The Evolution of Factoring with Integrations and AI

Written by: Darren Palestine, Managing Partner, Commercial Finance Partners

With advancements in technologies, it's interesting to see how factoring has lagged behind other financial sectors. However, the future looks promising with the potential for significant improvements in various areas of factoring: originations, underwriting, and portfolio monitoring. The integration of new technologies such as APIs, AI, and machine learning is poised to revolutionize the industry, providing new pathways for all aspects of factoring. I’d like to highlight a few areas that I believe will have the most impact, as well as a vision of how integrations and AI will shape factoring in 2029.

Integrations

One of the most significant technological advancements in factoring is the integration and aggregation of data sources. APIs have opened up new possibilities for factoring software developers, enabling them to bring in data from outside sources that historically had to run independently. This integration allows for more efficient review of information critical to underwriting and portfolio monitoring.

For example, the integration of customer credit data, financial data, personal credit, and background information into a single platform provides a comprehensive view of a client’s financial health. This holistic approach not only speeds up the underwriting process but also enhances the accuracy of risk assessments.

Real-time monitoring services, such as tax monitoring APIs that connect directly to the IRS, are already in existence and are being integrated into factoring systems. These services provide up-to-the-minute updates on a client’s tax status, ensuring that factors have the most current information available when making lending decisions. Similar real-time sources are also available for other metrics like credit reporting and background searches.

AI

The buzzword of 2024 has been artificial intelligence. Machine learning systems and AI have been immersed in many aspects of our day-to-day lives in both personal and business settings. We are just at the tip of the iceberg when it pertains to AI and factoring. Currently, there are use cases on the business development side through both marketing and lead generation activities. On a surface level, language models can help create content, develop contact lists, analyze market trends to identify new opportunities, among other activities. In deeper use cases, there are advanced automations that can be created, chatbots deployed on websites, advanced sales analytical data analysis and reporting, and marketing initiatives that can be implemented purely on an automated level.

On a much deeper basis, there is already a real-time use case for underwriting and portfolio monitoring. Language learning models can now extrapolate and provide meaningful data tied to financial documentation. Financial spreads, aging analysis, and predictive modeling can all currently be incorporated into underwriting tasks. Language learning models can even be used to summarize client information, like contracts with customers, and highlight risks or issues within those agreements. Portfolio monitoring can also be enhanced with AI tools that can be easily created for trend analysis, risk scoring, and other items that historically had to be run on a manual basis.

Factoring in 2029

I predict that by 2029, the factoring industry will have a complete technological overhaul as a result of integrations and AI. While not necessarily an overnight change, there will be third-party vendors that capitalize on the technology available to provide services to the factoring community. There will be new roles hired strictly for AI implementation and technology. Through integrations and AI analytics, factoring companies should be able to have streamlined access to data on a real-time basis, while also having that data analyzed through machine learning capabilities. Factors should be able to maintain a real-time viewpoint of a client and be alerted to changes within the credit profile of the business or individual.

Originations

Prospecting and follow-up communication will have a completely different landscape in 2029. Advanced CRM systems integrated with AI will transform the process of identifying potential clients and maintaining communication with them. These systems will be able to analyze market trends and client behaviors to identify the best prospects and tailor follow-up strategies accordingly. While the technology is available today, it can be difficult to integrate; however, that will begin to change rapidly in the near term and, by 2029, be a part of normal day-to-day CRM usage.

In 2029, there will be full-time AI Sales Representatives able to communicate over the phone, through email, or on a website chat. These enhancements will not only be available on an inbound basis but also for outbound reach as well. In almost all cases, it will be very difficult to distinguish between a computer-generated response and a human response. While it won’t replace some business development roles, the importance of relationship building will be even more critical and fundamental to success.

Underwriting

Underwriting should have an entirely new focus; most, if not all, manual underwriting processes will no longer be necessary. Underwriters should be able to leverage AI and technology to make quicker, more informed decisions when it comes to credit.

Machine learning algorithms can analyze vast amounts of data to assess risk more accurately and in a fraction of the time it currently takes. These algorithms can consider numerous variables, including financial statements, payment histories, market conditions, and even social media sentiment, to create a comprehensive risk profile. This level of analysis is beyond the capability of manual processes and results in more precise risk assessments.  Analysis on these items, which might have been done historically during the underwriting lifecycle, will be imbedded into a single task at the click of a button.  

In 2029, it will be more important for an underwriter to be able to review the aggregated data produced through machine learning than reviewing the data itself. Being able to interpret and prompt AI for the appropriate data will be a resume skill set that should be reviewed by factoring companies seeking to onboard new hires.

Portfolio Monitoring

AI should provide a complete overhaul of many portfolio monitoring activities. With the ability of AI to learn tasks, there is no reason AI can't be trained to do tasks such as verifying invoices, analyzing invoice trails for inaccuracies or conflicts, and detecting fraudulent activities. Continuous real-time monitoring of portfolio performance will allow factors to respond swiftly to any signs of distress or potential issues.

In 2029, AI-driven portfolio monitoring will revolutionize the factoring industry by providing real-time insights and predictive analytics.  Combined with language learning models assistance in day to day activities like verification, advanced portfolio monitoring will be the new normal.   AI will integrate seamlessly with external data sources, enabling continuous monitoring of client financial health and operational activities. This will allow factors to receive instant alerts on any significant changes, such as negative tax items, changes to business and personal credit scores, or transaction patterns that may indicate potential risks. With AI's ability to conduct deep trend analysis and generate predictive metrics, factors will be able to proactively manage their portfolios; this would be a sharp contrast from reactive risk mitigation strategies that many employ.  

The Road Ahead

As technology continues to evolve, the factoring industry must adapt to remain competitive. There are risks involved; implementing AI strategies without the appropriate development can be disastrous, especially as we rely more and more on AI and machine learning in our day-to-day lives. Business development professionals can easily fall into the trap of relying too heavily on AI and not enough on relationship development. In underwriting, it will continue to be important to have a human element involved versus just data-driven decision making. Having an AI risk mitigation handbook will be just as appropriate as regular portfolio monitoring guidelines.

If successfully managed and implemented, the integration of APIs, AI, and machine learning will not only streamline operational activities but also provide deeper insights into “what works.” Factors that embrace these technologies will be better equipped to source more opportunities, serve existing clients, make informed decisions, and reduce portfolio risks.

In conclusion, the integration of advanced technologies in factoring will bring about transformative changes, leading to greater efficiency, accuracy, and speed in originations, underwriting, and portfolio monitoring. By leveraging these advancements, factors can enhance their operations, reduce risks, and provide better service to their clients, ultimately driving growth and success in the years to come.

The views expressed in the Commercial Factor website are those of the authors and do not necessarily represent the views of, and should not be attributed to, the International Factoring Association.