aggressively reduced costs in the 2010s, only to find itself struggling to maintain market share and product innovation.
· WeWork slashed expenses during its post-IPO turmoil, reflecting cash flow problems and difficulty maintaining its valuation narrative.
1. Scrutinize restructuring announcements: These can indicate underlying financial distress, even when labeled as strategic pivots.
2. Evaluate cost-cutting in context: Temporary savings that compromise long-term sustainability are concerning for creditors.
3. Track industry trends: Operational shifts may reflect broader market challenges rather than company-specific weaknesses.
By closely monitoring these red flags, creditors can better understand the risks involved in extending credit to companies like CVS and proactively safeguard their interests.
Case Study: CVS Health – Early Financial Warning Signs for Credit Managers
Introduction
In recent months, CVS Health has faced mounting financial pressures, presenting credit managers with a real-world case study in identifying early warning signals of client risk. For those with a vested interest in CVS’s financial health, such as suppliers and other stakeholders, understanding these indicators is critical for maintaining sustainable credit practices. This article explores CVS’s financial red flags, highlighting what credit managers should watch for and referencing insights from a recent analysis shared in the memo to the CFO, available on the CIC blog site.
Background on CVS Health
CVS Health has long held a prominent position in the healthcare and retail industries, expanding its operations to include healthcare services, retail pharmacy, and insurance through its acquisition of Aetna. This diversification strategy placed CVS at the forefront of integrated healthcare and retail services but also led to a significant increase in debt and operational costs. As the company works to balance its vast operational scope with fiscal responsibility, credit managers should examine key indicators that point to potential risks.
Section 1: Financial Health Signals—What Was Visible Early On?
While CVS’s financial challenges became more apparent over time, certain financial health signals were evident early on and could have served as indicators for those extending credit. Key financial warning signs included a mounting debt load, shrinking profit margins, and erratic cash flow patterns. Here’s a breakdown of each signal and its relevance to credit risk:
1. Increased Debt Load
2. Profit Margin Compression
3. Cash Flow Variability
Key Takeaways for Credit Managers
By analyzing CVS’s financial health indicators, credit managers can derive valuable lessons applicable to other high-risk clients:
Understanding these financial warning signs is essential for managing credit risk effectively. By recognizing early signals like debt spikes, margin compression, and cash flow variability, credit managers can proactively adjust credit terms, set safeguards, and prepare for potential payment delays. For an in-depth analysis of CVS’s financial standing and additional recommendations for credit managers, see the memo to the CFO on the CIC blog site.
Next week, we’ll explore the operational and strategic red flags credit managers should watch for, using CVS as an ongoing example of navigating risk in today’s complex financial landscape.
As AI becomes an integral part of business operations, understanding the building blocks of AI—data, algorithms, machine learning (ML), and neural networks—enables professionals, including those in B2B credit, to see how they can support and leverage this technology in their work. Here’s a closer look at these foundational elements and how B2B credit professionals, especially those working for companies that extend credit to other businesses, can actively contribute to each component.
For a deeper dive into how B2B credit professionals can shape AI and drive smarter risk analysis, read the full article on our private blog. Join the conversation and share your thoughts—your insights are invaluable as we explore the future of AI in credit!
Data: The Foundation of AI
Data is the core of any AI model. The quality and relevance of data directly impact how well AI performs its tasks. For B2B credit professionals, this means focusing on collecting and managing high-quality data related to customer behavior, payment histories, industry trends, and macroeconomic factors that influence credit risk.
How B2B Credit Professionals Can Contribute:
- Data Collection: Ensure accurate, comprehensive data collection on clients, payment behavior, industry credit trends, and financial performance metrics. This might include setting up systems to capture customer credit application details, transaction histories, and payment timelines.
- Data Standardization: To make data useful for AI models, it should be structured and standardized. Credit professionals can work with data teams to define consistent formats and categories for financial records, risk ratings, and client information.
- Data Enrichment: Beyond internal records, supplementing data with external sources (e.g., economic indicators, industry reports, news) can help enhance the AI model's predictive power.
- Data Quality Assurance: Regularly audit and clean data to remove duplicates, correct errors, and ensure consistency. High-quality data reduces biases and inaccuracies, resulting in more reliable AI predictions.
Algorithms: The "Recipe" of AI
Algorithms are sets of rules that guide AI in processing information. In credit risk analysis, algorithms can determine how the AI system identifies patterns in creditworthiness, payment timelines, and potential risks.
- Collaborate on Algorithm Development: Work closely with data scientists to help define the credit-specific factors that should be included in risk algorithms. For instance, credit professionals may recommend weighting certain variables (e.g., payment history, debt-to-income ratio) more heavily based on industry experience.
- Define Credit Risk Variables: Credit professionals bring industry insights that can help identify key metrics and ratios (like DSO, DBT, and current ratio) relevant to assessing client creditworthiness. Their input can enhance algorithms by making them more industry-specific.
- Identify Patterns and Anomalies: By analyzing real-world credit cases, credit professionals can share insights on unique risk indicators, helping data scientists fine-tune algorithms to capture subtleties like seasonality in payments or red flags in financial reports.
Machine Learning (ML): Teaching AI to Improve Over Time
Machine learning, a subset of AI, involves training models with data so they can make predictions and improve over time. For instance, ML models in B2B credit could predict client payment delays based on past payment patterns or assess the risk of default based on current financial conditions.
- Train AI Models: Provide labeled data that the AI can learn from. For instance, tagging historical client data as “high risk” or “low risk” helps train the ML model to distinguish between different risk levels.
- Support Model Validation: Once an AI model is trained, it needs to be validated on new data to ensure accuracy. Credit professionals can assess and validate model outputs, identifying if the predictions align with actual outcomes and adjusting the model as needed.
- Feedback Loop Creation: Continuously provide feedback to the AI system. By sharing insights on false positives/negatives or unusual risk cases, credit professionals can help improve the model's accuracy over time.
Neural Networks and Deep Learning: Mimicking Human Decision-Making
Neural networks and deep learning models allow AI systems to handle complex, multi-dimensional data—important in credit risk analysis where multiple factors and their interactions impact decisions.
- Participate in Complex Model Building: Offer insights into complex credit scenarios that involve numerous variables, helping shape neural networks that capture non-linear relationships. For example, neural networks might analyze how a company’s revenue growth rate interacts with its debt levels to predict default risk.
- Assist in Multi-Layer Analysis: Credit professionals can suggest multi-layered data points to be included in the model, such as combining payment history with broader market signals, which neural networks can use to generate more comprehensive insights.
- Contribute to Scenario Analysis: Provide scenarios from past credit evaluations to help neural networks learn how to handle nuanced cases. This can improve the model's ability to make predictions across various credit conditions.
Practical Steps for B2B Credit Professionals
To contribute effectively, B2B credit professionals should consider a few practical actions:
1. Work Closely with Data Teams: Establish strong communication with data scientists and IT departments to align on data needs, feedback on model accuracy, and continuous improvements.
2. Promote a Data-Driven Culture: Emphasize the importance of data quality within the credit team and advocate for data literacy, ensuring team members understand the value of accurate data for AI insights.
3. Stay Informed on AI and ML Trends: Take part in training and workshops on AI basics, data science, and ML to understand how AI could be applied in their work area, making collaboration with data teams more effective.
4. Experiment with Predictive Models: Start small by testing predictive models on lower-risk accounts or specific data sets to assess accuracy, identify gaps, and gain confidence before scaling AI usage.
5. Measure and Report AI Success: Track the success of AI-driven initiatives, like reductions in default rates or faster risk assessments, and share these results within the organization to promote AI adoption.
Final Thought: A Collaborative Future
As AI becomes more integral to credit risk analysis, B2B credit professionals play a crucial role in shaping the technology to meet industry needs. By focusing on data quality, contributing to algorithm development, and assisting with model validation, credit professionals can help build smarter AI systems that support better credit decisions, manage risk effectively, and ultimately drive business growth.