Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Predicting Account Receivables with Machine Learning   Being able to predict when invoices will be paid is valuable in multiple
industries and supports decision-making processes in most financial workflows.
However, due to the complexity of data related to invoices and the fact that
the decision-making process is not registered in the accounts receivable
system, performing this prediction becomes a challenge. In this paper, we
present a prototype able to support collectors in predicting the payment of
invoices. This prototype is part of a solution developed in partnership with a
multinational bank and it has reached up to 81% of prediction accuracy, which
improved the prioritization of customers and supported the daily work of
collectors. Our simulations show that the adoption of our model to prioritize
the work o collectors saves up to ~1.75 million dollars per month. The
methodology and results presented in this paper will allow researchers and
practitioners in dealing with the problem of invoice payment prediction,
providing insights and examples of how to tackle issues present in real data.

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The categories of this paper are: plasm-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: q-alg
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The categories of this paper are: eess
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The categories of this paper are: stat
0
The categories of this paper are: hep-ph
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The categories of this paper are: supr-con
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-th
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The categories of this paper are: patt-sol
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The categories of this paper are: math-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: physics
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The categories of this paper are: math
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The categories of this paper are: dg-ga
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The categories of this paper are: quant-ph