Evaluate customer records and recommend payment plans, based on earnings, savings data, payment history, and purchase activity.
Work task
“Evaluate customer records and recommend payment plans, based on earnings, savings data, payment history, and purchase activity.” is a supplemental task performed by Credit Analysts. Among the occupation's 11 rated tasks, workers place it 4th by importance (#8 most important). About 65% of workers say it is relevant to their job.
This is a single occupation-specific task statement from O*NET. The figures below describe how central the task is to the job and what independent studies measure about AI and this kind of work — not a prediction that the task will be automated.
Work activities this task rolls up to
O*NET groups concrete tasks into broader work activities shared across many occupations.
AI exposure
The OpenAI / Eloundou “GPTs are GPTs” study rates this task E2. Exposure with tools — software built on top of a language model (not the model alone) could cut the time by at least half.
Exposure measures whether a model could meaningfully speed the task up — it is an estimate of overlap with model capabilities, not a measure of whether the work will be done by software. The study's intermediate score (β) for this task is 0.50. Automation potential label: T3.
Other tasks in this occupation
- Analyze credit data and financial statements to determine the degree of risk involved in extending credit or lending money. · importance 4.9
- Complete loan applications, including credit analyses and summaries of loan requests, and submit to loan committees for approval. · importance 4.8
- Prepare reports that include the degree of risk involved in extending credit or lending money. · importance 4.5
- Generate financial ratios, using computer programs, to evaluate customers' financial status. · importance 4.5
- Analyze financial data, such as income growth, quality of management, and market share to determine expected profitability of loans. · importance 4.5
- Compare liquidity, profitability, and credit histories of establishments being evaluated with those of similar establishments in the same industries and geographic locations. · importance 4.3
- Contact customers to collect payments on delinquent accounts. · importance 4.0
- Review individual or commercial customer files to identify and select delinquent accounts for collection. · importance 3.3
- Confer with credit association and other business representatives to exchange credit information. · importance 3.1
- Consult with customers to resolve complaints and verify financial and credit transactions. · importance 3.1
See all tasks on the Credit Analysts page.
Sources for this page
Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.
- O*NET 30.3 U.S. Department of Labor / National Center for O*NET Development
- “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130 OpenAI / academic
Data compiled June 2, 2026. Figures are estimates, not advice.
Cite this page
Singulariki. "Evaluate customer records and recommend payment plans, based on earnings, savings data, payment history, and purchase activity.." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130. Accessed June 7, 2026. https://singulariki.com/tasks/task-1252
Singulariki. (2026). Evaluate customer records and recommend payment plans, based on earnings, savings data, payment history, and purchase activity.. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/tasks/task-1252
@misc{singulariki-task-1252,
title = {Evaluate customer records and recommend payment plans, based on earnings, savings data, payment history, and purchase activity.},
author = {{Singulariki}},
year = {2026},
note = {O*NET 30.3; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130. Accessed June 7, 2026},
url = {https://singulariki.com/tasks/task-1252}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.