Welcome to the Experian Thought Leadership Hub

Recoveries hinge on knowing who can pay, not just who owes. This infographic explores how debt buyers and collections agencies can better identify signals of true payment capacity.

Did you know?

• U.S. household debt surpassed $18 trillion in Q3 2025.¹
• The average age of debt in collections is ~3.5 years.²
• The average recovery rate on delinquent accounts is ~20%.³

Read the infographic for more insights and strategies to drive smarter recoveries.

¹Household Debt and Credit report, Q3 2025, Federal Reserve Bank of New York.

²Debt Collection Industry Statistics, zipdo.

³Collections Industry Statistics, Gitnux.

Learn how you can move beyond single-point employee benefit solutions and embrace a more holistic approach that combines:

• Credit education to help employees understand and take control of their credit health
• Financial wellness tools to support budgeting and day-to-day financial confidence
• Identity protection to safeguard what employees have worked hard to build

The credit marketing landscape is evolving. As competition intensifies and consumer expectations rise, financial institutions that can predict, personalize and perform at scale will define the next generation of success.

In this webinar, our experts explore how financial institutions can use connected intelligence to find, know and grow the right customers, leading to:

• Smarter acquisition
• Stronger customer retention
• More proactive growth

As the credit card landscape evolves, understanding how consumers and businesses are adapting has never been more important.

Our second annual State of Credit Cards Report provides a timely update on key market dynamics and dives into an often-overlooked segment within credit card portfolios: business accounts.

Key takeaways:

• 30+ DPD delinquency rates remained above pre-pandemic levels in 2025.
• Fintechs continue to gain share in the credit card market, posting a 71% YoY increase in account originations.
• Many businesses use consumer credit cards to support business-related activity, making this segment easy for lenders to overlook.

Download Experian’s 2026 Future of Fraud Forecast to explore five fraud trends expected to have the biggest impact on businesses and consumers in the coming year, including: 

• Agentic AI and machine-to-machine fraud 
• Deepfake-driven employment fraud 
• Smart home device exploitation 
• Website cloning and emotionally intelligent fraud bots

The Regional Economic Health Tracker examines consumer health across the four U.S. census regions. It highlights how regional dynamics, from data center growth to housing costs and credit conditions, are shaping household finances today.

• Regional income, spending, and cost-of-living trends
  
• Housing market performance and price changes
  
• Consumer stress and 30+ DPD balances by product

As market conditions shift, collections success depends on continuous, high-quality consumer data. This checklist highlights how always-on collections intelligence can help debt buyers and agencies:

• Stay ahead of consumer behavior shifts
• Adjust strategies quickly
• Concentrate collections efforts where it counts

ITIN holders are active, responsible participants in the U.S. credit economy — yet many remain overlooked by traditional lending models.

Our white paper takes a closer look at this financially active and resilient population, revealing key insights into their credit performance and long-term growth potential.

Some findings include:

• Most ITIN holders are concentrated in states that represent a large share of the U.S. economy: California, Texas and New York.
• 76.9% of ITIN holders remained current on trades after 12 months, a rate 15% higher than SSN consumers.
• ITIN holders maintain a lower debt-to-income ratio (25%) than SSN consumers.

Read the full white paper for more insights.

This summary outlines important research findings that help lenders drive a more inclusive environment for customers, while mitigating risk.

A few takeaways:

• Understanding the profiles of who’s more prone to exclusion informs risk management.
• Machine learning techniques can also be deployed to help detect economic interaction effects or shifts.
• Attributes like education level, occupation or rent/utilities payment history have predictive power in credit outcomes.