Digitizing Case Management and Risk Scoring for Cross-Border Child Protection

Background

The International Social Service (ISS), an NGO assisting over 75,000 families annually, sought a solution to streamline and enhance its social case management processes. The organization faced challenges in utilizing expert knowledge effectively while safeguarding client confidentiality, which hindered efficient operations and data-driven decision-making.

Objective

The project aimed to:

• Develop an AI-powered case management system that enhances operational efficiency.
• Leverage publicly available data to overcome confidentiality barriers.
• Provide social workers with tools to access similar case insights, risk scoring, and key information swiftly.

Approach

Our data-driven approach involved several key steps:

1. Data Collection: Due to confidentiality limitations, the team collected 230 publicly available cases on child protection and abuse.
2. Data Processing: Applied Natural Language Processing (NLP) techniques to structure and analyze the collected data effectively.
3. Tool Development: Built a user-friendly web application with features such as case categorization, keyword identification, similar case references, and risk scoring.

Results and Impact

• Operational Efficiency: Caseworkers can now process and understand cases more rapidly, enhancing support delivery to families in need.
• AI Integration for ISS: This project serves as a foundational step in ISS’s journey toward integrating AI into its workflows.
• Scalable Solution: The AI-powered tool enables social workers worldwide to access and share knowledge collectively, driving better outcomes in child protection.
• Industry Advancement: The solution exemplifies the potential of AI in transforming the social service sector into a data-driven and efficient system.

For the technical results, find a case study here.

Future Implications

The successful implementation of this AI-powered case management tool highlights the potential for broader adoption of AI technologies in the social service sector. Future advancements could focus on:

• Expanding data collection while ensuring client confidentiality.
• Developing predictive analytics for case prioritization.
• Encouraging global collaboration to establish a centralized knowledge base for child protection cases.

This comprehensive solution demonstrates the transformative potential of AI in addressing complex social issues, setting the stage for innovation in the field of cross-border child protection.