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iLearningEngines (iLearningEngines) Receivables Turnover : 4.69 (As of Jun. 2023)


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What is iLearningEngines Receivables Turnover?

The Receivables Turnover ratio measures the number of times a company collects its average accounts receivable balance. It is calculated as Revenue divided by average Accounts Receivable. An efficient company has a higher accounts receivable turnover ratio while an inefficient company has a lower ratio. iLearningEngines's Revenue for the six months ended in Jun. 2023 was $195.2 Mil. iLearningEngines's average Accounts Receivable for the six months ended in Jun. 2023 was $41.6 Mil. Hence, iLearningEngines's Receivables Turnover for the six months ended in Jun. 2023 was 4.69.


iLearningEngines Receivables Turnover Historical Data

The historical data trend for iLearningEngines's Receivables Turnover can be seen below:

* For Operating Data section: All numbers are indicated by the unit behind each term and all currency related amount are in USD.
* For other sections: All numbers are in millions except for per share data, ratio, and percentage. All currency related amount are indicated in the company's associated stock exchange currency.

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iLearningEngines Receivables Turnover Chart

iLearningEngines Annual Data
Trend Dec20 Dec21 Dec22
Receivables Turnover
- 13.70 12.22

iLearningEngines Semi-Annual Data
Dec20 Dec21 Jun22 Dec22 Jun23
Receivables Turnover - - 9.03 4.77 4.69

Competitive Comparison of iLearningEngines's Receivables Turnover

For the Software - Infrastructure subindustry, iLearningEngines's Receivables Turnover, along with its competitors' market caps and Receivables Turnover data, can be viewed below:

* Competitive companies are chosen from companies within the same industry, with headquarter located in same country, with closest market capitalization; x-axis shows the market cap, and y-axis shows the term value; the bigger the dot, the larger the market cap. Note that "N/A" values will not show up in the chart.


iLearningEngines's Receivables Turnover Distribution in the Software Industry

For the Software industry and Technology sector, iLearningEngines's Receivables Turnover distribution charts can be found below:

* The bar in red indicates where iLearningEngines's Receivables Turnover falls into.



iLearningEngines Receivables Turnover Calculation

Receivables Turnover measures the number of times a company collects its average accounts receivable balance.

iLearningEngines's Receivables Turnover for the fiscal year that ended in Dec. 2022 is calculated as

Receivables Turnover (A: Dec. 2022 )
=Revenue / Average Total Inventories
=Revenue (A: Dec. 2022 ) / ((Accounts Receivable (A: Dec. 2021 ) + Accounts Receivable (A: Dec. 2022 )) / count )
=309.17 / ((15.898 + 34.698) / 2 )
=309.17 / 25.298
=12.22

iLearningEngines's Receivables Turnover for the quarter that ended in Jun. 2023 is calculated as

Receivables Turnover (Q: Jun. 2023 )
=Revenue / Average Total Inventories
=Revenue (Q: Jun. 2023 ) / ((Accounts Receivable (Q: Dec. 2022 ) + Accounts Receivable (Q: Jun. 2023 )) / count )
=195.225 / ((34.698 + 48.515) / 2 )
=195.225 / 41.6065
=4.69

* For Operating Data section: All numbers are indicated by the unit behind each term and all currency related amount are in USD.
* For other sections: All numbers are in millions except for per share data, ratio, and percentage. All currency related amount are indicated in the company's associated stock exchange currency.


iLearningEngines  (NAS:AILE) Receivables Turnover Explanation

An efficient company has a higher accounts receivable turnover ratio while an inefficient company has a lower ratio. This metric is commonly used to compare companies within the same industry to check whether they are on par with their competitors.


iLearningEngines Receivables Turnover Related Terms

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iLearningEngines (iLearningEngines) Business Description

Comparable Companies
Traded in Other Exchanges
N/A
Address
6701 Democracy Boulevard, Suite 300, Bethesda, MD, USA, 20817
iLearningEngines Inc is an AI and automation platform that empowers its customers to productize their institutional knowledge by transforming it into actionable intellectual property that enhances outcomes for employees, customers and other stakeholders. Its platform enables enterprises to build intelligent Knowledge Clouds that incorporate large volumes of structured and unstructured information across disparate internal and external systems and to automate organizational processes that leverage these Knowledge Clouds to improve performance. The company combines its offerings with vertically focused capabilities and data models to operationalize AI and automation to effectively and efficiently address critical challenges facing its customers.