Pricing Risk Premiums in Supply Chain Finance

Financial Services

One of the most accessible and cheapest forms of finance in business is supply chain finance - used to improve cash flow performance.

$750M

Financing provided to SMEs to help them unlock cash tied up within their supply chain operations

80K

Due diligence documents processed and analysed to assess credit quality, liquidity and supply chain risks

36%

Growth in financing provided to SMEs based on a stronger ability to customise solutions and increase the velocity of transactions

Situation

Our client is a growing financial services business that provides supply chain financing solutions to small and medium sized enterprises (SMEs). They initially began operations as a due diligence consultancy. In 2013 they set up a new entity to provide SMEs with supply chain financing solutions based on the intellectual property and experience they acquired in underwriting transactions for commercial banks, private equity funds and larger businesses looking to acquire high growth companies. They wanted to create a data driven solution to accelerate the process of underwriting transactions and pricing risk premiums in financing transactions.

Technology

Our team analysed over 80,000 due diligence documents in order to develop a model that would help our client underwrite transactions across various international supply chains covering more than 90 different countries. The analytics involved three key steps. Firstly, the team set out to determine basic segmentation criteria by dividing customers into sub-groups to determine opportunities and risks within their supply chains. Secondly, we identified differentiating value drivers within each sub-group. For example, we helped the client differentiate a group of food and beverage companies into raw coffee bean producers, merchants, roasters, distributors and large retail outlets. Finally, we helped the client determine the operational constraints and advantages to funding certain types of transactions in certain countries. For example, we set a limit on the amount of time financing could be provided for perishable goods.

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Analytics

Our team applied a variety of advanced statistical data mining and machine learning techniques to the data within the due diligence documents, such as text mining, discriminant analysis, Support Vector Machines and Random Forests. We helped our client gain deeper insights into the value embedded within specific international supply chains across several industry verticals and geographies. We helped them prioritise funding of transactions where SMEs in emerging markets sold highly liquid and identifiable goods to large enterprises in developed markets, essentially helping provide funding to SMEs based on the credit quality of the end client in the value chain.

Results

Our solution helped our client achieve a 50% increase in lending volumes, a 36% increase in revenues and a double digit increase in net interest income margins.

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