Lease-to-own financing options open up access and purchasing power for those with slim financial history. A US-company offers simple, straight-forward options to help automobile owners get the tires, wheels, and minor auto repairs needed to keep their vehicles on the road. They promise an easy application process with instant approval for applicants with lean financial records.
Internal and external data from multiple sources - third-party and internal and user provided - were orchestrated and stitched together and analyzed to better understand the applicants , by studying the distributions, patterns and anomalies in the data.
The better the data that is provided to models to make decisions, the better the decisions. To gather the most relevant information to train the model, additional features like ratios, velocities, and frequency counters, were created from the available input data. For example, standard features like debt/income ratio or non-traditional features like email trust. This was done seamlessly using the AutoAI capabilities of the RapidCanvas platform.
The AutoAI platform automated the creation of the best possible model to predict, at the time of lease application, which applications are risky. With this white box approach, the internal working of the model and the importance of each factor used for prediction can be easily explained. In situations involving risk, it's important to understand not only if someone is risky but also why they are risky. Explainability is important for ensuring accountability, fairness, and transparency in automated decision-making systems.
What-if Analysis: Evaluation of a case depends on individual applicant profiles as well as the macro economic environment. It is important to be able to simulate ‘What if?’ situations. Play with different features and find how they impact predictions.
Interactive data apps were generated for business users to review leasing risk predictions and make data-driven decisions. With increased visibility into the risk profile of each applicant, the team was able to better understand the factors that influenced leasing risk and trends arising from the data.
With an ever-increasing pool of applicants and changing trends, the model is continually updated to ensure effective predictions are always available for the team.
The company's brand promise is an easy application process with instant approval for applicants with a sparse financial profile. AI and machine learning allowed the company to scale its customer base while ensuring the brand promise could be reinforced.
The team was able to detect risky lease applications and positively impact their revenue, to the tune of 10%.
With the insights provided using dynamic real-time machine learning models to predict future outcomes, the leasing company's team could better assess and manage risk both during the application and the ongoing payback period.
The interactive data apps gave the team a deeper understanding of customer insights. The data apps showcase a 360-degree view of each customer, segment and cluster of users to better understand groups of customers with similar patterns and behaviors, and to analyze and explore alternative outcomes.