using business intelligence to manage risk · 2008. 12. 1. · risk data integration sas...

Using Business Using Business Intelligence to Intelligence to Manage RiskDavid Frankil, President, NAFCU Services Corporation

Jeff Hasmann, Senior Risk Solutions Architect, SAS

Copyright © 2008, SAS Institute Inc. All rights reserved.

What is Business Intelligence?

Business Intelligence is the process of, or applications and technologies for gathering storing analyzing and providingtechnologies for, gathering, storing, analyzing, and providing access to data to help make better business decisions.

Q & R tiQuery & Reporting

Analytics, both Descriptive & Predictive

Data Access & Integration


Predictive Analytics: Optimizing Your FuturePredictive Analytics: Optimizing Your FutureWhat’s the best that can happen?

What will happen next?

What s the best that can happen?

What if these trends continue?

Why is this happening?


Key Elements for Risk Governance

Define risk appetite (credit policy, target ti l )rating, leverage)

Establish risk reporting structurep g

Shape the risk culture

Establish risk review and audit process

Construct enterprise risk managementConstruct enterprise risk management infrastructure


Key Stakeholders for Risk Governance

Board of Directors

Loan Committee

C L l E tiC-Level Executives

Line Executives

Regulators


Risk Core Analytic Processes

Credit scorecards• “Factors” which drive portfolio performance (i e Sub prime to PrimeFactors which drive portfolio performance (i.e., Sub prime to Prime,

Credit score 550-575, LTV >90%, income band 3, zip code – XXXXX)

Monitor model performance

Portfolio risk analytics• Capital Allocation: Interest rates, ROI, Loss rates• Economic Capital: Charge offs, any losses over forecasts• Reserves Allocations: Rolling 12 months charge-offs, Impaired Assets

K P f I di t KPI’ R ll R t L P tf li G th• Key Performance Indicators – KPI’s- Roll Rates, Loan Portfolio Growth• Credit Cycle Forecasting: Fluctuations in losses/DQ’s• Stress Testing: Scenarios, extreme events


g

Credit Risk Analysis ProcessD hi

Credit Risk Analysis ProcessInputs Processes Outputs

Demographic HistoryHistory

Delinquency • Data Access/Loading• Data Cleansing

Transaction History

Balance Sheet

Delinquency

P di ti

g

• Descriptive StatisticsBalance Sheet History

Relationship & Event Histories

Predictionsp

• Predictive Modeling• Credit Cycle Forecasting

Other

Rating History Reporting Feedback

Non-Customer Data

History

Historical results


Historical results

SAS OpRisk SolutionSAS OpRisk Monitor SAS OpRisk VaRSAS OpRisk Monitor

Risk Control Self Assessment

Loss Data Collection

SAS OpRisk VaREconomic and Regulatory Capital

B i I di t LDA & AMAScenarios

KRI management

Issues and Action Plans

Basic Indicator, LDA & AMA methods

Mixing-in of Scenarios, KRIs, RCSA

Integrated Integrated

Issues and Action Plans

Risk Data Integration

SAS Operational Risk Education

RCSA

Flexible Modeling

What-if AnalysisIntegrated Integrated RepositoryRepositorySAS Risk Intelligence Portal

Risk Dashboard & Reporting

SAS OpRisk Global Data14,000+ publicly reported losses

360-degree view of risk (losses, KRI, RCSA results, issues, action plans)

Support qualitative assessments

Support quantitative models


Senior management view of OpRisk

For More Information on This Topic

www.nafcu.org/sasgwww.sas.com/industry/fsi/credit-unions/index.html


using business intelligence to manage risk · 2008. 12. 1. · risk data integration sas...

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