“A small team of A+ players can run circles around a giant team of B and C players.” — Steve Jobs
A recognized industry leader in risk management, stress testing, modeling, and model risk management, Andy has built models for 35 years in economics, credit risk, valuation, market risk, and loss forecasting. Nowadays, he especially enjoys building loss forecasting models for stress testing and CECL.
He has validated models for all bank activities and risks and has successfully established compliant model risk frameworks at two CCAR banks, when CCAR models faced substantial regulatory interest.
Prior to founding Spero Risk, Andy was a bank EVP and served as the head of strategic risk, model development, and model risk management functions. He has worked in model risk, market risk, and credit risk at three other CCAR banks.
He has been a frequent and popular speaker at model risk, validation, and stress-testing conferences and is the author of The Executive’s Guide to Model Risk Management, published in the ABA Compliance Magazine, and the presenter of the ABA’s Webinar on Model Risk Management.
A former business school professor at Washington University and the University of Minnesota, he has won both research and teaching awards. Andy has a Ph.D. in industrial administration from GSIA (now called the Tepper School) at Carnegie Mellon University and an MBA from the University of Pittsburgh.
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). Thes 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 model 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.