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PG 1 CCAR/DFAST Data Wrangling Regulatory Environment Summary Fallout from the 2008-2009 financial crisis included the emergence of a new regulatory landscape intended to safeguard the U.S. banking system from a systemic collapse. In 2012, the Federal Reserve Board of Governors (Fed), began to require the largest U.S. Bank Holding Companies (BHCs) to file a Comprehensive Capital Analysis and Review (CCAR), with stress tests intended to assess the capital adequacy of these BHCs in mes of crisis. By 2015, CCAR and stress tests, now known as DFAST (aſter the Dodd-Frank Act Stress Tests) were expanded to include U.S. BHCs with between $10 and $50 billion in consolidated assets and foreign banks, whose exempt status expired. For banks, CCAR reporng and DFAST stress tesng are complex and data intensive endeavors with some of the following challenges: DFAST requires credit modeling and risk assessment at a granular level over vast amounts of data There is often a need for third-party data from sources such as Trepp to supplement internal data Retrieving, maintaining, & standardizing both internal and external data is usually difficult and time-consuming Subsets of data selected for reporting and testing must reflect the existing portfolio of loans at the bank Like many organizaons, BHCs store data in several data repositories used by organizaonal units such as Finance, and Treasury and Credit. Stress and risk models require on a repeve basis data from these silos augmented by a variety of external data. The laer include but are not limited to economic data, exogenous credit scores, and external loan augmentaon data such as data provided by Trepp. Most financial instuons simply do not have the experse nor the personnel necessary to efficiently meet their regulatory requirements. Thus, they require outside data preparaon and reporng assistance in the form of staff augmentaon or automated soluons. In 2013, a Fed report on the financial industry’s compliance progress noted that several banks’ revenue esmates were inaccurate due to data limitaons, and weak informaon management systems. Why do banks have challenges in data wrangling? Internal data silos 1 Incomplete data 2 Unstructured data 3 Requirements for CCAR reporting and DFAST stress testing result in complex data challenges for many banks. BHCs store data in several repositories, posing data integration challenges.

CCAR/DFAST Data Wrangling - Opex Analytics...CCAR/DFAST Data Wrangling Regulatory Environment Summary Fallout from the 2008-2009 financial crisis included the emergence of a new regulatory

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PG 1

CCAR/DFAST Data Wrangling

Regulatory Environment Summary

Fallout from the 2008-2009 financial crisis included the emergence of a new regulatory landscape intended to safeguard the U.S. banking system from a systemic collapse. In 2012, the Federal Reserve Board of Governors (Fed), began to require the largest U.S. Bank Holding Companies (BHCs) to file a Comprehensive Capital Analysis and Review (CCAR), with stress tests intended to assess the capital adequacy of these BHCs in times of crisis. By 2015, CCAR and stress tests, now known as DFAST (after the Dodd-Frank Act Stress Tests) were expanded to include U.S. BHCs with between $10 and $50 billion in consolidated assets and foreign banks, whose exempt status expired. For banks, CCAR reporting and DFAST stress testing are complex and data intensive endeavors with some of the following challenges:

DFAST requires credit modeling and risk assessment at a

granular level over vast amounts of data

There is often a need for third-party data from sources such as Trepp to supplement internal data

Retrieving, maintaining, & standardizing both internal and

external data is usually difficult and time-consuming

Subsets of data selected for reporting and testing must reflect the existing portfolio of loans at the bank

Like many organizations, BHCs store data in several data repositories used by organizational units such as Finance, and Treasury and Credit. Stress and risk models require on a repetitive basis data from these silos augmented by a variety of external data. The latter include but are not limited to economic data, exogenous credit scores, and external loan augmentation data such as data provided by Trepp.

Most financial institutions simply do not have the expertise nor the personnel necessary to efficiently meet their regulatory requirements. Thus, they require outside data preparation and reporting assistance in the form of staff augmentation or automated solutions. In 2013, a Fed report on the financial industry’s compliance progress noted that several banks’ revenue estimates were inaccurate due to data limitations, and weak information management systems.

Why do banks have

challenges in data wrangling?

Internal data silos 1

Incomplete data 2

Unstructured data 3

Requirements for CCAR

reporting and DFAST

stress testing result in

complex data challenges

for many banks.

BHCs store data in

several repositories,

posing data integration

challenges.

PG 2

Opex Analytics Experience

CCAR/DFAST Data Wrangling

Opex Analytics has gained substantial experience with extract-transform-load steps in support of CCAR and DFAST from past projects. BHCs typically start mostly manually assembling and preparing data. Due to quarterly and annual report requirements, such laborious processes soon become a burden. On top, regulators require creation of scenarios based on idiosyncratic risk drivers and granular loss estimates. The challenge lies in integrating various data sources that historically have served only specific purposes, including:

Most of the steps performed for each analysis can be automated by creating data marts and automated scripts to perform the following:

Often, banks overwrite data with newer information — e.g. borrower’s credit score on the day of the loan application — yet the previous versions become indispensable for loan modeling. On top there are several loan types such as adjustable, fixed, commercial and industrial, residential, etc.

PG 3

Opex Analytics Experience

CCAR/DFAST Data Wrangling

At Opex, we executed several projects with BHCs requiring automation of data wrangling. With our diverse knowledge and expertise in a variety of tools, we create solutions tailored for each individual client. The workflow consists not only of typical extract, transform, load steps, but is also augmented with advanced data cleansing techniques requiring technical and business knowledge, and specific methodologies to automatically understand unstructured data.

At Opex Analytics, we use the tool of your choice, be it open source Python, R or Java/C, or a commercial offering such as SAS. We assist banks in transitioning from manual extract-transform-load processes in support of CCAR and DFAST to automated and intelligent solutions.

PG 4

Opex DFAST Leadership Team

CCAR/DFAST Data Wrangling

Diego Klabjan, Ph.D. is a founder of Opex Analytics. He serves as a chief data scientist

and technology officer. Diego is a leader in the field of analytics. As a full professor at Northwestern, he is the Founding Director, Master of Science in Analytics. He was also in the first group of people to be recognized as Certified Analytics Professionals (CAP) by INFORMS. Diego is a full professor in Northwestern’s Department of Industrial Engineering and Management Sciences.

Bradford Winkelman is a senior data scientist at Opex Analytics, where he uses his

diverse background in optimization and statistical modeling to bring creative solutions to difficult problems. In addition to Bachelor’s degrees in mathematics and economics from Indiana University, he recently completed a Master’s degree in Industrial and Systems Engineering at the University of Wisconsin in Madison. His work experience includes statistical analysis of state highway maintenance quality assurance data, and various analytical roles at Bank of America. At the bank, he first worked within the risk organization, gaining experience in economic time-series analysis and geographic risk assessment, and later developed models for customer credit card behavior.