AI-powered system for predicting leasing demand

The leasing market is growing at a slower pace, and leasing companies are finding it increasingly challenging to expand quickly by acquiring new clients. As a result, there’s growing interest in solutions that can forecast the leasing needs of potential customers, where AI plays a crucial role.

We recently completed a project to develop an AI-powered system for predicting leasing demand for a leasing company. Built on the ValueXI platform, this solution helps identify contacts ready for repeat purchases, predicts the type of leasing product needed, and uncovers new potential customers. Since adopting the system, our client has enhanced communication with customers, lowered agent compensation costs, and boosted the average transaction size.

How the system works

The WaveAccess team processed historical data and developed a machine learning model to identify and recommend contacts most likely to close a deal soon. What’s crucial is that the solution is hosted within the client’s infrastructure, ensuring security and full control over their data. Worth noting that ValueXI is environment agnostic, offering deployment options that include both cloud and on-premise setups, facilitating access to third-party services.

Our solution also features a separate model to predict the most relevant leasing products such as passenger vehicles, trucks, or specialized equipment. Another AI model identifies companies that have not previously entered into leasing agreements but are likely to need such services: it provides a list of potential clients, and this data is used to initiate contact and tailor targeted offers.

The ValueXI-based system generates predictions automatically, gathering monthly data from public sources and counterparty verification systems for new clients. Leasing specialists can also analyze specific datasets by manually uploading data as required.

Results

The AI model processes a database of hundreds of thousands of clients and evaluates thousands of new contracts each month. In a recent analysis, the system identified interest in repeat transactions for around 14,000 contacts. Of these, nearly a quarter were qualified as leads, with sales managers achieving a 15% success rate in closing deals. Furthermore, around 1/2 of the organizations highlighted by the model subsequently signed contracts with other leasing companies, reinforcing its predictive accuracy.

With timely outreach to high-potential contacts, cold call effectiveness doubled. Our client improved upselling results and increased the average transaction size. Additionally, the use of the ValueXI platform accelerated AI integration into their processes by 40%.

Perspectives

Thanks to the collaboration between our client’s internal R&D team and WaveAccess, the project’s effectiveness was evaluated, and the AI capabilities within the company are now expanding to areas like customer risk profiling and communication tools.