Actuarial Data Scientist

Summary

PhD in Actuarial Science with close to 6 years of work/project experience in the area of Actuarial Data Science
Expertise in predictive modelling, data interpreting, and visualization with 8 international journal papers and 1 book chapter
Speaker in top Actuarial Data Science conferences such as 22nd Global Conference of Actuaries, 2021 Insurance Data Science, 2022 CAS Spring Meeting, 2022 CANW Spring Meet, 2022 ASTIN Actuarial Colloquia
Invited as a speaker for 2023 CAS International Webinar Series and also by the African Actuarial community
Developed over 5 web apps/dashboards for dynamic reporting and visualizations to aid in decision-making

¹ Sri Sathya Sai Institute of Higher Learning

Actuarial Team Lead @ Accenture (Jul 2023 - Present)

Rater Delivery

Working with the Technical Pricing team to help transition raters in legacy excel based models to latest data science models

Actuarial Data Science Consultant @ RSA Actuarial Services (Dec 2022 - Jun 2023)

Motor Insurance Fraud

Developed and implemented a Machine Learning model to prioritize fraudulent cases, resulting in a 20% increase in the success rate of fraud applications.
Conducted a thorough Full-Time Equivalent (FTE) analysis to estimate the necessary resources to secure previously neglected benefits.
Created effective business rules based on SIRA reports that reduced the number of potentially fraudulent applications by 70%

Actuarial Analyst @ SwissRe Bangalore (Jun 2021 - Nov 2022)

Pricing

Successfully supported 30+ quotes on pricing and experience studies for various products comprising – whole/term life assurance, critical illness benefit, disability benefits and hospitalization benefits for the South-East Asia market
Conducted impact testing on terms of trade assumptions for whole/life assurance products
Responsible for monitoring critical illness capacity for over 70+ quotes in the South-East Asia market

Experience studies

Conducted experience studies using R to derive A/E ratios, resulting in cost revisions that accurately reflected actual experience

R Modeling and dashboarding

Created and utilized R and Excel models for experience studies, premium trends calculation, and data visualization for life and health products for South-East asia market
Utilized R model to generate profit commission for various quotes

Product design

Collaborated with claims and underwriting teams for product discussions and design, resulting in the successful launch of three new products viz. critical illness products with very specialized product offering

Communication and Presentation

Presented results to team lead and wrote executive summary detailing pricing strategy to deliver to clients
Delivered a presentation on “Intro to Machine Learning using Decision Tree”

Actuarial Consultant @ Tech Actuarial (Jun 2019 - Jun 2021)

Ayushman Bharat Claims Analytics (Oct 2020 – Jun 2021)

Worked on claims analytics for the world’s largest group health insurance scheme using Python and R to provide claims analytics to the broker and insurance company on 200,000+ records
Developed an RShiny dashboard to track the claims experience monthly, resulting in a 20% improvement in claims management efficiency
Analyzed claim patterns and fraud in 200,000+ claims and policy records during the COVID-19 period, resulting in a 5% reduction in fraudulent claims

Crop Insurance Pricing (Apr 2020 – Sep 2020)

Utilized Python for web scraping of 3 million data points of daily yield data for various crops, resulting in almost 90% reduction in data processing time
Developed an ARIMA model to project crop yield data for the next three months resulting is improvement in accuracy of crop yield projections
Built a dashboard to track the value of futures under 12 different scenarios for six different crops, resulting in a 70% reduction in time spent on manual data tracking and analysis

Cancer Product Pricing (Sep 2019 – Mar 2019)

Successfully priced cancer products for four different packages across 28 states in India
Created a dashboard for dynamic comparison and visualization of frequency, severity, and risk premiums by gender and state, totaling to 100+ scenarios, resulting in a improvement in visualization and comparison of pricing scenarios

Defined benefit pension scheme valuation (Jun 2019 – Aug 2019)

Simplified valuation using Excel spreadsheet, resulting in a 10x reduction in run time and an improvement in efficiency in the defined benefit pension scheme valuation process
Prepared 30+ consulting pitch presentations for various clients over two years

Student Central Summer Internship Program @ CAS (Jun 2020 - Aug 2020)

Completed a six-week program focused on Property & Casualty, gaining insights into data visualization, pricing, reserving, and predictive modeling
Completed four projects using Cognalysis Multirate software, resulting in improved pricing and reserving strategies
Recognized as a “CAS Spotlight Candidate” by the CAS for being an active student member and demonstrating exceptional knowledge and skills in actuarial science

Research Associate @ Tech Actuarial (Jun 2017 - Jun 2019)

Proposed a framework for fraud detection, which includes actuarial and data science techniques
Generated 450+ business rules used for fraud detection and developed an innovative method to automate rule generation using machine learning and data visualization
Used Python and R to develop data-driven fraud detection models for automobile and health insurance businesses with more than 100,000 records and have achieved results with 95%+ accuracy

Speaker at 22nd Global Conference of Actuaries – Delhi, India, March 2023
Speaker at 3rd Insurance Data Science Conference – City, University of London June 17, 2021
Speaker at 2022 CAS Spring Meeting, Disney’s Coronado Springs Resort, May 15 - May 18, 2022
Speaker at 2022 Actuarial Colloquia, organized by Casualty Actuarial Society, with IAA and ASTIN

Journal Papers

“Implementation of Correlation and Regression Models for Health Insurance Fraud in Covid-19 Environment using Actuarial and Data Science Techniques,” Int. J. Recent Technol. Eng. Sep. 2020
“A Comparative Study of Using Various Machine Learning and Deep Learning-Based Fraud Detection Models For Universal Health Coverage Schemes,” Int. J. Eng. Trends Technol., 2021

Italian Trulli