Michael Diez Email and Phone Number

Working on the Collections and Servicing team in the Credit Oversight space.

- Taken the lead in evolving the Real Estate Credit Risk team's knowledge of Python coding, particularly in the areas of report automation, dashboard creation, data visualization, and command line programming with respect to the CoreLogic Risk Model- Successfully conducts all the month end reporting for Home Equity and continued to produce month-end reporting and successfully helped to transition that reporting through the Home Equity loan servicing transfer. This required coordination and cross-validation with other teams- Performed initial impact analysis of VA risk-based pricing on applications, fundings, pull-through (app-to-fund rate), and presented results in Real Estate Risk Review- Developed and designed dashboard views to help understand the impacts of VA risk-based pricing on application volume, pull-through, revenue, and funding. Successfully presented this material in the September Real Estate Risk Review- Continued to push forward the Real Estate Credit Risk team's efforts at report automation by applying his processes and methodology to our Mortgage and Home Equity eligibility reporting. This is a very large and complex effort that has high visibility as this reporting is included on a monthly basis in the package sent to the Credit and Market Risk Committee- The reporting covers both monthly snapshot views along multiple dimensions (FICO, eligibility, geography) and vintage views. While the Mortgage reporting focuses on balance and delinquency metrics, the Home Equity reporting includes loss analysis. Automation process provides significant time savings on these monthly runs, reducing the time required to complete from 4-6 hours to 15-30 minutes- Core reporting and analytics responsibilities including: HE CLTV updates (including the semi-annual update of superior lien balances with Equifax), IRRRL tracking, mortgage pull-through, HELOC end-of-draw dashboard, and the HE CMRC memo

- Instrumental in facilitating Enterprise Operational Risk Management (EORM) workplace violence scenario analysis workshop with the Enterprise Security Group (ESG)- Diligently researched and compiled the operational loss data used at the workshop as a baseline across the industry for comparison to our USAA internal scenario- Assisted in compiling the data and insights garnered from this workshop into a succinct synthesis of the key insights, and briefed it to senior leadership- Provided the analytical efforts for the USAA Enterprise operational loss metric refresh by validating the prior metric and proposing a new, more robust metric for future use. This proposed metric would allow our operational loss data team the opportunity to provide timely insights into breaches of our triggers and appetites- Worked on the beginnings of our collaboration by performing sentiment data analysis, using Brandwatch, our social media vendor tool, to assist in following social media comments as it pertains to operational loss data capture, attempting to gain efficiencies by automating the gathering and ultimate dissemination of our loss data throughout USAA

- Performed exploratory data analytics and analysis utilizing big data - Designed and developed several non-linear, dynamical mathematical models- Conducted time-series and deep dive analytics and analysis to evaluate and assess mathematical model performance - Analyzed model parameters and inputs to ensure model use and efficiency was effective and appropriate- Analyzed model outputs in search of steady state solutions and conducted mathematical stability analysis- Utilized numerous computational and statistical tools i.e. Python, R, SAS, SQL, Maple, MATLAB, etc.

I served in the US Army from 1998 to 2007 and from 2012 to 2015 in a variety roles at various levels of leadership and management. I worked as a battalion operations and forecasting NCO from 2004 to 2007 performing the following tasks. - Conducted scenario analysis and provided analytic support as part of operational risk measurement, modeling, and analyses efforts- Provided short-term projections and long-term forecasts that were integral for operational planning efforts- Utilizing time-series data, performed analytics and analyses to optimize logistics and re-supply operations in an effort to maximize efficiency and effectiveness of finite resources- Performed trend analysis based on historical data to predict what will happen in the future, trend analysis was performed over multiple time horizons short-, intermediate-, and long-term - Presented mathematical and statistical findings to various levels of leadership that provided key decision makers invaluable insights