Actuarial Science @ Facultad de Ciencias, UNAM
Technical Advisor @ Lockton · 3+ yrs in collective health & life insurance
→ Transitioning into banking & financial risk

At Lockton I manage the full technical lifecycle of corporate insurance accounts — from risk analysis to final placement:

• Negotiation & underwriting — negotiate premiums and conditions directly with insurers for collective health and life accounts
• Portfolio scale — corporate portfolios with annual premiums ranging from $5M to $40M MXN
• Analytical work — claims analysis, morbidity trends, loss ratios, market benchmarking, and findings metrics
• Harmonization — when subsidiaries acquire other companies, I consolidate and homologate their insurance programs into a unified structure
• Stakeholder communication — present risk findings and proposals to directors, commercial teams, and clients — including US/UK accounts — in English and Spanish

A web tool I designed and configured — defining parameters, calculation logic, and data cubes — that cuts the quoting process from 2+ hours to under 10 minutes for accounts eligible under broker-insurer agreements.

• Queries multiple insurers simultaneously
• Auto-generates quotation slips and output slips in Excel format
• Produces a full corporate presentation with all conditions and pricing
• Designed to handle high-volume, standardized accounts at scale

A team-wide dashboard for tracking placement pipeline status across accounts.

Modular Python library for market risk quantification via Monte Carlo simulation across multi-asset portfolios.

• 4 methods: Cholesky (Normal & Empirical) · PCA (Normal & Empirical)
• 10,000 simulations; captures fat tails, skewness, and cross-asset correlations
• Automated data ingestion via the Yahoo Finance API; configurable reporting (summary tables, CSV exports, visualizations)
• Built to explore quantitative market risk metrics commonly used in portfolio risk management and regulatory capital frameworks

• Led inflation analysis for a simulated Banco de México Governing Board session; recommended holding the reference rate unchanged based on persistent core inflation relative to the 3% target, and authored the monetary policy bulletin
• Team built a multi-agent AI system simulating board deliberation across five differentiated stances, with automated voting and minutes generation

• Built an insurance loss prediction model in R using a GLM Tweedie with cross-validated parameter selection and per-coverage modeling
• Achieved a Gini coefficient of 0.46

Python · NumPy · pandas · R · SQL · SciPy · Excel · yfinance · Power BI · Git · GitHub

Currently building: credit risk models (PD / LGD / EAD)

The work I do — pricing risk, reading loss experience, modeling uncertainty — maps directly onto banking risk. I'm actively targeting credit risk, market risk, ALM, and portfolio risk roles across banking and asset management, where actuarial rigor meets financial regulation.

Currently learning: Basel III · IFRS 17 · stress testing · risk-based capital · ALM · financial econometrics

📩 alesssxia@icloud.com · 💼 linkedin.com/in/-alessandravargas