decor

Project Highlights

  • Implement an advanced credit scoring algorithm leveraging AI/ ML models to enhance risk assessment
  • Utilize of a variety of data sources including demographic, credit history, and transaction data to gain a holistic view of each customer's financial standing
  • 30% reduction in the average time taken for credit evaluation compared to before AI implementation 
decor

About Client

The client is a car rental service provider from Australia, offering a range of vehicles for both short-term and long-term usage. Their fleet ranges from economy cars for city travel to luxury models for high-end clients, and utility vehicles for business needs, ensuring that every customer finds a match for their requirements.

decor

Client Challenges

Difficulty in assessing credit and financial status

This challenge stems from limitations in their current risk assessment tools and methodologies, which fail to capture a complete and accurate picture of an individual's financial health. This problem is attributed to:

  • Reliance on manual entry: The reliance on manual data entry, which is prone to human error, leads to inaccuracies in the risk assessment process.
  • Lack of integrated data processing: There is no unified platform to efficiently combine and process information from the various data sources, leading to siloed and underutilized data.

Consequently, the client's challenges in their credit risk assessment process can affect their ability to safely rent cars to customers while maximizing their market potential. The company may disqualify individuals with sufficient creditworthiness but unconventional financial profiles. The inability to recognize and incorporate diverse financial indicators into their evaluation process can result in lost revenue opportunities and a narrower customer base.

With a constrained internal resource pool and a lack of specialized expertise in building credit assessment platforms, the car rental service recognized the need for software development company assistance. Our client understood that building a platform for credit risk evaluation required a level of resource investment and expertise they did not possess, which is where TMA’s expertise came into play.

decor

Solutions

Addressing the challenges presented by the client's current credit risk assessment process requires a comprehensive approach. Our tailored solutions are designed to ensure a more efficient, accurate, and reliable evaluation system.

Data collection and unification from multiple sources

Our first step involves the consolidation of data from a wide array of sources, including geographic locations, transaction history, social media and the diverse datasets currently stored in the client's CRM systems. This phase is crucial for creating a centralized database that offers a holistic view of each customer's interactions and behaviors across different platforms.

Data cleaning and abnormality detection

With the consolidated dataset in place, we proceed to clean the data using statistical indicators to identify and eliminate any anomalies or outliers that could skew the analysis. This step is vital for maintaining the integrity and reliability of the dataset, ensuring that the subsequent evaluations are based on accurate and relevant information.

Implementation of AI Models for Advanced Analysis

At the core of our solution lies the deployment of advanced AI models, with a particular focus on neural networks. These models are adept at handling complex tasks such as classification, regression, and predictive analysis, making them ideally suited for dissecting the intricacies of credit risk. By training these models on the cleansed and unified dataset, we enable a more dynamic and comprehensive evaluation of creditworthiness.

decor

Benefits

The AI project delivered the following benefits to the client:

  • Enhanced decision-making: With the refined credit scoring algorithm, the client is now able to make more informed decisions regarding whom to rent cars to, reducing the risk of default.
  • Improved risk assessment: The AI/ML models provided a more nuanced understanding of customer profiles, which allowed for more precise risk segmentation.
  • Increased efficiency: The ML system integration has automated credit evaluations, cutting the average processing time by 30% and enhancing operational efficiency.