PII PHI Redactor (Gen AI)
Domain: Insurance/Cross Industries
Description: It is a Gen-AI based solution. Companies incur losses due to not following laws like HIPAA, GDPR etc. Insurance companies receives various sensitive documents as part of customer onboarding process. None of these documents can be stored or processed without masking. This application would classify document based on how sensitive the contents are. It will further identify PII, PHI content and accordingly redact it or replace it with placholders. Document can be images as well as Text documents.
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Prequalification of Insurance Application (Gen AI)
Domain: Insurance/Cross Industries
Description: Improve Operational efficiency by automating, and/or modernizing the proofing process for Insurance application. This solution extract information from the document and cross validate the info with the supporting document. Increased operational efficiency efforts to save 4 to 5 hours of underwriter. This solution can be used in any other domain as well. Used Amazon Bedrock services and anthropic’s Claude v2 model.
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Accident Management-Service Date Adjustment Model (AI-ML)
Domain: Roadside Assistance & Accident Management
Description: Created a claim prediction model that predicts claims for cases that have not yet been approved for payment. AM-SDA model predicts the claim cost on daily bases. It allows for a more “up-to-date” view on claims, particularly for recent date. Generate the average claim cost report card at case level sooner. Easy access to the average claim cost data. Weekly and monthly reports for network and finance team to view the claims cases “up-to-date”
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Outlier Detection and Data exploration for KPIs (AI-ML)
Domain: Roadside Assistance & Accident Management
Description: In this project I have done the data exploration work to identify and remove data from experiments. Used different statistical methods to identify outliers for different distributions. CPC(Cost per case), EPJ(Effort per job), CPI(Cost per invoice) and EPC (Effort per case) are different KPIs for which outlier detection is done. Also created the sigma dashboard for visaulizing the data post outlier detection. Also created the sigma dashboard for visaulizing the data post outlier detection.
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Trade Credit Rater (AI-ML)
Domain: Insurance
Description: In thisTC Rater is a tool for TC underwriters to generate a technical premium, expected loss, expenses and capital charge considering the time probability of default, country risk, industry risk etc.
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US Doc Proofing (AI-ML)
Domain: Legal Content
Description: Improve Operational efficiency by automating, and/or modernizing the proofing process for US legislation print proofing. Improve cycle time from 2 mins per page to 1 min per page. Achieve a per section quality target of at least 98% per current quality guidelines. Reduce the number of required proofing reviews by eliminating the 2nd or 3rd review hand-offs.
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Legislation Extraction (NLP)
Domain: Legal Content
Description: Legislation extraction is to identify section, subsection, rule, sub-rule, article, etc. of any act or regulation mentioned in a Case Law document and map them to its corresponding source document. 30% accuracy increased in terms of linking the source document correctly.
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De-Duping (NLP)
Domain: Legal Content
Description: In this project I have used Bag of Word and TF-IDF algorithm to find the duplicacy between documents. Within these documents, we are targeting The fields like Judge Name, Case Number, Advocates and Party names, etc to find out the duplicate documents. The duplicacy of online product is a big drawback and impact the search results and linking adversely.
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Metadata Extraction (NLP)
Domain: Legal Content
Description: In this project I have used the Naïve Bayes algorithm to train the model. This model will find different metadata fields like Judge Name, Case Number, Advocates and Party names, etc from the PDF of a Judgment. This will also generate an excel sheet of the extracted metadata and store them in DB alongside.
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