Overview

A UK-based financial company offering student loans needed to identify misleading data from educational institutions, such as false promises and benefits, which were impacting timely loan repayments. This misinformation led to higher default rates and challenges in risk assessment, underscoring the need to maintain the integrity of loan processing and monitoring systems.

• Automate the daily monitoring of over 5,000 educational institutions.
• Track real-time public reviews to identify and mitigate risks proactively.
• Enhance loan processing for higher repayment rates.

Challenges

The client encountered significant challenges in handling loan requests, driven by the sheer number of educational institutions and the complexity of managing a large volume of data. Manual workflows were slowing operations, leaving room for human error, and failing to meet the increasing demands of tracking potentially misleading information.

• Monitoring over 5,000 institutions took weeks and required a substantial workforce.
• Identifying misleading or false information manually was time-consuming and prone to errors.
• Keeping up with public reviews from various platforms added another layer of complexity.
• The lack of a reliable real-time monitoring system left the organization vulnerable to missed risks.

The XDAS Approach

To effectively tackle the challenges of monitoring misleading information and loan repayments, the XDAS team began by thoroughly analyzing the client’s specific needs and pain points. Understanding the complexities involved in managing data from over 5,000 educational institutions, we implemented a series of automated bots and workflows.

Data Integration

The process kicked off with seamless integration of XDAS into the client’s existing systems and third-party applications, establishing a continuous flow of data that laid the groundwork for real-time monitoring and analysis.  XDAS deployed advanced Large Language Models (LLMs) to understand and process large volumes of text data to automate scanning educational institutions web pages. These models are trained to recognize patterns and language that may indicate misleading information, rapidly identify potentially misleading information, and minimize reliance on manual reviews.

The workflow

The team introduced an innovative ‘Web Change Monitoring‘ workflow to enhance oversight. This workflow tracks targeted web pages for new content and updates, informing the organization about changes that could impact loan repayments. For instance, if an educational institution updates its loan terms or makes a misleading claim, XDAS will immediately flag this change, allowing the client to take swift action. XDAS contextualized misleading information through advanced reports as data flowed in, transforming raw data into actionable insights that empowered the client to grasp risks comprehensively and act quickly.

Visibility of potential issues

Moreover, when discrepancies were detected, XDAS triggered automated alerts to relevant teams in real-time, ensuring immediate visibility into potential issues and facilitating timely interventions to mitigate risks associated with loan repayments. This proactive approach allowed our client to address emerging challenges and safeguard their financial interests.

Insights for better loan processing

Ultimately, the holistic insights provided by the XDAS platform enabled data-driven decision-making, streamlined loan processing workflows, and equipped teams with the critical information necessary to improve repayment outcomes. These insights include a comprehensive view of the client’s loan portfolio, real-time risk alerts, and analysis of potential repayment issues.

Solutions

Automated monitoring

XDAS automated monitoring over 5,000 educational institutions, providing near real-time access to potentially misleading information on review platforms and websites, enabling faster, informed loan approval decisions.

Contextualization of misleading data

XDAS transformed complex data into actionable insights by employing advanced reporting and monitoring dashboards, seamlessly integrating critical information into existing systems for faster decision-making.

Advanced scanning capabilities

Leveraging LLM models, XDAS intelligently scanned web pages and review platforms to identify misleading content, reducing the need for human intervention and enhancing data accuracy, which streamlined workflows and lowered operational costs.

Scheduled risk monitoring

The workflow was scheduled weekly to systematically detect new instances of misleading information and send real-time alerts to the relevant teams. This proactive approach enabled them to effectively manage risks and stay ahead of potential issues.