Inflation.

It's a word that most of us in the U.S. despise, almost as much as the word taxes. It's probably because, like taxes, we can't escape its wallet-draining effect when it increases. Maybe the way we feel about it is because the last time the U.S. economy deflated—giving us relief from it—was in the 1930s, when "Prices dropped an average of nearly 7% every year between the years of 1930 and 1933," according to Investopedia. But I digress.

We won’t go into how inflation works, but how the government calculates it—and how its categories affect it—has always been consistent. At least it was until the COVID-19 pandemic hit, that is.

What NLP text mining reveals about the U.S. economy inflation-rate factors and the online conversations about them

To ensure we're on the same page about how we came to the forthcoming information in this use case, let's cover a couple of basics on NLP text mining and inflation rate indexes.

What are NLP and text mining?

Natural language processing (NLP), an A.I. technology, automates the data analysis of mined textual, unstructured data. It includes natural language understanding and natural language generation to simulate a human’s ability to create language, and it’s a component of text mining that performs a special kind of linguistic analysis by deep learning algorithms so a machine can “read” text. Apps like Grammarly or Wordtune analyze text to improve a written text, for example, and chatbots use this technology to interact with customers.
Text mining, or text analytics, is the process of examining big data document collections. It’s a computer science discipline that converts unstructured text data in documents and databases into normalized, structured data and datasets for analysis by machine learning models. Deep learning machine-learning algorithms then analyze this data, analyzing semantics and grammatical structures, to gain new insight or aid research from human language.
Together, NLP and text mining are like a search engine on steroids.

The Consumer Price Index (CPI)

According to this Forbes Advisor article, "The two most frequently cited indexes that calculate the inflation rate in the U.S. are the Consumer Price Index (CPI) and the Personal Consumption Expenditures Price Index (PCE)." For this article, however, we'll only use the Bureau of Labor Statistics (BLS) method of CPI inflation calculation as a reference.
CPI observes a specific group of commonly-purchased goods and services to gauge how prices fluctuate. These foods and services include:

• Apparel: Women's and men's clothes, jewelry, etc.
• Alcoholic beverages: Beers, wine, liquor, etc.
• Energy and commodities: Gasoline, natural gas, electricity, etc.
• Food: Items bought by the average consumer, such as breakfast cereal, milk, meat, fruits, vegetables, etc.
• Housing and shelter: Rent, housing insurance, bedroom furniture, hotel or motel accommodation costs, etc.
• Medical care services: Physicians' services, prescription drugs, medical supplies, etc.
• New and used vehicles: Trucks, vans, sedans, SUVs, etc.
• Tobacco and smoking products: Tobacco-related items, such as cigarettes, cigars, bidis, kreteks, loose tobacco, etc.
• Transportation services: Airline fares, vehicle insurance, etc.

NLP text-mining process: web mentions matched to CPI categories

Using SESAMm's web text analysis engine TextReveal®, we analyzed textual data relating to the inflation topic within the U.S. from 2017 until now. For this analysis, we defined co-mentions as the articles and social media posts that mention "inflation" and at least one of the CPI categories. Note: Although we can analyze more than 100 languages, we focused on English in this case. Also, we didn’t conduct a sentiment analysis from the information extraction.

From 2017 to 2019, inflation co-mentions within the U.S. are relatively stable (see Figure 1). But this trend changes with the first shift in 2020, continuing its rapid growth and peak by the end of 2021 due to this surge of inflation reaching record levels.

What was one of the main drivers of the inflation surge? Used cars.

3 used-car and inflation trends uncovered through NLP Text Mining

According to the U.S. Bureau of Labor Statistics, the cost of used vehicles was one of the main drivers of the inflation spike. How did used cars contribute to inflation? The chain of events occurred like so: The increased used-car demand was fueled by a new-vehicle supply shortage caused by a chip shortage generated by supply-chain interruptions due to the COVID-19 pandemic.

As the pandemic-induced supply-chain interruption unfolded, used-car trends developed. Here are three we found in our data mining research:

Trend 1: Co-mentions percentage for used vehicles more than doubled

Based on the percentage of co-mentions compared to other topics, the used-car topic moves from the number eight spot to the number four spot in 2021 (see Figure 2).

Before 2020, mentions were relatively steady. However, we observe an increase in used-vehicles mentions caused by disruptions in supply chains leading to chip shortages (see Figure 3) as early as January 2020. These shortages led to a decrease in new vehicle inventory. The Statista report, indicating an increase of the used vehicle value index by 49 points compared to the price index recorded in 2020, supports our findings.

Trend 2: Used vehicle prices rose with used-car co-mentions

Because of the pandemic, car production nearly stopped along with the sale of cars, which created two situations: 1. high inventory to sales ratio and 2. historically low car production (see Figure 4). Vehicles sales picked up later, but car production was still suffering because of supply-chain disruption. That meant the inventory to sales ratio dropped to virtually zero.

So consumers with little-to-no options for new vehicles turned to used cars, increasing their demand and therefore increasing their prices. We confirm this hypothesis with increasing mentions within the used-vehicles topic, coinciding with an inventory volume decrease. All in all, used-vehicle prices rose 40.5%.

Trend 3: The COVID-19 pandemic and new vehicle inventory shortage increased demand

A smaller new-vehicle inventory wasn't the only reason consumers sought out used vehicles. They also wanted used cars because of the pandemic.

For 2020, we observe that consumers avoided public transportation by rising co-mentions between pandemic-related mentions and the demand for secondhand vehicles (see Figure 5).

Used-car and inflation trends summary

We can summarize the used-car and inflation trends with one phrase: It's a used-car seller's market. For example, online retailers like Carvana have leveraged these factors to grow significantly. In contrast, due mainly to significant supply chain disruptions, motor companies have had the opposite effect, with the Automotive industry projected to lose $210 Billion. Judging by the number of mentions in public web forums and social media, the chip shortage and used-car boom affected General Motors, Ford, and Toyota the most (see Figure 6).

About SESAMm and TextReveal’s® NLP Text-mining Capabilities

SESAMm is a leading company in alternative data and artificial intelligence, delivering global investment firms and corporations descriptive, prescriptive, or predictive investment analytics worldwide. TextReveal is SESAMm's premiere NLP text-mining product, a solution that allows you to fully leverage NLP-driven insights and receive high-quality results through data streams, modular API and dashboard visualization, and signals and alerts. In other words, we organize, categorize, and capture relevant information from raw data for you.

READ MORE: Check out "Alternative Data Trends – How Reddit Helped Fuel The Great Resignation."

Ready to uncover the invisible data about your investments and target companies? Request a TextReveal demo today.

Denmark's Danske Bank has announced a sweeping divestment from fossil fuel investments, cutting its portfolio from 2,000 companies to just 270, representing an over 85% reduction in the number of companies in its fossil fuel investment universe. However, its overall exposure to the fossil fuel industry remains stable, thanks to increased investments in some fossil fuel companies. This move, implemented through its Danske Invest fund management unit and Danica pension and insurance business, marks one of the most significant climate-related portfolio adjustments by a major European financial institution.

Assessment Framework

The bank's new methodology centers on a dual evaluation approach. First, it examines management quality, analyzing how companies handle emissions and assess transition risks. Second, it evaluates carbon performance, scrutinizing emissions reduction targets and their alignment with Paris Agreement goals. This comprehensive framework ensures a thorough assessment of companies' climate commitments and actions.

Strategic Approach

Rather than implementing a complete divestment strategy, Danske Bank has adopted a more nuanced approach. While significantly reducing the number of investee companies, the bank has maintained its overall sector exposure by increasing investments in selected companies demonstrating strong transition plans. This strategy focuses particularly on businesses actively working to future-proof their operations against climate-related challenges.

Leadership Perspective

Erik Eliasson, Head of Responsible Investment at Danske Bank, emphasizes the customer-centric approach: "Our new fossil fuels investment approach aligns with the preferences of the majority of our customers while underscoring our commitment to achieving competitive returns on a responsible basis.” Thomas Otbo, CIO at Danske Bank Asset Management, reinforces this position, noting their commitment to maintaining investments in fossil fuel companies that reflect global economic realities while becoming more selective in their choices.

Implementation Flexibility

The bank has designed its strategy to accommodate diverse customer preferences. Some funds remain exempt from the new methodology, while others offer complete fossil fuel exclusion. This flexible approach allows Danske Bank to serve varying client needs while maintaining its broader commitment to sustainable finance. "We firmly believe this to be in the best long-term interest of our investment customers," adds Otbo.

Market Implications

This development signals a sophisticated evolution in how financial institutions approach fossil fuel investments. Rather than implementing blanket exclusions, Danske Bank's approach demonstrates how major financial institutions can balance climate responsibilities with financial returns. The strategy could set a new standard for the industry, showing how banks can significantly reduce fossil fuel exposure while maintaining strategic investments in energy transition leaders.

Looking Forward

As financial institutions face mounting pressure to address climate change, Danske Bank's approach offers a practical template for others in the industry. It demonstrates that major banks can significantly reduce fossil fuel exposure while maintaining their role in financing the global energy transition. This balanced strategy could become a model for other institutions seeking to align their portfolios with climate goals while ensuring continued financial performance.

The bank's innovative approach to fossil fuel investment suggests a new phase in sustainable finance, where sophisticated evaluation methods and flexible implementation strategies replace simple exclusion policies. This development may well shape the future of institutional investment in the energy sector.

SESAMm’s AI Technology Reveals ESG Insights

Discover unparalleled insights into ESG controversies, risks, and opportunities across industries. Learn more about how SESAMm can help you analyze millions of private and public companies using AI-powered text analysis tools.

Infrastructure is no longer a niche allocation. It sits at the center of energy transition strategies, industrial policy, and long-term portfolio construction. For investors, banks, and insurers, exposure to infrastructure projects also means exposure to complex, evolving ESG and reputational risks.

More than 250,000 infrastructure projects worldwide are monitored on the SESAMm platform. This coverage continues to expand, with new projects added regularly, including in response to client requests.

“Infrastructure projects generate vast amounts of fragmented information across local media, regulatory sources, and public reporting. Our goal is to transform that information into structured intelligence. With more than 250,000 projects covered globally, SESAMm provides investors and financial institutions with the visibility needed to monitor infrastructure risks at scale,” commented Sylvain Forté, CEO & Co-Founder, SESAMm. 

A Truly Global View of Infrastructure Risk

SESAMm analyzes sources in more than 100 languages and provides global visibility into infrastructure assets, including in emerging markets and jurisdictions with limited public disclosure. Through multilingual AI analysis, SESAMm monitors projects in their local information environments, detecting controversies, regulatory actions, environmental incidents, corruption cases, and governance failures as they emerge.

Whether a project is located in Europe, Southeast Asia, Sub-Saharan Africa, or Latin America, clients gain access to:

• Local-language media monitoring
• Structured ESG event detection
• Clear visibility into how events evolve over time
• Severity and exposure scoring

Infrastructure risk is rarely static. It evolves during permitting, construction, operation, and financing, and SESAMm’s monitoring reflects that reality.

Coverage Across Key Infrastructure Categories

SESAMm’s infrastructure coverage includes a wide range of asset types, such as:

• Solar Stations
• Airports
• Waste Management Facilities
• Wind Stations
• Dams
• Coal Mines
• Oil & Gas Plants
• Hydropower Stations
• Coal Power Stations
• Steel Plants
• Bioenergy Stations
• Nuclear Stations
• Coal Terminals
• Geothermal Stations

This breadth enables clients to assess risks across both legacy and transition-aligned infrastructure.

Why Infrastructure Monitoring Matters Now

Infrastructure projects concentrate risk. They involve long timelines, large capital commitments, public scrutiny, regulatory complexity, and significant community impact.

For investors and lenders, point-in-time due diligence is no longer sufficient. A project cleared at financial close can face protests, litigation, environmental incidents, corruption allegations, or regulatory breaches years later.

Continuous monitoring is becoming essential for:

• Pre-investment screening
• Ongoing portfolio oversight
• Secondaries transactions
• Infrastructure debt underwriting
• Insurance risk assessment
• Sustainability and SFDR reporting

Our expanded coverage supports these workflows with structured, real-time intelligence.

On-Demand Expansion: Coverage That Grows With You

Beyond the projects already included in the dataset, SESAMm adds new infrastructure assets upon client request. This allows coverage to align directly with a pipeline of acquisition targets, a lender’s financing book, an insurer’s underwriting portfolio, or a fund’s watchlist. Rather than forcing clients to adapt to a fixed database, SESAMm’s infrastructure coverage scales dynamically to meet their needs.

With more than 250,000 assets included, SESAMm delivers one of the most comprehensive and scalable datasets for infrastructure risk monitoring. Together, this expanded infrastructure dataset and SESAMm’s AI reporting capabilities provide financial institutions with a scalable way to identify and monitor risks across infrastructure portfolios worldwide.

Learn more about our AI reports here.