Competing in an “X-Factor” style competition against IBM and Microsoft presenters, Christophe Spoerry, co-founder of the Euler Hermes Digital Agency, successfully pitched to a panel of judges and audience voters about why AI will have the greatest impact on the trade sector. Spoerry presenting on AI overcame competition from IBM’s pitch on blockchain technology and Microsoft’s focus on digital supply chains.
Highlights of Spoerry’s winning arguments on the benefits AI offers the trade sector include:
More efficient business to business (B2B) commerce processes
Despite the size of the global B2B market, many transactions are processed using dated financial solutions, including letters of credit and bills of exchange. Other barriers to commercial efficiency include poor credit management and a lack of collections or dispute handling processes.
In the business to consumer (B2C) market, however, transactions are efficiently handled via many well-established payment systems including Circle Pay, MasterCard, PayPal, Visa, and WeChat Pay. B2B commerce needs and deserves its own systems to transact efficiently in the digital world.
Euler Hermes estimates that just 5 percent of B2B trade leverages the benefits of credit insurance; receivables financing is similarly under-exploited. The cost to the world economy in missed opportunities and failed businesses is underestimated – 30 percent of bankruptcies are caused by delayed client payments. Disputes are not uncommon, can take up to three years to resolve and create major financial and legal stress.
Digitalizing B2B commerce with platformization and big data
While not all B2B trade is digitally-enabled, B2B e-commerce is already substantial and is the fastest growing area of commerce. Platformization and big data are fundamentally transforming traditions. Platformization includes the development of multiple B2B commerce platforms and marketplaces. Companies are increasingly shifting to SaaS solutions for accounting, CRM, invoicing, etc.
As a result, transactions are increasingly conducted between platforms, not humans in accounting departments. The platforms generate unprecedented quantities of data for everything they manage, in turn fueling an emerging ecosystem of specialists who interpret B2B commercial data.
AI-driven technology enables scalable access to business data required to make sound underwriting decisions. It’s now time to invent new products and processes equal to today’s technology.
‘Self-driving’ B2B companies
In the same way that self-driving cars will eventually free up valuable time, ‘autopilots’ in finance and accounting will increasingly become the norm. Intuit and Xero are examples of companies already moving in this direction; the journey has just begun.
Platformization and big data shifts mean that B2B trade can be mapped digitally, and AI-based trade navigation assistants can be built and trained based on companies’ accounting and banking data. By activating a “self-driving mode”, these assistants can take over after commercial negotiations: issuing purchase orders and invoices, collecting payments, handling disputes and claims – all in a timely, reliable and optimized manner. Building on accounting and banking data, companies will also start using “finance assistants” for cash-flow forecasting and credit management.
Mapping B2B trade and building trade navigation assistants and finance autopilots will ultimately make B2B commerce more efficient in two ways. At the transaction level, they will help companies optimize business relationships, minimizing payment delays and incidents. At the broader company level, they will actively manage sophisticated solutions like receivables finance and credit insurance, in alignment with company cash flows and business plans. While SME awareness of these solutions is currently limited, ‘robo-advisors’ have demonstrated effectiveness in B2C banking.
The artificial intelligence revolution won’t happen overnight. Not every company in B2B commerce will leverage trade navigation assistants in the short term, and payment behaviors take time to change. Building a trade navigation assistant need not wait, however. Data from just a few hundred participating SMEs in a given sector or geography is sufficient to gain fairly accurate predictions at the transactional level — as we and our partners have experienced at Euler Hermes Digital Agency.
B2B data from legacy providers such as Dun & Bradstreet and Experian is limited in many countries, but is sufficient to create trade navigation assistants in the Americas and Western Europe. Connecting with customers on platforms such as Xero or Quickbooks already provides partial access to their data.
Building a comprehensive map of B2B commerce requires an accurate listing of 50,000-100,000 companies worldwide. A transaction is relatively easy to model with machine learning. In B2B commerce, the variables to feed the model are relatively small in number and usually straightforward to interpret. The first generation of trade navigation assistants will ultimately progress from prediction to prescription: solutions will progressively manage more tasks, and reduce the inherent stress of B2B commerce including payment delays, liquidity risk and credit risk.