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Improving performance and creating efficiency around income collection for Universal Credit - FREE breakfast seminar

  • Type HQN Event
  • Venue Milton Keynes
  • Start 06 Dec 2017 09:00
  • End 06 Dec 2017 11:30

Details

Improving performance and creating efficiency around income collection for Universal Credit

HQN and Mobysoft are hosting a free breakfast seminar examining ways social housing providers can improve their income collection performance and generate wider efficiencies for their business.

The morning seminar will run from 9.00am to 11.30am and include a series of presentations by landlords highlighting how they are looking to protect revenues against Universal Credit, as well as the opportunity to network with fellow housing professionals.

Speakers will be announced soon.

About Rentsense

Rentsense is a cloud based predictive analytic solution that optimises frontline income management workload. It creates and embeds significant efficiencies within the income function, reduces officer caseload on average by 50.8% and frees up 31% income team capacity. Landlords are using Rentsense to protect revenues, create efficiencies and mitigate welfare reform, it is also helping landlords reduce their arrears by an average of 15.8% in year one.

Testimonials:

“RentSense has dramatically reduced our arrears case load on a weekly basis. We have seen case load reductions of 56% per week, this has resulted in an officer capacity (or FTE) of 1.29, these efficiencies have allowed our officers to ensure that they spend quality time on those cases that need it.” Melville Housing.

“On the day of go live and training RentSense reduced our overall caseload from 2,761 to just 744 cases.” South Yorkshire Housing Association.

“RentSense is a business critical application.” Merthyr Valleys Homes.

Please note: HQN does not officially endorse the RentSense software but we believe our members will welcome the opportunity to hear more about the benefits it could bring to their organisation.

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This is a FREE breakfast seminar

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