Harinath Reddy Y

Fraud Detection and Claims Automation

• Developed and deployed AI models for fraud detection , risk assessment , and customer behavior analysis using Python , scikit-learn , TensorFlow , and PyTorch . Leveraged Domino Data Lab's Enterprise MLOps platform for end-to-end model development and deployment, enhancing scalability and ensuring timely delivery of business-critical insights through the Analytics Center of Excellence (ACOE) .
• Implemented generative AI powered by OpenAI's GPT models to automate claims-related email communications , ensuring clarity , empathy , and consistency. This automation reduced manual efforts and freed up time for agents to focus on complex tasks, resulting in enhanced customer experience and operational efficiency .
• Collaborated with fraud investigators to deploy AI systems that automatically flag suspicious claims for further review, significantly improving fraud detection capabilities. Utilized super AI for image segmentation in automating vehicle damage claims , which streamlined claim processing and led to faster claim resolutions and improved customer satisfaction .
• Integrated AI-powered assistants like Amelia and chatbots into customer service operations , providing 24/7 support and reducing wait times. Deployed AI models on Microsoft Azure , ensuring seamless cloud integration and scaling to meet high-demand use cases. Additionally, utilized GCP and AWS to support cloud-based digital transformation initiatives.

• Led the development of AI-powered claims management solutions for the insurance sector , optimizing fraud detection and risk assessment models . These AI-driven solutions reduced claims processing time by 30% and improved accuracy by 20% . Conducted exploratory data analysis (EDA) and feature engineering to extract insights from complex datasets.
• Designed and implemented machine learning models using Python, scikit-learn, and TensorFlow , focusing on predictive analytics . Developed natural language processing (NLP) models to automate document processing and claims validation . Built and maintained end-to-end data pipelines with SQL, Azure Data Factory, and Databricks for seamless ETL processes .
• Collaborated with cross-functional teams to integrate AI models into insurance platforms , improving customer experience . Deployed and monitored models using Azure Machine Learning for scalability and performance optimization . Maintained adherence to Responsible AI principles , incorporating bias detection and model explainability for ethical AI implementation .