Automation for accident reports processing

About the Client

The client is a commercial web-based service that supplies vehicle history reports to individuals and businesses on used cars and light trucks for American and Canadian consumers.

Automation for accident reports processing

Story Line

FiveS Digital works on document digitalization of accident reports of various cities through a portal, and situations have dramatically changed after post covid which affected the working procedure, so to provide a solution to the client we implemented various technologies for automation.

Challenges

Manual data entry processing required human resource which was a challenge due to conditions arising during Covid, and due to this TAT achievement went down.
Processing time taken for each image was 210 secs which was affecting the delivery and TAT.
The process had 100% audit allocation which almost costed 50-60% of the processing cost, so with higher volumes, 100% audit was a challenge, and random method affected the quality target which was 99.5%.

Automation Challenges

Multiple type of accident reports of various agencies with many pages and fields had to be identified through the system.
The entire data extraction process was a challenge due to unstructured pdf's along with implementation of process rules which were different for each agency.
At start of the project the processing time was reduced to 120 secs per image but still it was not optimized.

Solution

Robotic Process Automation(UiPath) technology was identified and implemented for automating the data entry processing along with AI and ML algorithms.
Identified the behavior, tenure, past performance and attendance-based logic, A quality sample size recommendation engine built on the defined logic through UiPath, Daily update of quality scores generates quality audit % user wise.
Basis the type of data an AI/ML Classifier was built through Python which supported in classification and data cleansing and alignment.
For attaining the best quality, data annotation and OCR was applied for machine learning and extracting data from pdf and converting to text.
Multi threading programming construct was used which reduced the overall execution time of per image processing.

Result

TAT achievement increased from 85% to 100%.
Audit % reduced from 100% to 16-30%
Handled 1500 records daily with consistent Quality achievement which was above the client’s benchmark 99.50%
Processing cost reduction by 50%.
Per image handling time reduced from 210 secs to 40 seconds per image.