One can observe three major schools of thought in terms of trade finance and commodities financing:
1. Balance sheet-based financing
This type of corporate financing focusses mainly on companies with a stable and solid financial background, but requiring strong working capital to finance their core business. Such type of financing usually requires little control once the bank has decided to finance the company, and is based on the corporate’s capacity to reimburse. In other words, balance sheet analysis is the cornerstone of such a financing method.
2. A transaction-based financing method
This does not base itself on a corporate’s balance sheet, on the contrary, but on the goods financed. Indeed, one of the major characteristics of international trading companies, except for large corporates, is their relatively low capitalisation. With this method, the banks need to monitor the physical flow of goods since they represent their main collateral. Effectively, transaction-based financing requires a thorough evaluation of risks and an accurate follow-up of transactions financed.
3. Structured trade finance
The third form of financing is in fact a mix of the two financing methods explained above. This third method, including balance sheet analysis and transaction-based financing, has become much more common, and represents the future in terms of trade commodity finance. The purpose of such practice is that banks can back their risks both on collaterals and on a financed company’s balance sheet. This interesting way of financing can be defined as a tailor-made solution for a corporate needing financing depending on the particularities of their activities and their cash flows. The main difference with transaction based financing is a stronger balance sheet, allowing more complex and structured financing.
The second and third methods require a vast knowledge of the commodity markets and of the businesses seeking financing. In these cases, the risks will be limited in certain types of transactions due to the possession of the Bill of Lading (B/L). Nevertheless, the growing complexity of financing structures and the increasing demands of the supply chain, force the banking community to design more complex financing schemes more adapted to customers’ specific requests; the risks are higher but so is the revenue potential for the bank.
However, the bank specialising in transactional and structured financing need to weigh up several indicators in order to monitor efficiently this activity -more precisely the scrupulous respect of financing limits set for each customer, the level and type of commitments, the evaluation of its collaterals, and first and foremost a good comprehension of the different types of risks involved. A bank needs to look at the following risks:
- Customer risk or know your customer (KYC): A bank’s relationship manager must know their customer well, and identify whether the skills and professionalism of the latter represent a sufficient guarantee to finance a transaction.
- Country risk: A risk is evaluated differently depending in which country the goods are located at a given time. The fact that goods may transit from one country to another will have a strong impact on the risk calculation and evaluation.
- Market or price risk: Monitoring such risk is fundamental. With the goods being the only collateral for the bank, it becomes necessary to follow the price change of commodities. The more volatile the price of a commodity is, the higher the risk becomes.
- Counterparty risk: Banks need to evaluate the risk on counterparts of transactions they are financing. Indeed, the second step of a transaction involves a counterparty to which the goods will be sold, and whose payment will serve to reimburse the amount initially financed.
- Operational risk: The bank must put very strict internal procedures in place for this activity and make sure its employees follow them rigorously. Obviously, the set up of such procedures are accompanied by the implementation of IT solutions designed to apply these procedures in a secure manner, but also to help the bankers make quick and rational decisions based on valid data updated in real time.
As a matter of fact, banks are currently evaluating whether their existing trade finance systems are still in keeping with today’s market standards and with the evolving prerequisites of auditors.
In today’s post-crisis world, one major topic remains on the lips of bankers and software vendors: How can we improve risk management? And trade finance and commodities financing do not escape from such debate.
Furthermore, Basel II (and now Basel III) regulations oblige the banks to look more in depth on how they evaluate their risks linked to trade finance, since it will have repercussions on capital requirements for this activity.
If it is true that banks nowadays are more or less well-equipped with systems capable of supporting their back office operations linked to financial instruments such as letters of credit (LCs) and guarantees and collections, it is not obviously the case for more complex financing and the monitoring of its allocated credit limits, and the management of collateral. In this case, the most frequently used tool is an Excel spreadsheet.
The spreadsheet offers great flexibility for relationship managers to follow the evolution of their transactions, and establish the global economic position of a customer at a given time. The position is calculated on the spreadsheet by consolidating data manually coming from heterogeneous sources. The global economic position supports the decision-making process of a relationship manager or a credit committee, when deciding whether or not to finance.
Despite its proven flexibility, a spreadsheet is not sufficiently secure as far as the reliability of the data presented is concerned. On the other hand, this information supports the decision-making process for financing amounts up to seven or eight digits.
Therefore, there is an increasing market demand for innovative dashboard tools easily integratable in a bank’s IT infrastructure and capable of automating the extraction of data coming from various systems in order synthesise it in a tool capable of presenting a reliable, real-time view of a customer’s global economic position.
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