AI based Semantic Search Engine - GR
Governance, Risk, Compliance and the Regulatory structure supporting it is very expansive in nature. There are many layers to this integral system - from external rule data to data pertaining to legislation, and etc. To maneuver through this mountain of crucial data is very important, unfortunately this undertaking hasn’t been done until now by us.
Governance, Risk, and Compliance Industry
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. They tasked us to create an AI powered semantic search engine for their compliance teams, that can reduce regulatory compliance risk, resolve their compliance rule retrieval, structuring and analysis issues caused due to the massive and continuous changes in regulatory compliance data.
Our search model needs to tackle these challenges that were identified by our field analysis :
Scaling Issues - The amount of correlated and independent data can affect the overall scalability of our semantic search model. More steps would need to be undertaken to resolve this issue.
Indexing and Optimization of Data - The varying and expansive amounts of data need to be indexed and optimized properly for the efficient functioning of our search model.
User-oriented optimization - The user/client has to have the ability to select, sort, and do other operations on the search with ease promoting a high level of usability.
With the challenges in mind, we have created flexible solutions to deal with them:
The structured rule data within the database are spooled together to create an intelligent graph, which is the bedrock of our model.
This graph structure is specific to our clients, with their insights supporting their creation. These graphs are known to be versatile and independent but also very modular in nature. These graphs can be constantly updated and tweaked to fit our needs as well.
Our AI powered search which contains these graphs and also high-dimensional vectors (which are methods of orienting text and search terms to our specifications) can be very easily used to find out external regulatory rules that are semantically linked to our own system.
Our resourceful semantic search model can provide compliance teams with an extra flexibility and accuracy in collecting external regulatory data and also compare them with internal data as well. Disparity between rules is reduced, which leads to the creation of cohesive rule set which could tackle any compliance issue seamlessly.
The methodology of search operations done can be developed in an evolutionary manner due to the Machine Learning and Business Rule integration operations embedded in our search engine. This relatively increases the speed of searching and proliferates constant updation.
Our state-of-the-art model can also point out the places where the new and nascent rule changes has affected with the proper timestamp, logs, and metadata. This operation is done by comparing and contrasting the search terms and the vectors which contains the regulatory rules and obligations.
All in all, our model can be extremely helpful in the function of any GRC related application providing speed, efficiency, flexibility, and integration.
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