Division of Economic and 

Risk Analysis

U.S. Securities and Exchange Commission

February 19, 2016
The Commission, as a matter of policy, 

disclaims responsibility for any private 

publication or statement by any of its 

employees. The views expressed herein are 

those of the authors, and do not necessarily 

reflect the views of the Commission or of 

the authors’ colleagues on the staff of the 

Commission.
Leveraging Data and Analytics at 

the SEC to Assess Risk 

Scott W. Bauguess

Deputy Director and Deputy Chief Economist
Building a Risk Assessment Tool

4 

I. Understand market: Have experts

II. Develop quantitative measures of behaviors and outcomes: 

data from forms, filings, news, and other market sources

III. Build a model: Estimate using historical data

IV. Apply output and assess outcomes 

V. Generate feedback: educate experts, update required 

disclosure, recalibrate model
 BD-RA tool fully integrated into OCIE’s exam planning processes. Results of 

model help staff prioritize examinations across 4100 broker-dealers.

 Model tells OCIE examiners about outlier activities or certain changes 

over time at the broker-dealer relative to its “peers.” 

 Once the inputs causing the change is identified, OCIE examiners are 

better able to allocate resources, and prioritize and scope exams, which 

results in higher quality examination targets, significant findings, and 

more substantive referrals to Enforcement or FINRA. 

 Next steps:

 Operationalize through web-based user interface, so that Commission 

staff have real time access to analytics as soon as new data is received.

 Annual calibration of model based on examination results to refine and 

improve as results from exams are received/incorporated. 

Update on Broker-Dealer Risk 

Assessment

4 
 The CIRA Dashboard was developed in collaboration with the Enforcement 

Financial Reporting and Audit (FRAud) Group

 Designed to provide a comprehensive overview of the financial reporting 

environment of Commission registrants and to assist Enforcement staff in detecting 

anomalous patterns in financial statements that may warrant additional inquiry. 

 CIRA’s multiple dashboards enable the ENF staff to compare a specific company to 

its peers in order to detect abnormal, relative results; focus on particular financial 

reporting anomalies; and generate lists of companies for further analysis. 

 Next Steps: 

 Solicit feedback from ENF FRAud Group on new web-based dashboard.

 Continue to develop and enhance the analytic offerings within the 

tool......DERA is developing additional filters that identify suspicious 

patterns in the data for further review. 

 Incorporate additional data sources. 

Update on Corporate Issuer Risk 

Assessment 

5 
Investment Company Risk Assessment

Risk Rankings

Analysis of IC Activities Analysis of IC 

Characteristics

Flows

Returns

Expenses

Litigation

Gatekeepers

Cross-subsidization

Dividend Juicing Return Gap

Flow-Performance

Ad hoc/Event 

driven Analysis 

Cluster 

Analysis/Uniqueness 

Style Drift/Closet

Indexing

NAV analysis Affiliated IPO holdings

Text Analytics on 

Disclosures
 Natural language processing 

 Using text analytic techniques to identify ‘themes’ in unstructured 

narrative disclosures.

 This is done by applying statistical methods that look at distributions 

of (root) words in a document in relation to those in other 

documents

 Modeling methods like Latent Dirichlet Application (LDA) can be 

used to identify topics (themes) in disclosure that, once ‘trained’ on 

a set of documents, can identify similar themes in other documents.

 For example, registrant disclosures associated with misconduct can 

be used as a basis for training to identify similar disclosures in other 

documents

 DERA has structured 200,000 SEC filings, from 1993 to 2014 and is 

developing Text Analytic capabilities using this structured data.

DERA’s Text Analytics Initiative
Top 5 US GAAP Tags P

DisposalGroupsIncludingDiscontinuedOperationsDisclosure 99 %

MergersAcquisitionsAndDispositionsDisclosures 0.08 %

OtherIncomeAndOtherExpenseDisclosure 0.0006 %

BusinessDescriptionAndAccountingPolicies 0.0004 %

LoansNotesTradeAndOtherReceivablesExcludingAllowanceForCreditLosses 0.0003 %

Classifier
US GAAP Tag P

StockholdersEquityNoteDisclosure 68.0 %

PreferredStock 25.4 %

ShareholdersEquityAndShareBasedPayments 1.4 % 

TreasuryStock 0.7 %

EquityMethodInvestmentsDisclosure 0.3 %

Classifier