Hymans Robertson LLP is authorised and regulated by the Financial Conduct Authority Northamptonshire Pension Fund Importance of good data Lorna E. Lyon 21 October 2014

Agenda Background Employer lifecycle Data requirements Impact of incorrect data

Northamptonshire Pension Fund More than 53,000 members Managing assets of £1,543m* Over 200 employers A few pensioners over 100. Shared resources and expertise with Cambridgeshire Pension Fund *as at 31 March 2013

Valuing a single member

Valuing all members Source: Sample LGPS fund

Employer lifecycle Procurement Pensions Information Memorandum (PIM) Admission Contributions and bonds Ongoing Bond renewals, valuations and accounting Imminent exit Indicative cessation Exit Cessation

How do we impact on the results? Financial Salary increases Pension increases Discount rate / future investment return Demographic Longevity Early leavers Retirement age Dependants

How do you impact on the results? DATA

Name Date of Birth Title Date of Joining Date of Leaving Pensionable Pay Final Pay Part-time Hours Full-time Hours Changes Certificates of Protection NI Number Reason for leaving Opt-outs Opt-ins NI Class C/O Earnings Marital Status Employer code Postcode Added Years Augmentation Additional Contributions Contribution Rate Officer/Manual Worker Maiden name Spouse’s details Service Credit - transfers Year end info What membership data should be stored? Sex

Impact of inaccurate data ScenarioABCD SexMMMM DOB01/01/ /01/ /01/1965 Pensionable salary £25,000 £52,000 £25,000 Date of Joining 01/01/ /01/1998

Impact of inaccurate data: liabilities (Date of Birth) (Pay) (Date Joined)

Impact of inaccurate data: Contributions -3%

Name Date of Birth Title Date of Joining Date of Leaving Pensionable Pay Final Pay Part-time Hours Full-time Hours Changes Certificates of Protection NI Number Reason for leaving Opt-outs Opt-ins NI Class C/O Earnings Marital Status Employer code Postcode Added Years Augmentation Additional Contributions Contribution Rate Officer/Manual Worker Maiden name Spouse’s details Service Credit - transfers Year end info What membership data should be stored? Sex

Vita’s lifestyle effect (postcode effect) High life expectancy Mid life expectancy Low life expectancy

Vita’s lifestyle effect (postcode based) High life expectancy Mid life expectancy Low life expectancy Source: Club Vita research based on VitaBank as at January 2013

Employer lifecycle Procurement Pensions Information Memorandum (PIM) Admission Contributions and bonds Ongoing Bond renewals, valuations and accounting Imminent exit Indicative cessation Exit Cessation

What else could it impact? Benefits Benefit statements Finances of the employer GAD/EFA/Regulator Penalties

In summary Data is crucial Need accurate information from you

Any questions? Thank you