model accuracy achieved
The client, a prominent figure in the automobile leasing and fleet management industry, operates an internal system (MIMS) for gathering vehicle repair and maintenance information associated with each leased vehicle. However, the client faces a labor-intensive process where repair requests for vehicles are manually approved or declined based on historical data and cost, resulting in a time-consuming and resource-intensive procedure.
Amazon Sagemaker
Amazon Elastic MapReduce (EMR)
Amazon Aurora
Amazon Cloudwatch
Amazon Simple Notification Service(SNS)
Amazon S3
AWS Lambda
AWS Cloudtrail
Apache Spark
Quantiphi developed a highly scalable and cost-effective system that utilizes a Machine Learning classification model, based on historical data, to automatically determine whether vehicle repair requests should be approved or declined. This approach significantly reduces the need for human intervention and substantially reduces the processing time for repair requests.
