Uber and its use of technology rapidly expanded the ridesharing industry. Todd Ehrlich of FactorCloud suggests that, similarly, leveraging artificial intelligence and machine learning will ultimately make factoring more attractive to lenders and affordable for borrowers, resulting in exponential industry growth.
By Todd Ehrlich
Factoring is a challenging and sometimes cumbersome business. The labor to buy just one invoice requires a deep understanding of the industry. One mistake can set a company back years if it misstates a legal document or buy a fraudulent invoice.
Labor per transaction is much higher with factoring than other forms of lending, and companies must be vigilant to catch the constant fraudulent transactions. Factors are under pressure to make critical decisions quickly and with limited resources. Unfortunately, hyper-vigilance and quick decisions do not always work in a practical sense.
Technology, Uber & Disruption
Generally, factoring has lagged behind other industries in terms of technology. With a relatively small market size, and high transactional complexity, most factors have avoided investing much money to modernize.
Other previously outdated industries can provide examples of what the future could hold for factoring. For instance, when you look at ridesharing pioneer Uber, it measured the ride-hailing industry at $4 billion in an early pitch deck. Today, the industry size is more than $56 billion.
Obviously, the technology behind new ride-hailing services made it much easier to request a ride and pay for it. But it was the use of multiple types of technology, layered on top of each other that really made it work.
It follows that factoring will grow exponentially when the right types of technology are stacked in the right way to unlock the expansion of the industry.
AI, Machine Learning & Computer Vision
AI and machine learning allow machines to perform tasks that normally require human intelligence, ranging from visual perception (computer vision) to decision-making and more. These technologies can minimize risk and fraud, make underwriting decisions more quickly and objectively, take over monotonous tasks and increase data analysis.
The functionality of these technologies soon will allow factors to do what they couldn’t have even dreamed of in the not-too-distant past.
The expansion of factoring through technology will be beneficial at both the borrower level and the lender level in multiple ways:
- Ease of use: Tech-based factoring will be so streamlined that companies will not have the same challenges they typically experience when operating with a factor as a lending partner. This reduction in complexity will, in turn, create a larger pool of customers wanting to participate.
- Higher advance rates: Factors will be more comfortable extending higher advance rates because there will be more data quickly and readily available to inform better decisions.
- Speed: The process of purchasing receivables and funding clients should rapidly improve to near instant levels for most transactions in the not-too-distant future.
Risk & Fraud
AI can use predictive intelligence to allow factors to be proactive in terms of risk and fraud. In transportation, if the ratio of trucks in the fleet to revenue being billed is out of alignment, AI can see this pattern and raise a red flag. Or if a regular client suddenly shows an unusual spike or decline in fundings, AI can bring this to an employee’s attention for investigation without the need to solely depend on an astute account manager.
On the other hand, a slow, steady increase in funding month after month may also be spotted by AI, which may suggest a proactive credit limit increase rather than a last-minute reactive response when there is a critical client need. As the systems continuously learn individual client patterns, they can begin mitigating risks and potential losses before a human even notices.
Underwriting is supposed to be objective, but since it is handled by fallible human beings, it inevitably ends up being subjective. Underwriters are under a lot of pressure to make fast yet accurate decisions, which is inherently challenging.
Imagine how much risk and fraud would be reduced if AI could run every transaction through a robust, multi-level data analysis. This could provide an automated score and allow the factoring company to decide whether or not to buy each individual invoice.
With enough data and machine learning in the mix, the system could begin to make (or at least recommend) buy decisions on its own. This would reduce the likelihood that a single fraudulent transaction could wipe out several years of profit.
Factoring is labor-intensive for several reasons, including the amount of paper to review, data to enter and reconciling to perform within the system. AI, computer vision and optical character recognition (OCR) can automate much of this. Computer vision and OCR can “read” purchase schedules, supporting documentation and payments, and AI can match them with the correct transaction.
This automation gets smarter over time, thanks to machine learning. For example, the first time an invoice from “Acme Customer” is processed, a human may have to designate which field is the invoice number, which field has the date and where the total amount is located. But the next time an invoice from “Acme” is purchased, the system will already know where to pull the relevant data, and it will be performed more quickly than a human could do it. More importantly, the risk of human error is vastly reduced, if not eliminated altogether.
Data & Decision-Making
The rapid decision-making of AI and machine learning will create huge improvements in profitability and efficiency. Data that was previously unavailable digitally, or was only able to be gathered manually, will now be integrated into every process automatically. This will lower costs, increase speed and reduce risk. The technology will recognize good transactions and predict bad ones.
What does this all mean? I predict that technology improvements will enable, and even encourage, more players to enter the market as factoring transactions become easier and the cost to apply AI goes down.
The most shrewd and experienced people in the factoring industry have always had a distinct advantage, and they will continue to hold those advantages for some time. But as technology democratizes the ability to run these businesses adroitly, capital will matter a lot. I expect to see larger institutions with significant balance sheets move into the space.
The expertise of existing factors will continue to be very valuable. I predict the best operators will be highly sought after by well-funded organizations who enter the market as AI makes factoring more accessible. Clerical work in the space will be automated, and independent companies and banks will thrive because of the lower labor costs.
The benefits of AI may be obvious, but there are larger implications for the industry. The lower labor costs and reduced risk of AI will bring competition and, ultimately, lower yields. Banks will be very competitive, but so will institutions with a lot of equity.
However, once factoring is a more efficient and affordable alternative for borrowers, many more borrowers will take advantage. Factoring will solidify a position as a widespread and accepted form of lending and will grow exponentially.
Todd Ehrlich is CEO of FactorCloud, a cloud-based factoring software that allows teams to manage more transactions without adding staff. Ehrlich also founded the recovery beverage Kill Cliff, Triserv Appraisal Management Company and two companies in the mortgage services space. He is a former Navy SEAL.
This article originally appeared in the March/April 2020 issue of Commercial Factor. Click here to view the original article (PDF – scroll down to pages 26 & 27).