1. 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

2. 63 166 204 108 188 216 155 210 229 210 234 242 240 106 0 247 183 125 243 152 68 250 218 188 110 192 64 151 210 118 183 224 160 218 239 207 242 1 108 247 61 150 249 127 185 251 191 220 75 100 125 150 2 Agenda Background Employer lifecycle Data requirements Impact of incorrect data

3. 63 166 204 108 188 216 155 210 229 210 234 242 240 106 0 247 183 125 243 152 68 250 218 188 110 192 64 151 210 118 183 224 160 218 239 207 242 1 108 247 61 150 249 127 185 251 191 220 75 100 125 150 3 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

4. 63 166 204 108 188 216 155 210 229 210 234 242 240 106 0 247 183 125 243 152 68 250 218 188 110 192 64 151 210 118 183 224 160 218 239 207 242 1 108 247 61 150 249 127 185 251 191 220 75 100 125 150 4 Valuing a single member

5. 63 166 204 108 188 216 155 210 229 210 234 242 240 106 0 247 183 125 243 152 68 250 218 188 110 192 64 151 210 118 183 224 160 218 239 207 242 1 108 247 61 150 249 127 185 251 191 220 75 100 125 150 5 Valuing all members Source: Sample LGPS fund

6. 63 166 204 108 188 216 155 210 229 210 234 242 240 106 0 247 183 125 243 152 68 250 218 188 110 192 64 151 210 118 183 224 160 218 239 207 242 1 108 247 61 150 249 127 185 251 191 220 75 100 125 150 6 Employer lifecycle Procurement Pensions Information Memorandum (PIM) Admission Contributions and bonds Ongoing Bond renewals, valuations and accounting Imminent exit Indicative cessation Exit Cessation

7. 63 166 204 108 188 216 155 210 229 210 234 242 240 106 0 247 183 125 243 152 68 250 218 188 110 192 64 151 210 118 183 224 160 218 239 207 242 1 108 247 61 150 249 127 185 251 191 220 75 100 125 150 7 How do we impact on the results? Financial Salary increases Pension increases Discount rate / future investment return Demographic Longevity Early leavers Retirement age Dependants

8. 63 166 204 108 188 216 155 210 229 210 234 242 240 106 0 247 183 125 243 152 68 250 218 188 110 192 64 151 210 118 183 224 160 218 239 207 242 1 108 247 61 150 249 127 185 251 191 220 75 100 125 150 8 How do you impact on the results? DATA

9. 63 166 204 108 188 216 155 210 229 210 234 242 240 106 0 247 183 125 243 152 68 250 218 188 110 192 64 151 210 118 183 224 160 218 239 207 242 1 108 247 61 150 249 127 185 251 191 220 75 100 125 150 9 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

10. 63 166 204 108 188 216 155 210 229 210 234 242 240 106 0 247 183 125 243 152 68 250 218 188 110 192 64 151 210 118 183 224 160 218 239 207 242 1 108 247 61 150 249 127 185 251 191 220 75 100 125 150 10 Impact of inaccurate data ScenarioABCD SexMMMM DOB01/01/1965 01/01/1956 01/01/1965 Pensionable salary £25,000 £52,000 £25,000 Date of Joining 01/01/1989 01/01/1998

11. 63 166 204 108 188 216 155 210 229 210 234 242 240 106 0 247 183 125 243 152 68 250 218 188 110 192 64 151 210 118 183 224 160 218 239 207 242 1 108 247 61 150 249 127 185 251 191 220 75 100 125 150 11 Impact of inaccurate data: liabilities (Date of Birth) (Pay) (Date Joined)

12. 63 166 204 108 188 216 155 210 229 210 234 242 240 106 0 247 183 125 243 152 68 250 218 188 110 192 64 151 210 118 183 224 160 218 239 207 242 1 108 247 61 150 249 127 185 251 191 220 75 100 125 150 12 Impact of inaccurate data: Contributions -3%

13. 63 166 204 108 188 216 155 210 229 210 234 242 240 106 0 247 183 125 243 152 68 250 218 188 110 192 64 151 210 118 183 224 160 218 239 207 242 1 108 247 61 150 249 127 185 251 191 220 75 100 125 150 13 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

14. 63 166 204 108 188 216 155 210 229 210 234 242 240 106 0 247 183 125 243 152 68 250 218 188 110 192 64 151 210 118 183 224 160 218 239 207 242 1 108 247 61 150 249 127 185 251 191 220 75 100 125 150 14 Vita’s lifestyle effect (postcode effect) High life expectancy Mid life expectancy Low life expectancy

15. 63 166 204 108 188 216 155 210 229 210 234 242 240 106 0 247 183 125 243 152 68 250 218 188 110 192 64 151 210 118 183 224 160 218 239 207 242 1 108 247 61 150 249 127 185 251 191 220 75 100 125 150 15 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

16. 63 166 204 108 188 216 155 210 229 210 234 242 240 106 0 247 183 125 243 152 68 250 218 188 110 192 64 151 210 118 183 224 160 218 239 207 242 1 108 247 61 150 249 127 185 251 191 220 75 100 125 150 16 Employer lifecycle Procurement Pensions Information Memorandum (PIM) Admission Contributions and bonds Ongoing Bond renewals, valuations and accounting Imminent exit Indicative cessation Exit Cessation

17. 63 166 204 108 188 216 155 210 229 210 234 242 240 106 0 247 183 125 243 152 68 250 218 188 110 192 64 151 210 118 183 224 160 218 239 207 242 1 108 247 61 150 249 127 185 251 191 220 75 100 125 150 17 What else could it impact? Benefits Benefit statements Finances of the employer GAD/EFA/Regulator Penalties

18. 63 166 204 108 188 216 155 210 229 210 234 242 240 106 0 247 183 125 243 152 68 250 218 188 110 192 64 151 210 118 183 224 160 218 239 207 242 1 108 247 61 150 249 127 185 251 191 220 75 100 125 150 18 In summary Data is crucial Need accurate information from you

19. Any questions? Thank you