Microsoft solves liquidity management challenge post-LinkedIn with RPA
In December 2016, Microsoft acquired LinkedIn for US$26bn, the largest acquisition in the company’s history. It was planned to integrate the treasury function which includes the cash positioning, intercompany lending, cash funding and sweeping.
For years Microsoft has embraced a cross company zero-balance account (ZBA) structure as a central liquidity management strategy. The major challenge that the company faced with LinkedIn wasn’t the banking solution, but rather it was limited by the fact that Microsoft was leveraging SAP as its ERP system and LinkedIn was using Oracle. This created journal entry and reporting challenges, as the two ERP systems were not in sync to record real-time intercompany transactions in an automated fashion.
While the in-house bank statements could track the cash transactions in all currencies, LinkedIn’s intercompany structure required all flows to be recorded in USD. At the time, Oracle was not able to support the USD translation without the need for a manual journal entry.
Microsoft and LinkedIn both agreed that to be successful, a technological solution would need to be implemented to support this growing business.
The Microsoft treasury team partnered closely with the GL and consolidations accounting teams, LinkedIn’s internal engineers supporting Oracle, and the robotic process automation (RPA) transformation team to identify potential solutions to automate the accounting postings. The teams align on a shared vision to implement a new RPA for the reconciliations.
The objectives were to create automated processes to both post the restatement of intercompany transactions into USD and allow automatic reconciliation of the manual journal entries with the in-house bank statement.
Best practice and innovation
Leveraging a cross currency, cross company ZBA structure, Microsoft treasury was able to reduce idle cash sitting in subsidiary bank accounts by over US$100m. Rather, Microsoft treasury was able to hold this cash in the portfolio, receiving approximately US$1.6m annualised incremental interest.
Leaning on RPA allowed Microsoft to improve resource utilisation while reducing risk of human error that occurs with manually posting journal entries. The cash concentration structure freed up more than eight hours a month for Microsoft treasury on time not spent performing daily cash positioning and intercompany funding per legal entity.
For LinkedIn finance, time saving was estimated at six hours per month not spent checking bank balances to ensure enough liquidity for AP runs and payroll. Through process automation, accounting saved over 50 hours per month on manual JE postings before considering the effort involved in reconciliations.
The bot technology was a successful test case and pilot project that required a deep partnership across treasury, accounting and business applications teams.
Through the implementation of foreign currency ZBA, Microsoft treasury was able to reduce idle cash sitting in the subsidiary bank accounts by over US$100m+ on average per month while ensuring automated, just-in-time funding for an additional 14 legal entities. Rather, Microsoft treasury was able to hold this cash in the portfolio receiving approximately US$1.6m annualised incremental interest.
The cash concentration structure has enabled Microsoft treasury to save a significant amount of time and resources previously dedicated to daily cash positioning and intercompany funding for each legal entity.
Huge time savings were also achieved for LinkedIn when assessing the sufficiency of liquidity to support its AP runs and payroll.
The solution also allowed the teams to take advantage of process automation, alleviating treasury’s dependency on accounting resources, saving over 50 hours per month on manual journal entry postings before considering the effort involved in reconciliations.