Last Update: 3/13/2026
Your role’s AI Resilience Score is
Median Score
Changing Fast
Evolving
Stable
This reflects the reliability of your score based on the number of data sources available for this career and how closely those sources agree on the outlook. A higher confidence means more consistent evidence from labor experts and AI models.
What does this resilience result mean?
These roles are undergoing rapid transformation. Entry-level tasks may be automated, and career paths may look different in the near future.
AI Resilience Report for
They help businesses get paid by contacting customers who owe money and arranging payment plans to settle overdue bills.
This role is changing fast
The career of Bill and Account Collectors is changing fast because many routine tasks, like sending reminders and updating records, are now handled by advanced software and automated systems. AI tools are being used to schedule calls and send the right messages, which boosts efficiency and repayment rates.
Read full analysisLearn more about how you can thrive in your career
Learn more about how you can thrive in your career
This role is changing fast
The career of Bill and Account Collectors is changing fast because many routine tasks, like sending reminders and updating records, are now handled by advanced software and automated systems. AI tools are being used to schedule calls and send the right messages, which boosts efficiency and repayment rates.
Read full analysisContributing Sources
We aggregate scores from multiple models and supplement with employment projections for a more accurate picture of this occupation’s resilience. Expand to view all sources.
AI Resilience
AI Resilience Model v1.0
AI Task Resilience
CareerVillage's proprietary model that estimates how resilient each occupation's tasks are to AI automation and augmentation
Microsoft's Working with AI
AI Applicability
Measures how applicable AI tools (like Bing Copilot) are to each occupation based on real usage patterns
Anthropic's Observed Exposure
AI Resilience
Based on observed patterns of how Claude is being used across occupational tasks in real conversations
Will Robots Take My Job
Automation Resilience
Estimates the probability of automation for each occupation based on research from Oxford University and other academic sources
Althoff & Reichardt
Economic Growth
Measured as "Wage bill" which is a long term projection for average wage × employment. It's the total labor income flowing to an occupation
Medium Demand
We use BLS employment projections to complement the AI-focused assessments from other sources.
Learn about this scoreGrowth Rate (2024-34):
Growth Percentile:
Annual Openings:
Annual Openings Pct:
Analysis of Current AI Resilience
Bill & Account Collectors
Updated Quarterly • Last Update: 2/17/2026

What's changing and what's not
Many routine collection tasks are already handled by computers. For example, the official job summary (O*NET) lists duties like “record information about financial status” and “monitor overdue accounts using computers and a variety of automated systems” [1]. In real life, collectors use software and automated dialers to send reminders and update records.
The U.S. Bureau of Labor Statistics (BLS) notes that advanced software and automated calling lets collectors handle far more accounts with the same number of people [2]. In one study, an AI system that learned when to schedule collection calls achieved about 23% higher repayment rates than human agents [3]. Despite these gains, the most personal tasks (like explaining options or understanding a person’s situation) still need humans.
Chatbots or robots can give simple answers or reminders, but BLS and industry experts agree: collection work will still need real people to talk through complicated cases [2] [3].

AI in the real world
Lenders are optimistic but cautious about using AI in collection. On the plus side, the tools are available and often effective. Consultants observe that many customers now prefer digital communication, so companies use machine learning and automation to send the right message at the right time [4].
McKinsey analysts note that building a “digital-first” contact strategy can be very cost-effective – the technology investment is a small fraction of the payoff, because it brings in much more recovered money [4]. Indeed, the study on call scheduling showed clear gains with AI [3].
On the cautious side, collection is a regulated, people business. Collectors only make around \$22 per hour on average (about \$46K/year), so companies compare that cost to expensive AI systems. Because of legal rules about how and when people can be called, firms must still supervise automated calls.
In practice, BLS projects about a 10% decline in bill-collector jobs through 2034 [2], reflecting steady use of technology but also ongoing demand to replace retiring workers. In short, money-saving AI will be adopted where it clearly helps and follows rules, but the human skills of explaining, trusting, and negotiating remain important parts of the job [4] [3].

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Median Wage
$46,040
Jobs (2024)
166,900
Growth (2024-34)
-10.5%
Annual Openings
13,700
Education
High school diploma or equivalent
Experience
None
Source: Bureau of Labor Statistics, Employment Projections 2024-2034
AI-generated estimates of task resilience over the next 3 years
Answer customer questions regarding problems with their accounts.
Confer with customers by telephone or in person to determine reasons for overdue payments and to review the terms of sales, service, or credit contracts.
Notify credit departments, order merchandise repossession or service disconnection, and turn over account records to attorneys when customers fail to respond to collection attempts.
Advise customers of necessary actions and strategies for debt repayment.
Contact insurance companies to check on status of claims payments and write appeal letters for denial on claims.
Persuade customers to pay amounts due on credit accounts, damage claims, or nonpayable checks, or to return merchandise.
Trace delinquent customers to new addresses by inquiring at post offices, telephone companies, credit bureaus, or through the questioning of neighbors.
Tasks are ranked by their AI resilience, with the most resilient tasks shown first. Core tasks are essential functions of this occupation, while supplemental tasks provide additional context.

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