AI Powered Regulatory Content Enrichment Pipeline
Every major with its own Governance, Risk, and Compliance framework have both general and organization specific rules that have to updated and revised as needed. External rules also play an important rule in the creation of an efficient GRC framework. To identify and integrate these rules, reports, and documentation within a company’s pre-existing rule-set is a very hard task to maintain manually.
California, USA
Client
Governance, Risk, and Compliance Industry
CLIENT
Our client provides advanced artificial intelligence solutions oriented towards managing and analysing various GRC functions. They provide these services globally and have been doing so for the past three years based out of California, USA. Their focuses tend to be fixed on research, automated assessments, and other GRC oriented functions. We were tasked by them to create an Extraction model which can be transformed and loaded onto existing GRC frameworks.
CHALLENGE
Our model needs to tackle these challenges that were identified by our field analysis :
-
Incoherence and Unavailability of Data - Data Extraction can sometimes be hindered by the irregularities and data purposefully hidden (such as data and reports behind paywalls). This could render some operations inefficient.
-
Data Complexity - Complexity of the Data being extracted and parsed can sometimes lead to certain
misgivings on fielding the data. -
Data Loss and Security - Sometimes, due to the massive range of data, there could sometimes be loss of data while extraction. Data could also vary in their security and we need to provide our own security strategies for our model.
SOLUTION
With the challenges in mind, we have created flexible solutions to deal with them:
-
Various regulatory sources are identified and their regulatory documentations are then downloaded and archived for retrieval purposes.
-
These downloaded documents are then regularized based on their general structure and relative to company specifications by an unique parsing methodology. This unified dataset is the bedrock for further AI operations.
-
The parsed rules present in the system are further enriched and made authoritative by categorically processing the rules by AI operations. This is done by constant classification, organization, and transformation of extracted data.
- These text based rules are converted into lingual vectors, that is, expressions which are logical and could be rendered searchable.
- Modulate the speech contextually and emotionally so that it maintains a natural flow.
- These rules are then committed into the desired data lake/database for further analysis and usage.
The changes of rules and the extent of these changes are logged as well.
RESULT
An intelligent Extraction-Transform-Load model like this, which can handle updation and commits at an extremely faster pace than any manual induction of rules could boost productivity very much. New inductions are immediately reflected in GRC framework as well. The semantic search engine integrated in the system can boost retrieval and identification of rules effectively. Management of data and newer, updated data is also made easier with our model.