Download - Session 61
Session 61
Financial Partners’
Journey Through
NSLDS
Pam Eliadis
and
Valerie Sherrer
2
Objectives• Identify how loan level data affects student eligibility
• Specify how the flow of enrollment data impacts timely conversion to repayment
• Examine ways financial partners can improve NSLDS Data Quality
• Recognize what data reported to NSLDS impacts the Cohort Default Rate
• Define NSLDS Security rules and user responsibilities
• Re-engineering NSLDS to Enhance Student Aid History Management
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Student Eligibility
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Student Eligibility
• Loan level data that affect Student Eligibility:
– Loan Statuses
– Originations
– Disbursements
– Cancellations/Refunds
– Outstanding Balances
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Student Eligibility
Schools see eligibility information:
1. ISIR
2. NSLDS FAP Website
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Student Eligibility
Loan Status
• Default Status is based on Lender Claim Payment
• Rehabilitated Loans should be reported timely
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Student Eligibility - Aggregates
• Three Major Variables:
– Net Loan Amounts (Guaranty minus
Cancellations)
– Disbursements
– Outstanding Principal Balance
Aggregate Loan Limits are calculated for each student on NSLDS to assist the FAA in
determining student eligibility.
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Student Eligibility – Loan Status
• Identification of Consolidation’s Underlying
Loans:
– Loan Status Code = PN, DN, PC, DP or PF
– Loan Status Date within 210 days (before or
after) of the Consolidation Loan Date
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Student Eligibility
Originations
• Report Originations as they happen
• Helps Schools see pending aid for Transfer Students
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Student Eligibility
Cancellations/Refunds
Factor in determining the Net Loan Amount for aggregate calculation
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Student Eligibility
• Disbursements
Factor in aggregate calculation
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Student Eligibility
• Outstanding Principal and Interest Balance
Amounts
OPB factored in aggregate calculations, OIB and Other Fees are not
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Enrollment Reporting
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Enrollment Reporting
• Impacts of Enrollment Reporting on Financial
Partners
– Enrollment Reporting Flow
– Changes to the Loan Status
– Impact on the Date Entered Repayment
– Effects of Non-Reported Enrollment Changes
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Enrollment Reporting
• How do students get on a school’s roster?
– Reporting of a loan
– School adds them to a roster
– Reporting of an ACG or SMART Grant
(as of Jan. 2007)
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Enrollment Reporting Flow
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Enrollment Reporting
When student drops below half-time attendance: Loan Status: IA to IG
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Enrollment ReportingWhen student re-enters school:
Loan Status: RP to DA
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Enrollment Reporting
• Impact on Date Entered Repayment (DER)
– Enrollment Effective Date used to drive DER
– DER is loan based, not student based
– GA Data Provider Instructions
• Grace = Separation + 6 months
• DER = Separation + 6 months + 1 day
• After entering repayment, DER does not change
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Enrollment Reporting
• Possible Effects of Non-Reported
Enrollment Changes
– Loan converted to repayment early
– Loan entering repayment delayed
– Borrower enters grace period without
knowledge
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Enrollment Reporting
Data Quality Monitoring
2006 IA/ID DA
Jan 260,389 65,378
Feb 282,469 82,381
Mar 294,921 91,004
Apr 290,532 87,451
May 297,569 87,417
Jun 281,140 73,435
Jul 265,881 64,955
Aug 261,977 60,826
Inconsistencies between enrollment
and loan statuses
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Enrollment Reporting
• Effective Enrollment Reporting and usage:
– Reduces risk of default
– Minimizes technical defaults
• NSLDS:
– Maintains the official enrollment data
– Provides GA’s enrollment data weekly
– Instructs GA’s to inform Lender/Lender Servicers timely
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3NSLDS Data
Quality
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NSLDS Data Quality
• Examine how Financial Partners can improve
NSLDS Data Quality
– Timely reporting to GAs by lenders
– Accurate Contact information
– Accurate information
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NSLDS Data Quality
• Benefits of Timely Reporting
– Timely reporting of information affecting
student eligibility
– New data reported quickly for decision making
– Effective program management and oversight
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NSLDS Data QualityAccurate Contact Information
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NSLDS Data Quality – Accurate Contact Information
• Contact information displayed on NSLDS web
sites
• Contact Information used for the Interactive Voice
Response Unit for 1-800-4FEDAID
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NSLDS Data Quality – Accurate Information
• Resolve reporting errors
• Close unconsummated loans
• Report loan transfers
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NSLDS Data Quality – Accurate Information
• Resolve reporting errors:
– Actively work reporting errors
– Analyze loan errors holistically
– Resolve for next submission
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NSLDS Data Quality –Accurate Information
• Non-Closure of Unconsummated Loans:
– May cause GAs to “assume” the loan status
– Creates conflicting information
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NSLDS Data QualityAccurate Information
Unconsummated Loan Aging
Assumed by the GA
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NSLDS Data Quality
• GA Data Quality Measures
– Review Benchmark reports
– Data Integrity Improvement Plans
– Reconcile Fee Payment Backup Data
– Annual Reconciliation with NSLDS
– Research Reasonability Report variances
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NSLDS Data QualityAccurate Information
Lender Held Loans OPB Update in Last 60 Days
0
20
40
60
80
100
Jan Feb Mar Apr May Jun Jul Aug
Month
Per
cen
tag
e (%
)
Average
Lowest
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NSLDS Data Quality – Accurate Information
Number of Identifier Conflicts by Month
Jan 2006 12,708
Feb
2006 12,691
Mar
2006 12,714
Apr 2006 12,623
May
2006 12,464
Jun 2006 12,601
Jul 2006 12,835
Aug
2006 13,208
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Cohort Default Rates
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Cohort Default Rates
• Recognize what data reported to NSLDS impacts
the Cohort Default Rate (CDR)
– Formula
– Fields
– Rates
– Frequency
– Adjustments
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Cohort Default Rates
Borrowers who entered repayment in FY04 and
defaulted in FY04 & 05
divided by
Borrowers who entered repayment in FY04
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Cohort Default Rates
• What NSLDS fields affect the CDR calculation?
– Date Entered Repayment (DER)
– Loan Type
– Date Claim Paid
– Claim Reason Code
– Loan Status Codes
– Student Identifiers
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Cohort Default Rates
• Two Lender Calculations
– Originating Lender Rate
– Current Holder Rate
• One GA Calculation
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Cohort Default Rates
• How often are the CDRs Calculated?
– Draft Cohort Default Rate (CDR)
• Calculate January
• Publish February
– Official Cohort Default Rate (CDR)
• Calculate August
• Publish September
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Cohort Default RatesCDR on the Web
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Cohort Default RatesCDR on the Web
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Cohort Default Rates• How do Financial Partners request an adjustment
to their CDR?
– Lenders:
• Data Correction to GA within 30 days of publication
• The GA has 15 days to respond
– GA:
• GA has 45 days to submit data correctionsCDR Guide for GAs and Lenders: http://www.ifap.ed.gov/drmaterials/FY04Cohortguide.html
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NSLDS Security
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NSLDS Security
• Define NSLDS Security rules and user
responsibilities
– DCL Gen 05-06
– Audit Reports
– Data Mining
– User IDs
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NSLDS Security
• Dear Colleague Letter GEN 05-06
– NSLDS may not be used for marketing
purposes
– Student/Borrower’s permission is required
– Reminds users of Federal Student Aid’s
enforcement obligation
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NSLDS Security
• Audit Reports
– Every student look-up is tracked by User ID
– Lender Audit Security Reports
• Requested by Destination Point Administrator
• Number of look-ups by user id
• Past 90 days
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NSLDS Security
No Data Mining
• Completely Automated Public Turing Test to Tell Computers and Humans Apart (CAPTCHA) tool
• Monitoring top 20 users and organizations
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NSLDS Security
• User ID’s
– Will be disabled after 12 months of inactivity
– Cannot be shared with colleagues
– Obtain through FSAwebenroll.ed.gov
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NSLDS Re-engineering
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NSLDS Re-Engineering
• NSLDS Functionality
– Monitor aid eligibility through applicant pre-
screening, post-screening, and transfer monitoring
processes;
– Receive student enrollment updates from schools and
their servicers, process and store this information in the
Operational Data Store (ODS), and then distribute
relevant enrollment updates to interested trading
partners (i.e. lenders, lender/servicers, guaranty
agencies).
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NSLDS Re-Engineering
• NSLDS Functionality
– Manage the default rate processes including calculation, distribution, and publishing of default rates
– Provide aid-level calculation services and provide SAHM operational reports and metrics
– Manage receipt of student, aid, and organization data to provide an integrated student view of financial aid history
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NSLDS Re-engineering
• Goals for Re-Engineering
– Align with Federal Student Aid Data Strategy
Efforts
• Implement FFEL data flow changes to facilitate
design and implementation of Information
Framework(IF)/ Student Aid History Management
(SAHM)
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NSLDS Re-engineering
• Goals for Re-Engineering
• Improve Data Usefulness:
– Data Timeliness
– Data Quality
– Program Monitoring and Oversight
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NSLDS Re-engineering
• FFELP Community Benefits:
– Data source becomes responsible for reporting
– Using current industry data exchange formats and
methods (i.e. XML Schemas)
– Standardized reporting for all life-cycle stages (i.e
CommonLine)
– Reduced duplicative reporting among FFEL
participants
– Interface Consistency
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NSLDS Re-engineering
• Students, Parents, and Schools Benefits:
– Timely information for making eligibility
decisions
– Enhanced data integrity
– Program Information Parity
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NSLDS Re-engineering
• Federal Student Aid Benefits:
– Improved Customer Service to all constituents
– Facilitates better decision making
– Enhanced data integrity
– Improved oversight of FFEL Program
– Meets Target State Vision for the Enterprise
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NSLDS Re-engineering
• Next Steps:
– Collaboration with community stakeholders
– Focus Groups being held in conjunction with
both Federal Student Aid conferences
– Definition of requirements
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Thank you!
We appreciate your feedback and comments.
We can be reached at:
• Phone: Valerie Sherrer 202-377-3554
Pam Eliadis at 202-377-3554
• Fax: 202-275-0913
• Email: [email protected]