Scope
Reference
DN781589
Description
Border to Coast offers investment propositions across Public and Private asset classes that
deliver investment opportunities and management to our Partner Funds. As part of our
strategic plan, we intend to expand our range of services for our Partner Funds. A key
capability is to enable us to support Partner Funds with their SAA and Portfolio Construction
decisions. Therefore, we aim to procure an Asset Liability Management (ALM) model to
support the efficient implementation of these new capabilities. The ALM model will support
evaluation of scheme investment strategy and risk management, strategic investment
planning, asset allocation, and scenario analysis. The core functionalities and modules of the
ALM are expected to include Economic Scenario Generator and Capital Market Assumptions,
capable of accurate representation of Partner Funds underlying asset classes and investment
strategies, stochastic ALM projections and stress testing, and comprehensive reporting
modules. The ALM model will utilize the current scheme actuaries' liability data as input.
The contract will cover a 6-year period, followed by a review, with the expectation that if
satisfied, the contract will be extended for another 3 years (therefore 9 years in total).
Total value (estimated)
- £3,600,000 excluding VAT
- £4,320,000 including VAT
Above the relevant threshold
Contract dates (estimated)
- 8 December 2025 to 7 December 2031
- Possible extension to 7 December 2034
- 9 years
Description of possible extension:
The initial contract length will be 6 years with the option of an additional 3 year extension.
Main procurement category
Goods
CPV classifications
- 48441000 - Financial analysis software package
Submission
Enquiry deadline
9 September 2025, 12:00am
Submission type
Tenders
Deadline for requests to participate
12 September 2025, 12:00pm
Submission address and any special instructions
The tender will be published on Proactis ProContract.
Bidders who have not previously submitted an EOI will need to respond to the ProContract Expression of Interest/Advert, published under Opportunity ID DN781589, to be automatically invited to access tender documentation and submit bids.
Relevant registration and log in links are below:
ProContract Log in - https://procontract.duenorth.com/Login
ProContract Registration link - https://procontract.due-north.com/Register
If Suppliers identify a technical issue with the ProContract portal, they should contact Border to Coast without delay via procurement@bordertocoast.org.uk. All other clarification questions/communication must be raised via the ProContract portal.
Tenders may be submitted electronically
Yes
Languages that may be used for submission
English
Award decision date (estimated)
24 October 2025
Award criteria
Name | Type | Weighting |
---|---|---|
Functional Requirements | Quality | 70% |
Bid Price | Price | 20% |
Strategic Alignment and Partnership | Quality | 5% |
ESG | Quality | 5% |
Other information
Applicable trade agreements
- Government Procurement Agreement (GPA)
Conflicts assessment prepared/revised
Yes
Procedure
Procedure type
Competitive flexible procedure
Competitive flexible procedure description
The process will take part in two distinct stages:
Stage 1 - Down Selection:
This stage will be scored and the highest scoring maximum of 3 Suppliers will be invited to
participate in Stage 2.
Stage 2 - ITT Stage
Assessment and scoring of down selected Supplier bids, Where relevant, down selected Suppliers will be invited to take part in a Workshop at Border to Coast's head office in Leeds w/c 6th October 2025.
Reduced tendering period
Yes
Qualifying planned procurement notice - minimum 10 days
Contracting authority
Border to Coast Pensions Partnership Limited
- Companies House: 10795539
5th Floor, Toronto Square, Toronto Street
Leeds
LS1 2HJ
United Kingdom
Contact name: Border to Coast Procurement Team
Email: procurement@bordertocoast.org.uk
Website: https://www.bordertocoast.org.uk/
Region: UKE42 - Leeds
Organisation type: Public authority - sub-central government