Receivables financing technology: the four developments to watch

06 Mar 2019

Media Coverage

The importance of new and emerging technology in trade and receivables financing are not to be underestimated.

The last few years have seen significant developments in this area, from the rise of e-invoicing and mobile applications to the ability to handle increasingly high volumes of data. At the same time, corporates have embraced the value of receivables financing solutions in helping them access working capital finance at favourable rates – while banks have increasingly come to see receivables financing as core to their product offerings.


As the market continues to develop, trade receivables platforms have much to gain by tapping into the opportunities offered by emerging technologies. Areas such as artificial intelligence (AI) and machine learning have much to offer when it comes to improving fraud detection and supporting AML, while platforms which support trade receivables securitisation have the potential to extend the reach of receivables financing to the SME market.


Against this backdrop, which new and emerging developments have the most to offer to receivables finance in the coming years – and which developments may take longer to fulfil their potential in this market?

Areas of interest

There are many areas of interest where receivables finance technology is concerned, but the maturity levels of different developments vary considerably.
Some technologies are already being adopted, while others will need more time to mature. The following are four areas in which technology may sooner or later lead to some attractive opportunities in receivables finance.

1. AI and machine learning
The International Data Corporation (IDC) predicts that worldwide spending on
cognitive and AI systems will reach $77.6 billion by 2022, up from an expected
$24 billion in 2018. With numerous use cases on the horizon for this emerging
technology, it is clear that AI has much to offer to the trade and receivables
finance area.


Based on past behaviour, machine learning has the potential to predict the time
taken to approve invoices. AI also has an important role to play in predicting potential instances of fraud before invoices have even been onboarded – for example, by identifying instances where a supplier is registered in a non-restricted jurisdiction, but the invoice address is located in a restricted jurisdiction. AI and machine learning can also bring benefits when it comes to flagging up risks relating to money laundering, even after the necessary KYC checks have been successfully completed.


As such, AI is likely to result in substantial benefits, from reducing risks to increasing the efficiency of manual processes. In order to leverage these opportunities, however, receivables finance platforms first need to become better and faster – and at this stage, the benefits of this type of technology are largely unrealised. For fintechs, the next step will be to take the leap into implementation and begin building test cases, refining algorithms and updating platforms so that they can take advantage of the opportunities.


Of course, certain challenges will need to be overcome before this can be achieved. Resourcing is one concern: there is a clear need for vendors to recruit people who possess the technical skills required to translate these ideas into full solutions. Although, the technologies themselves are still evolving towards the level of maturity needed for receivables finance technology. Therefore, it is likely to be another 18 months before AI is ready to begin fulfilling its potential in this area.


Another big issue is data quality, as such, AI can only learn a nd predict based on the quality and quantity of the underlying data set, in such , FinTech in collaboration with market participants have to work together to build better data lakes.

2. Securitisation and the opportunities for SMEs
In today’s market, receivables finance continues to be heavily geared towards big corporates. As such, there is plenty of scope to extend the benefits of these types of solutions into the SME market – and there is a considerable appetite amongst SMEs for receivables finance, particularly in light of the funding gap experienced by smaller corporations.


In practice, there are a number of technical and business restrictions to consider when extending the reach of receivables finance to SMEs. For example, challenges can arise when calculating the risk premium for this market. Unlike large corporates, which may have strong credit ratings, SMEs typically lack the size needed to obtain a rating at all. Many SMEs operate in fast-moving markets with shorter cycles, which can result in additional risks. Furthermore, in an uncertain geopolitical climate, developments such as Brexit could also have an impact on companies’ cash flow and invoices.


Calculating the risk premium that funders can apply to smaller companies may therefore require access to a sizeable volume of information. That said, these challenges are not insurmountable and there are plenty of opportunities for technology to mitigate the risks across this market. One solution is the use of platforms which have the ability to support trade receivables securitisation across a range of different systems, currencies and operating companies. This approach can be used to calculate the relevant risk factors effectively, while also maintaining proper checks along the lifecycle of the loans.

In order to do this successfully vendors must first be able to reduce processing times: the securitisation process must be fast enough to meet the corporate’s needs, while still providing the controllable risk levels needed by funders. Going forward, predictability may also have a role to play in facilitating securitisation by predicting default rates more effectively and thereby giving funders greater clarity about the risks they are taking.


3. Multi-tenant platforms
At the same time, there is significant potential in the development of cloud based
multi-tenant platforms. This type of platform can enable corporates to be served by a number of different providers, thereby providing both corporates and funders with access to a single platform to manage multiple funding relationships.

In order to achieve this, multi-tenant platforms must draw upon a large pool of data in order to calculate the risks associated with different receivables across a variety of markets. Platforms of this type also need to be able to review corporate structures, from legal entities to parent companies, to determine whether a risk premium can be shared across the company and how risk can be distributed to investors with differing risk appetites. The more data points the platform can access, the greater its resilience.This technology is more fully developed than more nascent technologies such as AI.


4. Distributed ledger technology
While some emerging technologies are already playing a role in the evolution of receivables financing, other developments have somewhat further to go before the benefits can be fully realised. For example, while there is plenty of impetus across the industry to explore business cases around distributed ledger technology (DLT) and blockchain, so far this has resulted in limited progress.


This area is certainly developing steadily, and the use of DLT has already seen success in some industries. Banking consortium R3’s Corda platform, for example, is making considerable headway in the insurance sector, where there is plenty of scope to automate manual processes and reduce fraud via the use of immutable information. The area of trade and receivables finance faces similar obstacles in the form of inefficiencies and fraud risk. However, while blockchain has the potential to deliver improvements in the long term – for example, by addressing the use of
paper and providing greater security and auditability – there are also some significant stumbling blocks to consider before these benefits can be realised.


One issue is the processing costs associated with DLT are curre ntly too high for the level of margin typically associated with receivables finance solutions. Another is the speed of processing does not readily scale to the needs of trade and receivables finance: while many distributed ledger technologies are currently processing in the hundreds, receivables financing vendors may process hundreds of thousands (or even millions) of messages per day. A further issue is that the node-based struct ure needed for distributed ledger technology does not readily apply to trade and receivables finance as it currently operates.


On balance, therefore, we don’t expect to see distributed ledger technology playing a major role in this area in the next five years – but looking further ahead, once processing rates and stability increase, this technology may become more applicable for the type of infrastructure required for trade receivables finance.

In with the new

The area of receivables finance technology continues to evolve. With banks
seeking more comprehensive end-to-end products, the developments discussed above could play an important role in the evolution o f this area in the coming years.


That said, it is important to understand that some of these developments will have more obstacles to overcome than others. For example, while AI is expected to deliver considerable improvements in the next couple of years, more work is needed before distributed ledger technology can have a meaningful impact on this area.


In conclusion, these technologies should not be seen as a form of disruption but as a means of enabling the receivables finance market to achieve better returns, greater standardisation and more efficient processes. Technology also has an important role to play in helping banks reach more clients – and ultimately in helping corporates around the world optimise their working capital.