Best Risk Management Solution Highly Commended: Toyota Financial Services

Published: Jul 2019

Photo of Jeffrey DeSilva, Toyota Financial Services and Meg Coates.

Photo of Jeffrey DeSilva, Toyota Financial Services and Meg Coates.

Jay Lee

National Manager, Treasury Risk & Analytics


Toyota Motor Credit Corporation (TMCC), which does business under the Toyota Financial Services service mark, is one of the largest consumer finance companies in the US and one of the highest-rated captive auto finance companies in the world. It is a subsidiary of Toyota Motor Corporation. As of 31st March 2019, TMCC employed approximately 3,200 team members nationwide and had managed assets in excess of US$116bn.

TMCC sees big saving after rationalising and optimising liquidity risk

The challenge

Toyota Motor Credit Corporation (TMCC) runs one of the largest commercial paper (CP) programmes in the world, with over US$100bn direct issuance per year. It is one of the company’s primary short-term funding sources and liquidity management vehicles. The programme offers relatively cheaper borrowing cost given its short-term nature, but it also requires rigorous liquidity risk management as approximately US$200m-US$300m CP matures on any given day.

TMCC carries a short-term highly liquid investment portfolio to manage daily cash needs as well as to serve as the first line of defence in case of debt capital market disruption. Its investment portfolio is primarily funded by CP, which results in a negative carry cost, as investment yield is typically lower than that of CP.

While the investment portfolio serves as a risk buffer, it costs approximately US$20m-US$30m per year in carry cost. TMCC’s treasury was tasked to right size the investment portfolio to not only reduce the carry cost, but also optimise the liquidity risk profile.

The solution

After performing industry benchmarking and data analytics, the project team developed a solution in-house that includes three phases:

  1. Determined 30-day cash coverage is an appropriate amount for the investment portfolio. This affords treasury the right amount of time to deploy other contingency liquidity sources such as credit lines, securitisation and affiliate borrowings. It also conforms to the current banking industry practice under Basel’s liquidity coverage ratio (LCR), although it is currently not mandatory for captive finance company like TMCC.

  2. Optimised the CP issuance strategy which shifted some maturities from the one to around 30-day bucket into other maturity buckets, which in turn reduced the 30-day cash coverage requirement as mentioned above. At the same time, treasury was able to optimise the rest of the maturity buckets in order to maintain a similar portfolio average life and overall issuance cost as before.

  3. Developed a predictive data analytics model that can estimate future CP issuance and maturities, and automatically calculate ongoing cash coverage requirement.

Best practice and innovation

In collaboration with the CP sales and trading team, the project team was able to optimise risk profile while cutting down negative carry cost. By holistically examining the liquidity risk profile, treasury was able to identify areas it has some control over, such as the CP maturities which directly impact the cash coverage requirement. Reducing the one to around 30-day bucket of maturities lowers its short-term liquidity risk and allows a reduction in the size of the investment portfolio and its associated carry cost.

To better forecast future cash coverage requirement, the project team used various data tools (such as SQL Server, VBA) and techniques (such as linear programming) to create an automated process that brings current CP maturity profile and yield curve into a calculation engine which solves for a theoretically optimised future maturity profile and cash coverage requirement. Combining the machine assisted recommendation with qualitative assessment by the management team has elevated its decision-making capabilities in liquidity risk management.

Key benefits

  • Reduced negative carry cost by approximately US$7m per year.

  • Liquidity risk is better rationalised and optimised.

  • Demonstrated how artificial intelligence (AI) can support decision making in liquidity risk management. Building on the success, the team is looking to expand AI in risk management and other treasury functions in near future.

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