Photo of Abraham Geldenhuys, Kongsberg Automotive and Laurens Tijdhof, Zanders.
Kongsberg Automotive provides world-class products to the global vehicle industry. The company operates through three segments: interior, powertrain and chassis products, and specialty products. It employs 11,400 people.
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A trend of increased attention to intercompany loan pricing can be seen in all corners of the world as the international tax world is bursting with new developments. Some examples are the Anti-Tax Avoidance Directive (ATAD), the base erosion and profit shifting (BEPS) project and the new corporate tax regulations in the US.
This trend increased the challenge and the cost of compliance for Kongsberg Automotive significantly. As a response to the increased need for a reliable, consistent and efficient framework to determine arm’s length interest rates on intercompany finance transactions, Kongsberg Automotive implemented an advanced but easy-to-use web-based Intercompany Rating & Pricing (ICRP) solution from Zanders Inside.
The goal was not only to find a way to assess the compliant arm’s length interest rates for the many intercompany transactions, but also to store transparent and defendable documentation explaining the rationale behind the pricings.
The implementation of the Zanders Inside subscription-based solution facilitates an otherwise intransparent and complex process. The standardisation of the methodology and the transfer pricing report avoids any miscommunications between lender and borrower, time-consuming discussions with tax authorities and unclarity for all the stakeholders originating from different departments within the company.
The subscription model also makes sure that Kongsberg Automotive has access to a continuously up-to-date solution while being subscribed to a cost-efficient package fitting the functional requirements.
A mutually beneficial way of communicating with Zanders assures that input is taken into account and most of the time processed in newer versions of the solution.
The ICRP solution provides arm’s length interest rates on intercompany transactions based on the generally recommended ‘comparable uncontrolled price’ (CUP) – method and Basel credit risk methodology (PD, EAD, LGD and M).
The pricing analysis is done by means of data analytics. Each month, comprehensive data on more than 2,500 bonds is retrieved in order to feed the loan pricing algorithm. The outcome is a list of the bonds that are the most comparable to the financial transaction under review.
Additionally, comparability adjustments are calculated to reflect any differences between the comparable bond and the tested transaction. This pricing exercise is available for different intercompany finance instruments (term loans, current accounts, leases, guarantees), in different currencies, for different tenors, rankings (senior secured, senior unsecured, subordinated and near-equity) and repayment schedules.
Driven by innovation, Kongsberg Automotive implemented this best in class fintech solution in order to automate this complex process by leveraging emerging technologies like data-analytics in a cost-efficient and transparent way.
Kongsberg Automotive has a very complex organisational structure in place with a large number of legal entities and with various intercompany facilities (term loans, leases, current accounts).
Before the subscription solution, a very simplistic manual workaround approach was put in place to determine intercompany interest rates. An additional challenge was that the subsidiaries rely on explicit or implicit group support, meaning that a standalone credit rating would not necessarily represent the correct rates for intercompany facilities. Therefore, Kongsberg was looking for a new consistent but rational way of pricing their facilities in order to ensure compliance with local transfer pricing regulations.
Next to the above-mentioned transparency, the implemented solution resulted in a significant reduction in time-consuming repetitive tasks, while being fully compliant with the latest regulations. Last but not least, the business case of the solution is also supported by the reduced reliance on market data providers.