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February 15, 2017
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Operationalizing CCAR Modeling with Open Architecture

Comprehensive Capital Analysis and Review (CCAR) is a regulatory framework introduced by the Federal Reserve in order to assess, regulate, and supervise large banks and financial institutions – collectively referred to in the framework as Bank Holding Companies (BHCs). – (WikiPedia) Dodd-Frank Act stress testing (DFAST)-an exercise similar to CCAR- is a forward-looking stress test conducted by the Federal Reserve for smaller financial institutions. It is supervised by the Federal Reserve to help assess whether institutions have sufficient capital to absorb losses and support operations during adverse economic conditions. Under the current executive order affecting Dodd-Frank, BHCs would continue to be subject to quantitative review portion of the CCAR and the requirements for Dodd-Frank Act stress testing. – (Deloitte – Banking Regulatory Outlook 2017) This article focuses on the objective to address the challenges of CCAR through an open-source Business Friendly platform for Big Data Analytics.

Every year, an increasing number of Tier 2 banks come under the CCAR mandate. CCAR basically requires specific BHCs to develop a set of internal macroeconomic scenarios or use those developed by the regulators. Regulators also develop their own systemic stress tests to verify if a given BHC can withstand negative economic scenarios and continue to operate their lending operations.

As part of CCAR reporting guidelines, the BHC’s have to explicitly call out

  1. their sources of capital given their risk profile & breadth of operations,
  2. the internal policies & controls for measuring capital adequacy &
  3. any upcoming business decisions (share buybacks, dividends etc.) that may impact their capital adequacy plans.

Challenges

  1. In order to furnish upon these guidelines, Tier 2 banks are obligated to produce clean data sets that are not readily available and have to be assimilated from historical data sources. These information sets are often non-existent or unstructured in cases where they are available
  1. When made available, these datasets are scattered across silos each of which may have their own data repositories. Integrating front office trading desk data (Position data, pricing data and reporting) with back-office systems –  risk & finance are making the job of accurately reporting on stress numbers all the more difficult. BHCs have to rely on a manual and iterative process of horizontal reconciliation across the front, mid and back offices groups as well as vertical rapprochement across Finance, Treasure, and Credit silos before aggregating modeling outputs.
  1. Exposure for BHCs becomes further apparent with market pressures from competition and growth initiatives taking precedence over CCAR modeling, thereby, making it a non-revenue generating operational necessity. Some of these complementing pain-points are felt in the following areas:
    • Mobile technology and social media – explosive growth of smart devices have created a new distributed channel requiring banks to adopt mobile strategy to acquire a larger Share of Wallet from customers
    • Evolving consumer landscapes – millennials and retires have exhibited unexpected patterns of demand elasticity towards banking products. A bank that can build comprehensive models incorporating as many data sources as possible and build the most optimized pricing, will come ahead as a winner
    • Need for upgrading core banking platforms – A key factor testing BHC’s ability to adapt is real-time access to information and services demanded by consumer. Banks need to have their legacy core banking platforms upgraded in order to not only address real-time market behavioral changes but also architect the platform to scale for future unknown patterns and expansion
  1. Current set of reports are defensive and rigid. BHCs would need to adapt a reporting framework that is cost-effective as well as flexible and capable of rendering proactive alerts when producing regulatory outputs. Three forms of submissions are required – FR Y-14A (biannual), FR Y-14Q (quarterly), and FR Y-14M (monthly). CCAR results are also required to incorporate Basel III capital ratios in their reports.

A Vision for an Open Architecture

An open and scalable architecture can help address above mentioned challenges cost effectively with an analytical setup customized for periodic analysis and automating the operationalization of modeling process held under close watch of regulators.

Need for open architecture is better understood when considering the variety of technology and business units within BHCs in the figure below.

Following set of technology implications have to be considered in order for all business and technology units to align with CCAR requirements.

  • Besides computation & reporting library standardization, banks need to be able to perform common data storage for data from a range of BORT systems
  • Banks also need to standardize on data taxonomies across all of these systems.
  • To that end, Banks need to stop creating more silos data across Risk and Finance functions; a move to a Data Lake enabled architecture is highly recommended as a way of eliminating silos and the problem of unclean data which is sure to invite regulatory sanction
  • Banks need to focus on Data Cleanliness by setting appropriate governance and audit-ability policies
  • Move to a paradigm of bringing compute to large datasets instead of the other way around
  • Move towards in memory analytics to transform, aggregate and analyze data in real time across many dimensions to obtain an understanding of the banks risk profile at any given point in time

A Reference Architecture for CCAR..

Understanding these implications, we recommend an architecture that helps build capabilities around data assimilation, storage and compute capabilities along with ability to address the need for data taxonomy across BHCs’ affected divisions.

Operationally, this architecture will enable Administrators and CCAR stewards to define, annotate, and automate the capture of relationships between CCAR variables and underlying elements including source, targets and model derivation process.

Strategically, an open architecture allows BHCs to regain focus on day-to-day business critical operations impacting their bottom line and continuing to innovate around customers and products.

Conclusion

Addressing regulations like CCAR & DFAST through technology is not just about meeting quantitative requirements but also about adopting sound risk-management practices that require a holistic approach across the value chain (model development, data sourcing, reporting) across Risk, Finance and Treasury functions. A good risk-management practice should happen regardless of the prodding from regulators. Doing this can only ensure that the metrics and outputs of capital adequacy can be produced accurately and in a timely manner, thus satisfying the regulatory mandate.

References –

[1] Federal Reserve CCAR Summary Instructions 2016
https://www.federalreserve.gov/newsevents/press/bcreg/bcreg20160128a1.pdf

[2] Deloitte – Banking Regulatory Outlook 2017
https://www2.deloitte.com/us/en/pages/regulatory/articles/banking-regulatory-outlook.html

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