Technology has always played a vital role in trade between global partners. Sellers, buyers and funders all rely on technology to ensure the seamless transmission of the data that supports the transaction between each partner from purchase orders to receivables to letters of credit. At the end of the process, corporates are looking for ways to monetise their receivables and want a coherent process that eliminates manual work and enables them to obtain the highest advance rates.
The challenge for the funder is how to manage the large volumes of receivable data generated by corporates and effectively monitor this data to reduce the risks and identify fraudulent transactions.
Several technological advancements aim to satisfy both needs.
One of the critical challenges is how to simplify the exchange of data between the various parties to ensure that all participants involved in the transaction can collaborate. Many funders are actively working on initiatives to facilitate this collaboration by creating networks that allow Corporates and their Bank partners to exchange information.
Many of these networks are using Blockchain technology because its distributed ledger system allows all parties to exchange data quickly in a secure and decentralised way. Blockchain as a technology reduces the risk of fraud as all parties involved have complete transparency and traceability as every interaction in the chain uses cryptography which prevents tampering.
Several Banks and technology companies have formed a consortium for creating a blockchain trade network. Some of these networks have facilitated several live transactions under controlled conditions, but there are two critical challenges faced by these networks:
- To create a scalable network that can handle many thousands of invoices for a single corporate to millions of transactions per day across the entire network;
- The collaboration between these networks – several Banks are involved in numerous consortiums to ensure they are involved from the start in creating the next generation trade network, but none of these networks are collaborating with one another.
Blockchain technology is an excellent solution for the digitisation and automation of documentary trade as it reduces complexity and paperwork. The use of smart contracts on these networks automates the process and reduces the administrative procedures. A smart contract is a pre-agreed set of conditions which once met automatically allows the transaction to proceed to the next stage. This technology significantly reduces the time that Corporates must wait for funding from days into minutes.
There are also several non-Blockchain initiatives such as the Trade Information Network (‘TIN’), which are aiming to create an open trade network that allows Corporates to upload their information and select the relationship banks with which they would like to exchange this information. TIN is an exciting development as it aims to get the backing of more Banks and will create an open standard across banks for exchanging trade data.
In addition to these bank operated trade networks, there are several Fintech platforms that allow Corporates to work with multiple funders, including non-bank funders. Corporates have a single integration point with the platform and can select and change funders as they choose. These platforms are often more sophisticated than bank platforms as they provide multiple products from Supply Chain Finance to Inventory Finance to Receivables Finance on a single platform.
As Banks open up their systems through APIs that can be used by Fintech companies to build platforms and services, a number of new technology solutions will start to appear that automate and improve the traditional services that were previously only provided by Banks. Technology solutions that help automate payments, reconciliation processes and give access to account information that will reduce time and risk for the corporates.
Robotic Process Automation
Robotic Process Automation is a new technical term for automation where repetitive manual processes are automated to allow employees to focus on creating business value. Corporates traditionally create invoices in their accounting package or ERP system and then send them to buyers by email or uploading them to eInvoicing platforms. Then same invoices are sent to the funder by uploading them or rekeying the information into the funders systems. Robotic Process Automation will remove the need for manual exchange of data and the manual verification of this data that currently takes place. Funders will use Robotic Process Automation to automate the manual approvals that are presently required allowing Corporates to receive faster feedback.
Technology is also helping to change the traditional credit models that are used by funders to manage risk and calculate the advance rates. These credit models are biased towards the funder, aiming to minimise the risk for the funder and often do not reflect the trading patterns of the corporate. Using Machine Learning (‘ML’), these credit models will become more dynamic as the credit algorithms will monitor the trading patterns and adapt the risk parameters when it identifies unusual activities. Machine Learning will track the trading patterns between individual corporate relationships down to the invoice level to identify any unusual activity, which may indicate a fraudulent transaction. With the use of ML, funders will be able to offer more complex receivable finance structures to SMEs who currently must use factoring or invoice discounting where the fees and advance rates are higher.
One of the key challenges for ML to identify fraud or risks is that it requires time to learn and recognise normal behaviour, or it needs access to large amounts of historical data. Funders may not have access to this data or where they do it may not be easily accessible. Technology companies that hold large amounts of trade data will be essential in helping to train these algorithms.
ML can also help with the automation of many manual processes. One of these processes is the reconciliation and allocation of payments to invoices. Payment reconciliation is difficult because a payment may be a partial payment, overpayment or a bulk payment with very little information in the payment about what it relates to. This is further complicated when multiple buyers pay into a single account. Machine Learning can be used to help with reconciliation by learning the payment patterns and automatically allocating the payments to invoices. Banks currently rely on the Corporates to perform this payment reconciliation in their role as the servicer, but in the future Banks could use machine learning to offer payment reconciliation service to corporates.
Cloud technology has been around for many years now, but most Corporates and Banks haven’t adopted the technology due to security and data ownership concerns. Banks and Corporates have continued to use internal infrastructure, which in some cases may have more security issues than using the public Cloud. As Banks and Corporates start to accept the Cloud, there will be more cloud-based platforms that will make it easier for companies to exchange data, visualise this data and utilise the data to obtain financing. Fintech companies have started creating cloud platforms that corporate treasures can use to analyse their receivables and payable cashflows, making it easier for them to manage their financing decisions.
One of the main challenges to the Cloud is regulation; regulation is forcing banks to become more local. Data sovereignty laws in certain countries prevent data being stored outside of the country, and this often prevents the bank from using public cloud solutions. Banks that operate globally can’t easily share information or generate global management reports across all regions. Public cloud providers such as Microsoft Azure are slowly expanding their global presence by creating local data centres.
Platforms that can utilise the public Cloud to create a global solution, which abides to sovereignty laws by processing and storing data in-country, will become very powerful. They will be able to help finance global trade by allowing regional offices access to the global solution.
Several technological initiatives are being explored by banks and FinTech companies to make it easier and quicker for corporates to finance their invoices. A number of these initiatives can be used together to create the next-generation platform that will help companies collaborate and share information whilst making it easier for banks to finance these trades through reduced risk.
Kishore Patel joined Demica in 2001. An experienced system architect and application developer, Kishore is responsible for the design, development, security and maintenance of all components of the platform.