The Problem

A global credit information provider had major problems with their operations that made it challenging for them to grow their business. The product head talked about how difficult it was for them to put together credit reports, which took a lot of time and had to be done by hand, making it impossible for them to meet the growing demand in the market. Their primary objective was to reduce processing time by two-thirds while maintaining the accuracy of data from various sources.

Main Operational Problems

Chart showing main operational problems leading to processing limitations.

  • Data Integration Challenges

    The process was challenging due to the need to combine multiple data sections from various regional sources.

  • Quality Control Issues

    Checking data by hand caused problems with quality and consistency. The organization couldn't grow because generating insights manually required a significant amount of time.

  • Resource-Intensive Reporting

    Putting together credit reports took too many resources, which stopped the market from growing.

  • Processing Limitations

    Processing limits make it impossible to serve market segments that have never been served before.

Our Plan of Action

Implementation Phases

Chart showing implementation phases: Testing, Automation, and Full Solution.
1

Testing the Capacity

Initially, the client tested our capabilities by assigning specific tasks such as extracting registry data, scraping secure web content, and retrieving gatekept information at prearranged times. Each of the individual agents consistently produced high-quality results on all of these different tasks.

2

Slowly Automating

The client moved on to automated credit report generation because the results were satisfactory. We used a phased approach, automating a certain percentage of credit reports and planning to raise this percentage over time as trust in the system grew.

3

A Full-On Approach

To solve their main problems, we came up with a three-part plan:

  • 1

    Source Validation

    An integrated quality framework scans multiple data sources automatically to ensure that reports are reliable across all regional differences.

  • 2

    Pattern Recognition

    This makes it easier to combine different types of data with built-in checks for accuracy and quality so that it can work with different regional data structures.

  • 3

    Insight Synthesis

    Smart processing and creation of standardized outputs with in-depth insights for making decisions.

Our Solution

Automated Data Mapping System

Set up advanced mapping features to make sure that information is consistent across regions, even when it comes from different jurisdictions with different data formats and terms.

Automated Cross-Referencing System

Created smart validation processes that make sure data is correct by checking information from many sources against each other. This gets rid of inconsistencies that come up when people check data by hand.

Automated Analytics Engine

Made it possible to automatically generate standardized credit insights, replacing the need for time-consuming manual analysis.

Adaptive Web Navigation

Created systems that can adapt to changes in the structure of source websites, making sure that data continues to flow with little interruption. This new idea cut down on response time for similar changes to less than 24 hours.

Getting Past Obstacles to Implementation

During the planning stage, the team considered how changes to the site's structure would impact most extraction processes. To deal with these changes, the agents learned how to quickly change their route when they ran into navigation problems. Using traditional methods, these kinds of changes could take three days. However, our agents managed to circumvent the issue and completed the task within a matter of minutes.

Traditional Method

3 days to adapt

VS

Our AI Agents

Minutes to adapt

Business Impact

Operational Efficiency

90%

Faster turnaround time with full automation

60%

Fewer errors by eliminating manual inconsistencies

5X

Operations growth without additional resources

Expanding the Market

40%

More customers by serving new market segments

25%

More customers by expanding service capabilities

40%

More reports by covering a wider market

Strategic outcome

  • The business achieved growth in various areas without the need to increase resources at the same pace.

Strategic outcome

This partnership changed the client's business model from one that required a lot of hands-on work to one that could be scaled up and run automatically to serve a wide range of global markets. By automating data mapping, cross-referencing, and analytics generation, we assisted the client in identifying previously unattainable market opportunities. We also made sure that the credit information services they used met the high accuracy standards they needed.

The success shows how smart automation can get around basic operational problems, turning processing limits into competitive advantages and allowing quick market growth without having to spend a lot of money on resources.