Financial Economics Practice

In Financial Economics, our experts assist financial institutions and their legal counsel with managing exposure to credit risk and compliance issues, including discrimination with respect to pricing, underwriting, steering, servicing, and redlining on the basis of race, ethnicity, gender and age. Our work focuses on the quantitative analysis of regulatory risks associated with consumer, mortgage, and small business lending products, including those created by artificial intelligence and machine learning techniques. Recent assignments provided regulatory support with respect to the Fair Housing Act, the Equal Credit Opportunity Act, and allegations of Unfair, Deceptive, or Abusive Acts and Practices. This work has been performed over a wide range of consumer and small business lending products, including mortgages, auto finance, credit cards, student lending, unsecured products, as well as various depository products. Our team uses econometric and statistical analyses to examine application and loan level data at the origination, pricing, and servicing stages for depository institutions and non-depository institutions, including FinTech firms.

Full Time – We look for final-year undergraduate or master’s students pursuing a degree in economics, statistics, data science, mathematics or another quantitative discipline. We want our Analysts to have knowledge of both economic and financial concepts, research experience, quantitative ability, and exceptional written and oral communication skills. Candidates should be well-versed in Microsoft Excel, Microsoft Word, and have expertise with an econometric programming language such as Stata or SAS. Familiarity with Python and machine learning tools would be helpful.

Interns – We look for rising penultimate-year undergraduate or master’s students pursuing a degree in economics, statistics, data science, mathematics or another quantitative discipline.

Junior staff and interns will develop financial and economic analyses to support case theories. At CRA, staff will increase their familiarity with data that serves as inputs to these analyses, including company financial reporting data, regulatory reporting data, and social and economic survey data, as well as non-financial measures of organizational performance. The day-to-day life of Analysts can be highly-variable, but generally they can expect to spend:

• 50%-60% of their time creating code for the analysis of lending data in Stata
• 20%-30% of their time creating summary tables and graphs in Excel or Tableau and/or maps in ArcGIS
• 10% of their time discussing analysis strategy or case details with their project manager
• 10% of their time assisting on research projects and white papers

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