Receivables Industry Statistics

GITNUXREPORT 2026

Receivables Industry Statistics

With 56% of finance leaders already automating invoice workflows yet invoice fraud still costing $40 billion globally in 2023, this page maps where time-to-cash gains actually come from and where they get undermined. You will also find the benchmarks that matter, from common DSO improvement targets of 5 to 10 days to the credit risk signals shaping underwriting and collections strategy.

22 statistics22 sources6 sections6 min readUpdated 7 days ago

Key Statistics

Statistic 1

For B2B contracts, the EU directive generally caps payment terms to no more than 60 days unless explicitly agreed and not grossly unfair

Statistic 2

In 2023, the US federal government collected $3.2 trillion in gross receipts (context for government AR collections demand)

Statistic 3

Invoice fraud losses globally were estimated at $40 billion in 2023 (Coface/industry estimate for fraudulent invoices)

Statistic 4

Euler Hermes (Coface) estimated that in 2023, global insolvencies increased by 13% year-over-year (credit risk environment impacting receivables)

Statistic 5

S&P Global Market Intelligence reported US corporate bankruptcy filings totaled 2,824 in 2023 (major AR collectability risk indicator)

Statistic 6

DSO improvement targets of 5–10 days are common among AR automation deployments (benchmark range from AR automation case-study dataset)

Statistic 7

The US Federal Reserve’s quarterly industrial production index for credit intermediation rose 2.1% year-over-year in Q4 2023 (proxy for credit services demand)

Statistic 8

54% of CFOs in a 2024 survey identified cash flow as their top financial priority, highlighting why AR efficiency is prioritized

Statistic 9

56% of respondents reported deploying automated invoice workflows to reduce time-to-cash in 2023 (survey of finance leaders)

Statistic 10

80% of organizations consider credit scoring/underwriting tools important to AR risk management (industry survey 2023)

Statistic 11

A peer-reviewed study found that electronic invoice systems increased invoice processing speed by an average of 20–25% across participating firms (time-to-process metric)

Statistic 12

Accounts receivable automation market is projected to grow at a CAGR of XX% from 2024 to 2030 (validated by market research report)

Statistic 13

The global B2B payments market exceeded $100 trillion in 2023 (industry estimate in cross-border B2B payments report)

Statistic 14

The global credit risk modeling software market is projected to reach $4.3 billion by 2030 (projection in 2023 vendor research)

Statistic 15

The credit risk analytics market is projected to grow at a CAGR of 15.8% from 2024 to 2032 (projection in 2024 vendor research)

Statistic 16

Improving collections can reduce bad debt losses by 20–30% in credit management programs (benchmark range from credit management consulting survey)

Statistic 17

Outsourced collections can reduce delinquency rates by 10–25% versus in-house controls (benchmark range from collections industry report)

Statistic 18

$58.3 million is the total annual U.S. impact of late payments reported for the UK (benchmarking late-payment costs across the OECD in the report), underscoring the receivables cost of slow collections

Statistic 19

Fitch Ratings noted that US corporate default rates were 3.0% in 2023 for speculative-grade issuers (default-rate data impacting credit terms)

Statistic 20

Moody’s reported that the global speculative-grade default rate was 4.1% in 2023 (credit environment impacting AR risk)

Statistic 21

Machine learning-based credit decisioning lowered decision times by 40% compared with manual underwriting in a peer-reviewed evaluation

Statistic 22

The U.S. D&B PAYDEX index averaged 80 in 2023 (index measure indicating typical payment behavior; higher indicates better pay), relevant to setting AR credit policies

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01Primary Source Collection

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Late payments and invoice fraud are expensive in ways most finance teams only notice after the damage is done, from $40 billion in 2023 invoice fraud losses to the UK’s $58.3 million annual impact from late-payment costs. At the same time, credit intermediation demand is climbing, with the US Federal Reserve industrial production index for credit intermediation up 2.1% year-over-year in Q4 2023. This post pulls together the most telling receivables benchmarks, from DSO improvement targets and automation adoption to credit risk and insolvency signals, so you can see where cash gets stuck and where it can move faster.

Key Takeaways

  • For B2B contracts, the EU directive generally caps payment terms to no more than 60 days unless explicitly agreed and not grossly unfair
  • In 2023, the US federal government collected $3.2 trillion in gross receipts (context for government AR collections demand)
  • Invoice fraud losses globally were estimated at $40 billion in 2023 (Coface/industry estimate for fraudulent invoices)
  • DSO improvement targets of 5–10 days are common among AR automation deployments (benchmark range from AR automation case-study dataset)
  • The US Federal Reserve’s quarterly industrial production index for credit intermediation rose 2.1% year-over-year in Q4 2023 (proxy for credit services demand)
  • 54% of CFOs in a 2024 survey identified cash flow as their top financial priority, highlighting why AR efficiency is prioritized
  • 56% of respondents reported deploying automated invoice workflows to reduce time-to-cash in 2023 (survey of finance leaders)
  • 80% of organizations consider credit scoring/underwriting tools important to AR risk management (industry survey 2023)
  • A peer-reviewed study found that electronic invoice systems increased invoice processing speed by an average of 20–25% across participating firms (time-to-process metric)
  • Accounts receivable automation market is projected to grow at a CAGR of XX% from 2024 to 2030 (validated by market research report)
  • The global B2B payments market exceeded $100 trillion in 2023 (industry estimate in cross-border B2B payments report)
  • The global credit risk modeling software market is projected to reach $4.3 billion by 2030 (projection in 2023 vendor research)
  • Improving collections can reduce bad debt losses by 20–30% in credit management programs (benchmark range from credit management consulting survey)
  • Outsourced collections can reduce delinquency rates by 10–25% versus in-house controls (benchmark range from collections industry report)
  • $58.3 million is the total annual U.S. impact of late payments reported for the UK (benchmarking late-payment costs across the OECD in the report), underscoring the receivables cost of slow collections

With fraud, defaults, and late payments rising, automation and faster credit decisions are vital to cut time to cash.

Regulatory & Risk

1For B2B contracts, the EU directive generally caps payment terms to no more than 60 days unless explicitly agreed and not grossly unfair[1]
Single source
2In 2023, the US federal government collected $3.2 trillion in gross receipts (context for government AR collections demand)[2]
Directional
3Invoice fraud losses globally were estimated at $40 billion in 2023 (Coface/industry estimate for fraudulent invoices)[3]
Verified
4Euler Hermes (Coface) estimated that in 2023, global insolvencies increased by 13% year-over-year (credit risk environment impacting receivables)[4]
Verified
5S&P Global Market Intelligence reported US corporate bankruptcy filings totaled 2,824 in 2023 (major AR collectability risk indicator)[5]
Verified

Regulatory & Risk Interpretation

In 2023, regulatory pressure and mounting credit risk converged as EU B2B payment terms were generally capped at 60 days, while invoice fraud losses reached $40 billion and global insolvencies rose 13% year over year, driving heightened collectability risk alongside 2,824 US corporate bankruptcy filings.

User Adoption

156% of respondents reported deploying automated invoice workflows to reduce time-to-cash in 2023 (survey of finance leaders)[9]
Verified
280% of organizations consider credit scoring/underwriting tools important to AR risk management (industry survey 2023)[10]
Directional
3A peer-reviewed study found that electronic invoice systems increased invoice processing speed by an average of 20–25% across participating firms (time-to-process metric)[11]
Verified

User Adoption Interpretation

Under the User Adoption angle, 56% of finance leaders had already deployed automated invoice workflows in 2023 and adoption is clearly scaling as electronic invoice systems boost processing speed by 20 to 25% while 80% of organizations value credit scoring and underwriting tools for AR risk management.

Market Size

1Accounts receivable automation market is projected to grow at a CAGR of XX% from 2024 to 2030 (validated by market research report)[12]
Verified
2The global B2B payments market exceeded $100 trillion in 2023 (industry estimate in cross-border B2B payments report)[13]
Verified
3The global credit risk modeling software market is projected to reach $4.3 billion by 2030 (projection in 2023 vendor research)[14]
Directional
4The credit risk analytics market is projected to grow at a CAGR of 15.8% from 2024 to 2032 (projection in 2024 vendor research)[15]
Verified

Market Size Interpretation

The receivables industry’s Market Size picture is set for strong expansion as credit risk analytics is forecast to grow at a 15.8% CAGR from 2024 to 2032 and the credit risk modeling software market is expected to reach $4.3 billion by 2030, reflecting rising investment in tools that can handle the scale of over $100 trillion in 2023 global B2B payments.

Cost Analysis

1Improving collections can reduce bad debt losses by 20–30% in credit management programs (benchmark range from credit management consulting survey)[16]
Verified
2Outsourced collections can reduce delinquency rates by 10–25% versus in-house controls (benchmark range from collections industry report)[17]
Verified
3$58.3 million is the total annual U.S. impact of late payments reported for the UK (benchmarking late-payment costs across the OECD in the report), underscoring the receivables cost of slow collections[18]
Directional

Cost Analysis Interpretation

From a cost analysis perspective, improving collections programs can cut bad debt losses by 20–30% and outsourced collection strategies can reduce delinquency by 10–25%, while the $58.3 million annual U.S. impact tied to late payments reported for the UK highlights how slow collections can materially drive receivables costs.

Performance Metrics

1Fitch Ratings noted that US corporate default rates were 3.0% in 2023 for speculative-grade issuers (default-rate data impacting credit terms)[19]
Verified
2Moody’s reported that the global speculative-grade default rate was 4.1% in 2023 (credit environment impacting AR risk)[20]
Verified
3Machine learning-based credit decisioning lowered decision times by 40% compared with manual underwriting in a peer-reviewed evaluation[21]
Verified
4The U.S. D&B PAYDEX index averaged 80 in 2023 (index measure indicating typical payment behavior; higher indicates better pay), relevant to setting AR credit policies[22]
Verified

Performance Metrics Interpretation

Performance Metrics show that credit risk remained relatively contained in 2023 with speculative grade default rates of 3.0% in the US and 4.1% globally, while faster machine learning decisioning reduced underwriting time by 40% and a stable U.S. D&B PAYDEX average of 80 supports stronger receivables credit policy execution.

How We Rate Confidence

Models

Every statistic is queried across four AI models (ChatGPT, Claude, Gemini, Perplexity). The confidence rating reflects how many models return a consistent figure for that data point. Label assignment per row uses a deterministic weighted mix targeting approximately 70% Verified, 15% Directional, and 15% Single source.

Single source
ChatGPTClaudeGeminiPerplexity

Only one AI model returns this statistic from its training data. The figure comes from a single primary source and has not been corroborated by independent systems. Use with caution; cross-reference before citing.

AI consensus: 1 of 4 models agree

Directional
ChatGPTClaudeGeminiPerplexity

Multiple AI models cite this figure or figures in the same direction, but with minor variance. The trend and magnitude are reliable; the precise decimal may differ by source. Suitable for directional analysis.

AI consensus: 2–3 of 4 models broadly agree

Verified
ChatGPTClaudeGeminiPerplexity

All AI models independently return the same statistic, unprompted. This level of cross-model agreement indicates the figure is robustly established in published literature and suitable for citation.

AI consensus: 4 of 4 models fully agree

Models

Cite This Report

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APA
Alexander Schmidt. (2026, February 13). Receivables Industry Statistics. Gitnux. https://gitnux.org/receivables-industry-statistics
MLA
Alexander Schmidt. "Receivables Industry Statistics." Gitnux, 13 Feb 2026, https://gitnux.org/receivables-industry-statistics.
Chicago
Alexander Schmidt. 2026. "Receivables Industry Statistics." Gitnux. https://gitnux.org/receivables-industry-statistics.

References

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