Installment Receivables Credit Loss Numerical Reasoning Dataset

#numerical reasoning #receivables aging analysis #credit loss rate calculation #allowance movement analysis #consumer installment finance #receivables risk analysis #credit loss allowance assessment

3600
0.2GB
JSON
CATL
MOBIUSI INC

Updated:2026-11-04

AI Analysis & Value Prop

Built around financial reports on consumer installment receivables, this dataset provides structured information and expert analysis examples covering aging, defaulted balances, charge-offs and recoveries, credit loss allowances, and period changes. Examples address calculations such as loss rates, net charge-off rates, and allowance roll-forward reconciliations, helping models learn to extract figures, apply formulas, and explain results. It is suited to consumer finance credit risk analysis, financial data understanding, and domain-specific model fine-tuning and evaluation.

Dataset Insights

Sample Examples

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Technical Specifications

Field	Type	Description
file_name	string	File name
recoveries	number	Amounts collected during the reporting period on receivables previously written off.
currency_code	string	Currency code for reported monetary amounts, such as USD.
reporting_period	string	Reporting period extracted from the source report.
closing_loss_rate	number	Closing loss allowance divided by total gross receivables as of the reporting date.
defaulted_balance	number	Balance identified as defaulted during the reporting period or at the reporting date, according to the source data.
gross_charge_offs	number	Receivables written off as uncollectible during the reporting period, before recoveries.
past_due_1_30_days	number	Receivables that are 1 to 30 days past due as of the reporting date.
analysis_conclusion	string	Concise interpretation of the aging profile, credit loss performance, allowance adequacy, and reconciliation results.
current_receivables	number	Receivables that are not past due as of the reporting date.
net_charge_off_rate	number	Gross charge-offs less recoveries, divided by average gross receivables for the period.
past_due_31_60_days	number	Receivables that are 31 to 60 days past due as of the reporting date.
past_due_61_90_days	number	Receivables that are 61 to 90 days past due as of the reporting date.
calculation_reasoning	string	Step-by-step explanation of extracted inputs, formulas, calculations, and assumptions.
credit_loss_provision	number	Provision recognized during the reporting period to adjust the loss allowance.
past_due_over_90_days	number	Receivables that are more than 90 days past due as of the reporting date.
closing_loss_allowance	number	Loss allowance balance at the end of the reporting period.
opening_loss_allowance	number	Loss allowance balance at the beginning of the reporting period.
total_gross_receivables	number	Total gross receivables as of the reporting date, before the loss allowance is deducted.
financial_report_excerpt	string	Source text from the financial report used for the analysis.
average_gross_receivables	number	Average gross receivables over the period, used as the denominator for the period-based net charge-off rate.
allowance_reconciliation_difference	number	Difference between the calculated and reported closing allowances, using opening allowance plus provision less gross charge-offs plus recoveries.

Compliance Statement

Authorization Type	Proprietary - Commercial AI Training License (No Redistribution)
Commercial Use	Requires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and Anonymization	No PII, no real company names, simulated scenarios follow industry standards
Compliance System	Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Frequently Asked Questions

What does the dataset include?: It focuses on consumer installment receivables in financial reports and includes reporting periods, currencies, receivables by aging bucket, defaulted balances, charge-offs and recoveries, opening and closing loss allowances, loss rates, and reconciliation analysis.

Which calculations and analyses does it support?: It supports aging profile analysis, closing loss rate calculations, net charge-off rate calculations, allowance roll-forward reconciliation, and explanations of calculation steps and credit loss performance. Calculations should follow the definitions and reporting conventions in the source report.

Is the dataset intended for training or evaluation?: Designed for domain-specific supervised fine-tuning, it can train models to extract figures from financial reports, perform financial calculations, and produce analytical conclusions. It can also be organized into evaluation samples to test numerical accuracy and reasoning.

How can I obtain the dataset, and what are the licensing terms?: Refer to the product listing or sales agreement for availability, delivery format, and licensing terms. Before use, confirm whether the license covers commercial use, model training, derivative data creation, and external distribution.

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Cite this Work

@dataset{Mobiusi2026,
  title={Installment Receivables Credit Loss Numerical Reasoning Dataset},
  author={MOBIUSI INC},
  year={2026},
  url={https://www.mobiusi.com/datasets/ce75eb64af2cdb1c34c4e2aa077b930f},
  urldate={2026-10-21},
  keywords={installment receivables dataset,credit loss reasoning,consumer finance data,financial numerical reasoning,receivables aging analysis,credit loss allowance,net charge-off rate calculation,financial report analysis},
  version={1.0}
}

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