Machine learning verification procedure

1. A system, comprising:

at least one data processor; and

at least one memory storing instructions which, when executed by the at least one data processor, result in operations comprising:

training a machine learning model by at least processing training data with the machine learning model, the training data including a plurality of invoice statements and a plurality of payment statements, the machine learning model being trained to match an invoice statement of the plurality of invoice statements to a corresponding payment statement of the plurality of payment statements;

manipulating a first data set of the training data by changing at least one parameter of at least one of the invoice statements and the payment statements;

applying the manipulated first data set to the machine learning model to thereby determine a first matching rate, wherein the first matching rate includes a first number of matches between invoice statements of the plurality of invoice statements and payment statements of the plurality of corresponding payment statements within the first data set;

applying the manipulated first data set to a rule engine to thereby determine a second matching rate, wherein the second matching rate includes a second number of matches between invoice statements of the plurality of invoice statements and payment statements of the plurality of corresponding payment statements within the second data set;

determining a difference between the first matching rate and the second matching rate;

determining whether the difference is within a predefined threshold range; and

providing an error indication if the determined difference is outside of the predefined threshold range.

2. The system of claim 1 , wherein the predefined threshold range includes the difference between the first number of matches and the second number of matches.

3. The system of claim 1 , wherein the operations further comprise retraining the machine learning model if the determined difference is outside of the predefined threshold range.

4. The system of claim 1 , wherein the at least one parameter includes one or more of an invoice amount, a payment amount, an invoice number, and a customer name.

5. A computer-implemented method comprising:

training a machine learning model by at least processing training data with the machine learning model, the training data including a plurality of invoice statements and a plurality of payment statements, the machine learning model being trained to match an invoice statement of the plurality of invoice statements to a corresponding payment statement of the plurality of payment statements;

manipulating a first data set of the training data by changing at least one parameter of at least one of the invoice statements and the payment statements;

applying the manipulated first data set to the machine learning model to thereby determine a first matching rate, wherein the first matching rate includes a first number of matches between invoice statements of the plurality of invoice statements and payment statements of the plurality of corresponding payment statements within the first data set;

applying the manipulated first data set to a rule engine to thereby determine a second matching rate, wherein the second matching rate includes a second number of matches between invoice statements of the plurality of invoice statements and payment statements of the plurality of corresponding payment statements within the second data set;

determining a difference between the first matching rate and the second matching rate;

determining whether the difference is within a predefined threshold range; and

providing an error indication if the determined difference is outside of the predefined threshold range.

6. The method of claim 5 , wherein the predefined threshold range includes the difference between the first number of matches and the second number of matches.

7. The method of claim 5 , further comprising retraining the machine learning model if the determined difference is outside of the predefined threshold range.

8. The method of claim 5 , wherein the at least one parameter includes one or more of an invoice amount, a payment amount, an invoice number, and a customer name.

9. A non-transitory computer-readable medium storing instructions, which when executed by at least one data processor, result in operations comprising:

training a machine learning model by at least processing training data with the machine learning model, the training data including a plurality of invoice statements and a plurality of payment statements, the machine learning model being trained to match an invoice statement of the plurality of invoice statements to a corresponding payment statement of the plurality of payment statements;

manipulating a first data set of the training data by changing at least one parameter of at least one of the invoice statements and the payment statements;

applying the manipulated first data set to the machine learning model to thereby determine a first matching rate, wherein the first matching rate includes a first number of matches between invoice statements of the plurality of invoice statements and payment statements of the plurality of corresponding payment statements within the first data set;

applying the manipulated first data set to a rule engine to thereby determine a second matching rate, wherein the second matching rate includes a second number of matches between invoice statements of the plurality of invoice statements and payment statements of the plurality of corresponding payment statements within the second data set;

determining a difference between the first matching rate and the second matching rate;

determining whether the difference is within a predefined threshold range; and

providing an error indication if the determined difference is outside of the predefined threshold range.

10. The non-transitory computer-readable medium of claim 9 , wherein the predefined threshold range includes the difference between the first number of matches and the second number of matches.

11. The non-transitory computer-readable medium of claim 9 , wherein the operations further comprise retraining the machine learning model if the determined difference is outside of the predefined threshold range.

12. The non-transitory computer-readable medium of claim 9 , wherein the at least one parameter includes one or more of an invoice amount, a payment amount, an invoice number, and a customer name.