Andy heads the risk management leadership team. He has spent 25 years building, validating, and governing quantitative models across the full range of financial risk — credit, market, operational, and beyond — at major institutions including PNC, BB&T, and Regions Bank. Before that, a combined 15 years of PhD training at Carnegie Mellon and academic research and teaching at Washington University and the University of Minnesota built the rigorous first-principles foundation that distinguishes how Spero Risk approaches every engagement. Since 2016, he has brought that combined depth to banks and credit unions of all sizes.

At Regions, in his first full year leading model risk, the MRM program received zero regulatory findings and the highest rating in the Federal Reserve’s 2014 horizontal review of model risk management across major U.S. financial institutions. Shortly thereafter, he centralized and expanded model development. Before Regions, he built BB&T’s SR 11-7 compliant MRM function from three people to fifteen at a $180 billion bank. That regulatory track record matters for a model developer: the same person who builds your models knows exactly what examiners will look for when they validate them.

The first-principles approach runs through everything Spero Risk does. Andy received teaching awards from Executive and Professional MBA programs at Washington University and research awards from the Western Finance Association (Best Corporate Finance Paper, 2001) and the American Accounting Association (Best Management Accounting Dissertation, 1994). He began his career at Mellon Bank as a credit officer, building M&A valuation software and teaching credit analysis. That combination — rigorous quantitative training, institutional banking experience, and a career spent explaining complex models to boards, regulators, and executives — is what makes Spero Risk’s work transparent, defensible, and built to last.

With the unmatched ability to synthesize and systematize the technical and the practical and their intersection, Chrissy defines and monitors project scope efforts including data collection, development, user acceptance activities, and resource allocation.

She is forward-thinking, and she challenges assumptions and prevents roadblocks. Using her strong theoretical understanding of predictive modeling, she identifies appropriate and economic analytical and modeling tools.

She has built loss forecasting model suites—both CECL and stress testing—from start to finish, including process automation, implementation, ETL, and performance monitoring. This work includes creating and overseeing exhaustive documentation for both technical and non-technical project phases, including training materials for all data management, ETL, and model execution functions.

Chrissy has both master’s and undergrad degrees in applied math from UAB.

Ben brings his meta-knowledge of programming languages—R, SAS, Python, whatever—to solve and systematize our most important development tasks. These skills are extremely valuable in combination with his strong conceptual math background and innate curiosity. There is never a task too big for Ben.

He brings a wide and deep set of technical and analytical skills to our development projects. With an encyclopedic knowledge of R and SAS, he has developed and documented data mapping, governance, transformation, and cleaning rules for data warehouses and has trained our clients on how to follow these processes and procedures.

Ben has led teams on all technical aspects on a variety of projects—from data prep to investigation to testing to final implementation (on our own platform or the client’s platform of choice). These tasks include data pipeline development, model development (of course), and report automation. He prioritizes automating the work for easy refreshing and updating and prides himself on producing intuitive, easily-explainable work.

He builds all types of loss forecasting models and besides PD and LGD models, he has extensive experience building prepayment and utilization models. He places an emphasis on data quality and governance.

Ben has a BS in mechanical engineering from The Ohio State University.

Dexter combines an exceptionally strong math background with the ability to think clearly and logically and apply theoretical concepts to derive practical solutions that tell “a reasonable story,” in his words.

By applying his sound technical skills combined with his natural curiosity and creativity, he frequently discovers innovative solutions to the problem at hand, and then focuses on building general, long-term solutions, i.e., building frameworks to automate both investigative practices as well as production processes—in R, SAS, VBA, etc. Using both private or publicly-available data, he has built models for all bank loss forecasting activities, and has implemented those models across a variety of commercially-available productions, including our own.

His clear insights and engaging personality allow him to quickly discover issues and relate those issues and proposed solutions to our clients.

When not working with bank data, he enjoys analyzing performance across a variety of sports.

Dexter has a MS in applied math from UAB and BS in math from Auburn University-Montgomery.