Home / [RPA] Optimizing data entry processing in banking
[RPA] Optimizing data entry processing in banking
15000 hours
of labor saved per year
of document processing time reduced
of operation cost cut

Client Overview

Our client is a big Japanese banking partner.

Our Project

Business Challenges

Our client has over 200 branches operating in Japan and abroad. As a result, they have to manually process a significant amount of input data every day, taking up 12000 hours per year. This leads to slow customer response times, resulting in negative feedback from customers. Additionally, the data entry process is not standardized, with inconsistencies in capitalization, lowercase, and abbreviations, making information search and filtering challenging. They are struggling to efficiently process and synchronize information as quickly as possible.

Our Solutions

Automated data entry processing by RPA is the first and fundamental step in the success of the digital transformation process. Our project aimed to transform this operation to enhance efficiency with high accuracy. We consulted and built an automation roadmap using RPA solutions. This process aimed to establish a standardized and synchronized data storage and management library, thereby facilitating the timely processing of input data and satisfying customer needs.

Once bots in this project have learned and begun doing repetitive tasks, the whole data processing workflow becomes automated 100%. Our RPA solution followed a structured process with 4 key stages:

Use OCR to read documents in a scanning folder

Check the information on the internal system

Search for customers associated with that profile

Upload to the system.

In this project, using RPA integration with Optical Character Recognition (OCR) expands the automatic proficiency of data. Normally, OCR is limited to processing highly structured documents. However, with the help of RPA, it gains the capability to process and analyze unstructured data as well.

Key results

Saving 12,000 working hours per year

Reducing 76% in document processing time

Cutting operating costs by 20%.

Optimizing resources with the bot’s 24/7 operation

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