Traceability, Impact Assessment & Compliance Map

One of the most important and critical challenges for a compliance team is to manually trace all internal business unit rules, policies, procedures and controls, for each and every new compliance requirement which is appended to the search engine.
Here is where our AI powered semantic search engines takes precedence.It can traverse across regulatory landscapes to trace internal policies, controls, processes, risks, etc and can map a regulatory relationship with the new external compliance requirements.

California, USA

Client

Governance, Risk, and Compliance Industry

 

CLIENT

Our client, based in California, has been working in the field of providing AI-based GRC support tools for many major BFSI businesses for the past 3+ years. We were tasked to create a Traceability, Impact Assessment & Compliance Map under our AI powered Semantic Search Engine that we have engineered for our client.
This ”Traceability, Impact Assessment & Compliance Map” is a visual and textual representation of correlations between pre-existing regulatory frameworks and newly updated compliance requirements. The compliance team can detect matches and deviations between these rules making adaptation and updation easy.

 

CHALLENGE

Our model needs to tackle these challenges that were identified by our field analysis :

  • Chronicling the Data - The searches, changes, and the comparisons have to be chronicled with their proper history and metadata to ensure relevancy and data security.

  • Conditions for Changes - The conditions for altering internal data with respect to the external compliance recommendations have to be defined and reviewed by the company’s regulatory authority.

  • Mapping of Data - Scoring and Tagging could be integrated into our search engine for proper categorization and identification due to the relational, expansive, and morphing nature of compliance rules.

 

SOLUTION

With the challenges in mind, we have created flexible solutions to deal with them:

  • The weaknesses between rules are identified using a verified machine learning algorithm, where correlations are identified between the internal and external rules, and the correlation score is then interpreted into a relationship score between the rules. Lower the score, weaker the external compliance mapping to the specific rules and policies.

  • The compliance team is given control over the mapping, categorizing, and relational processes of the analyzed rules. The weaknesses and the differentiation between company rules and general regulations are also identified.

  • The relational rules across the laws and standards of both internal and external regulators are tagged. These tags help in retrieving regulator and external compliance rules at any given moment. The flow of updation to every other minute changes within the system are catalogued in a manner of timeline for referential purposes.

 

RESULT

  • This feature appended to the Semantic Search Engine helps the compliance team to identify weakness in their policies and controls against compliance rules. It also ensures an adequate rule coverage and consistency across all relevant internal regulatory business units.

  • The mapping of similarities and requirements visually helps in the development of quick review and revisions.

  • The creation of tags and timelines helps the compliance agents in the retrieval and organization of the regulatory rule data and searches.

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