Machine Learning Discovery for Predictive Analytics on Insurance Claims

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Platform: ClaimDeck 1. Project Overview ClaimDeck is a SaaS platform designed to streamline insurance claim management for insurance carriers and law firms. The platform captures a wide range of data fields related to claims, including case life, indemnity, legal spend, and other critical metrics. Primary Goal: Assess the feasibility of applying predictive analytics to the following key metrics: • Case Life (in days): Predict the duration of a claim from opening to resolution. • Indemnity (in dollars): Predict the total indemnity payout for a claim. • Legal Spend (in dollars): Predict the total legal expenses associated with a claim. Additionally, the project will explore other potential predictive opportunities based on the available data fields and provide recommendations for improving data quality and model performance. Note: This is a discovery phase and does not include the development of machine learning models. 2. Scope of Work The data scientist or team will be responsible for the following tasks: 2.1 Data Analysis • Review the provided data fields and assess their suitability for predictive analytics. • Identify data quality issues (e.g., missing values, inconsistencies) and recommend preprocessing steps. • Analyze the volume and distribution of data to determine its adequacy for training predictive models. 2.2 Feasibility Assessment • Evaluate the feasibility of building predictive models for: o Case life (in days). o Indemnity (in dollars). o Legal spend (in dollars). • Estimate the potential accuracy of predictions based on the available data volume and quality. 2.3 Additional Predictive Opportunities • Suggest other potential predictive analytics use cases based on the available data fields (e.g., claim severity, settlement likelihood, budget accuracy, fraud detection). • Provide a prioritized list of recommendations for future model development. 2.4 Data Privacy and Compliance • Ensure that all analysis adheres to HIPAA compliance requirements, particularly for sensitive data in case documents. 3. Available Data Fields The following is a list of key data fields available in the ClaimDeck platform. Additional data includes notes, comments, contacts, and files associated with claims. Category Data Fields Claim Details Claim Name, Claim Score, Claim Number, Policy Number, Status, Date Open, Date Closed, Date Pending, Days Active, Date of Loss, First Notice of Loss (FNOL), Statute of Limitations, Resolution Date, Resolution Type, Resolution Amount, Settlement Documents Signed Date, Court Order Signed Date, Claim Overview Last Updated, Resolution Strategy Last Updated Financials Latest Demand Amount, Latest Demand Date, Latest Offer Amount, Latest Offer Date, Plaintiff's Original Demand, Plaintiff's Original Demand Date, Expenses Reserve, Indemnity Reserve, Budget, Billed Amount, Billed To Budget, Budget Type, Budget Accuracy, Valuation Accuracy Legal Details Law Firm, Lead Attorney, Assisting Attorney, Legal Assistant, Plaintiff’s Counsel Law Firm, Plaintiff’s Counsel, Paralegal, Partner Billing Rate, Associate Billing Rate, Legal Assistant Billing Rate, Latest Mediation Date, Latest Mediation Proposal, Liens, Liens Note, Settlement Negotiations Note, Claim Valuation Note, Budget Note, Possible Coverages Note Case Details Corporate Representative, Insured, County, City, State, Country, Cause Number, Court, Judge, Practice Area, Sub Practice Area, Liability Valuation Of Insured(s), Possible Coverages Carrier Details Carrier, Syndicate, Third-Party Administrator (TPA), Client/Matter Number, Unique Market Reference (UMR) 4. Deliverables The following deliverables are expected at the conclusion of the discovery phase: 1. Data Analysis Report: a. Summary of data quality and preprocessing recommendations. b. Assessment of data volume and suitability for predictive analytics. 2. Feasibility Report: a. Feasibility of predictive models for case life, indemnity, and legal spend. b. Estimated accuracy of predictions based on current data. 3. Recommendations Report: a. Additional predictive analytics opportunities (e.g., claim severity, settlement likelihood). b. Prioritized list of recommendations for future model development. 4. Presentation: a. A presentation to stakeholders summarizing findings and recommendations. 5. Timeline and Milestones The proposed timeline for the discovery phase is 4-6 weeks, with the following milestones: • Week 1: Kickoff meeting and data access provision. • Week 2-3: Data analysis and feasibility assessment. • Week 4-5: Recommendations and report drafting. • Week 6: Final delivery and presentation. 6. Budget Please provide a cost estimate for the discovery phase, including: • Hourly rates or fixed project fees. • Any additional costs for data privacy or compliance measures. 7. Submission Requirements Proposals should include the following: • Approach and methodology for the discovery phase. • Relevant experience with insurance data and predictive analytics. • Case studies or examples of similar projects. • Estimated timeline and cost. • Team composition and qualifications. 8. Evaluation Criteria Proposals will be evaluated based on: • Expertise in machine learning and predictive analytics. • Experience with insurance or legal data. • Proposed approach and methodology. • Cost and timeline. 9. Confidentiality and Compliance All work must adhere to HIPAA compliance requirements, particularly for sensitive data in case documents. A non-disclosure agreement (NDA) will be required before project initiation.

Keyword: Machine Learning

Price: $50.0

Machine Learning Data Analysis Data Science Data Mining Statistics