High-risk passage automation in a digital transaction management platform
A document execution engine receives a training set of data including training documents that each include one or more passages associated with a passage type and a level of risk. The document execution engine trains a machine learned model based on the training set. The trained machine learned model, when applied to subsequently identified passages within documents in the document execution environment, can identify a passage with above threshold levels of risk (e.g., a high-risk passage) based on a passage type of the passage. The trained machine learned model can then provide for display the high-risk passage and a related passage of the same passage type from a second document within the document execution environment to the user via a document passage comparison interface. Differences between the passages can be highlighted, enabling a user to quickly compare and contrast the passages.
1 . A method, comprising:
accessing a training set of information including training documents within a document execution environment, each training document including one or more passages, each passage associated with a passage type and a level of risk;
training a machine learned model based on the accessed training set of information, the machine learned model configured to determine a level of risk associated with at least one passage based at least in part on the passage type of the at least one passage;
receiving feedback indicating whether the determined level of risk for the at least one passage is accurate;
modifying the training set of information based on the received feedback; and
re-training the machine learned model based on the modified training set of information.
2 . The method of claim 1 , wherein each training document is associated with a set of document characteristics comprising one or more of a document type, a region, a language, and an industry, and wherein the machine learned model is configured to determine a level of risk associated with the at least one passage based additionally on the set of document characteristics associated with a document in which the at least one passage appears.
3 . The method of claim 1 , wherein the passage type for a passage includes at least one of: a legal clause, a type of business clause, a type of finance clause, or a type of content within the passage.
4 . The method of claim 1 , wherein the feedback is received via an interface displaying the at least one passage on a device.
5 . The method of claim 1 , wherein modifying the training set of information comprises including the at least one passage within the training set associated with a level of risk specified by a user.
6 . The method of claim 1 , wherein one or more recommendations to mitigate the determined level of risk are presented on an interface in conjunction with displaying the at least one passage and the determined level of risk.
7 . The method of claim 6 , wherein the one or more recommendations include at least one of: a recommendation to provide a document including the at least one passage for review, a recommendation to digitally sign the document including the at least one passage, or a recommendation for one or more security measures to be implemented in association with the document including the at least one passage.
8 . A non-transitory computer-readable storage medium storing instructions that, when executed by a hardware processor, cause the hardware processor to:
access a training set of information including training documents within a document execution environment, each training document including one or more passages, each passage associated with a passage type and a level of risk;
train a machine learned model based on the accessed training set of information, the machine learned model configured to determine a level of risk associated with at least one passage based at least in part on the passage type of the at least one passage;
receive feedback indicating whether the determined level of risk for the at least one passage is accurate;
modify the training set of information based on the received feedback; and
re-train the machine learned model based on the modified training set of information.
9 . The non-transitory computer-readable storage medium of claim 8 , wherein each training document is associated with a set of document characteristics comprising one or more of a document type, a region, a language, and an industry, and wherein the machine learned model is configured to determine a level of risk associated with the at least one passage based additionally on the set of document characteristics associated with a document in which the at least one passage appears.
10 . The non-transitory computer-readable storage medium of claim 8 , wherein the passage type for a passage includes at least one of: a legal clause, a type of business clause, a type of finance clause, or a type of content within the passage.
11 . The non-transitory computer-readable storage medium of claim 8 , wherein the feedback is received via an interface displaying the at least one passage on a device.
12 . The non-transitory computer-readable storage medium of claim 8 , wherein modifying the training set of information comprises including the at least one passage within the training set associated with a level of risk specified by a user by the feedback.
13 . The non-transitory computer-readable storage medium of claim 8 , wherein one or more recommendations to mitigate the determined level of risk are presented via an interface of a device in conjunction with displaying the at least one passage and the determined level of risk.
14 . The non-transitory computer-readable storage medium of claim 13 , wherein the one or more recommendations include at least one of: a recommendation to provide a document including the at least one passage for review, a recommendation to digitally sign the document including the at least one passage, or a recommendation for one or more security measures to be implemented in association with the document including the at least one passage.
15 . A system comprising:
a hardware processor; and
a non-transitory computer-readable storage medium storing executable instructions that, when executed by the hardware processor, cause the hardware processor to:
access a training set of information including training documents within a document execution environment, each training document including one or more passages, each passage associated with a passage type and a level of risk;
train a machine learned model based on the accessed training set of information, the machine learned model configured to determine a level of risk associated with at least one passage based at least in part on the passage type of the at least one passage;
receive feedback indicating whether the determined level of risk for the at least one passage is accurate;
modify the training set of information based on the received feedback; and
re-train the machine learned model based on the modified training set of information.
16 . The system of claim 15 , wherein each training document is associated with a set of document characteristics comprising one or more of a document type, a region, a language, and an industry, and wherein the machine learned model is configured to determine a level of risk associated with the at least one passage based additionally on the set of document characteristics associated with a document in which the at least one passage appears.
17 . The system of claim 15 , wherein the passage type for a passage includes at least one of: a legal clause, a type of business clause, a type of finance clause, or a type of content within the passage.
18 . The system of claim 15 , wherein the feedback is received via an interface displaying the at least one passage on a device.
19 . The system of claim 15 , wherein modifying the training set of information comprises including the at least one passage within the training set associated with a level of risk specified by a user.
20 . The system of claim 15 , wherein one or more recommendations to mitigate the determined level of risk are presented via an interface of a device in conjunction with displaying the at least one passage and the determined level of risk.