Trade Finance as Investible Asset Class
Trade finance is a segment of the financial sector that supports Suppliers & Buyers or Importers & Exporters as they conduct their trade activities (selling & buying). In any trade value chain, this is THE most important thing. Today there are various traditional instruments including SME loans, LC, factoring, reverse-factoring, export credit and insurance, and they represent a safety net to protect corporates from the counter-party, liquidity and currency risks involved in trades (cross-border or otherwise).
The global trade finance market is worth $12trn USD annually, which is largely managed by banks and currently isn’t an investable asset class for institutions. Therefore we believe there should be a marketplace platform (or many marketplace platforms globally, powered by digital distribution strategy) which should open up trade finance to a more diverse set of companies than traditional credit scoring models do, expanding the market and enabling institutions to provide capital to emergent and fast-growing SME/SMBs.
Unlike many other lending activities, trade finance is mostly needed for smaller companies, with relatively small loan amounts, short terms, and borrowers lacking formal credit ratings. These are the businesses who are suffering the most because traditional financial institutions’ lending strategy doesn’t fit there. These are small ticket transaction and need extensive human resource (hence high cost of operation). Since opening branches in every nook and corner is almost impossible, most banks shy away from these kind of transactions.
We at DRGREP, serving more than 200+ Small Businesses in last 10 months with their Receivable Financing, understood that this gap can be filled in many ways. Capital can be brought into this segment not only from the financial institutions, but also from private individuals who can invest into these magnificent asset class. The only catch is earning the confidence of private individual investors — they want to play safe, they invest to always gain some more, never to loose a penny. Hence credit analytics is an important part of this asset class. Because this will deliver everything that needs to earn the trust of individual investors — credit risk, measurable ROI, exit plan etc.
The traditional approach to trade finance would require a lot of antiquated and labour-intensive bank processes to assess SME credit. With banks optimising their books and cutting branch networks in many countries, many information flows that the traditional approach is relying on are now broken. The existing bank credit underwriting and credit scoring approaches are very rigid and require the availability of specific accounting information from each applicant.
DRGREP’s approach to credit analytics:
- Leverages a broad set of available and emerging data sources.
- Uses many different company features as inputs, but does not place a hard requirement on the availability of most of them.
- Applies a rigorous evidence-based credit scoring process.
This approach allows for both better credit decisions and the improved access to credit for many SMEs.
Traditional credit scoring requires a small number of accounting entries and ignores most information available in typical company accounts, as well as any non-accounting information that is available for a company. At the same time, these models place a hard requirement on each of the entries used — the score cannot be calculated for any company that misses even one of these entries. That is, the model imposes a fixed depth requirement on the dataset, thus posing a severe restriction on its breadth.
This means that good predictive credit models should be able to accommodate varying data availability across companies. If a certain entry is missing for a company, the absence of this entry may give useful information about a company and a good model would incorporate this lack of information into the score rather than discarding the company. This approach allows us to use quite deep datasets, each of which may have just moderate breadth.
At DRGREP we are working on credit models that will combine accounting information (sales, expense, cash flow, payment, debt, asset etc.) about companies and enrich this data with geographical and socio-economic information. We will also factor in data like — how business owners are using the capital, how profits are being invested, how they are drawing salary etc. We plan to recognise data depth and breadth by carefully handling any missing entries. This means that if a company is missing a certain accounting entry, we are not excluding it from the training and test sets, but instead note the absence of data and learn from this pattern of absences.
We believe in next 1–2 years will have enough tools and processes at our disposal to measure the credit worthiness of SMEs and we can invite thousands of private individuals to invest directly into this asset class for better and safe ROI.
