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Collaboration Against Crime: Harnessing Data Sharing for Smarter Fraud Detection

Publisher IDC
Published Feb 21, 2025
Length 18 Pages
SKU # IDC19656992

Description

This IDC Perspective explores the critical role of data sharing in combating financial fraud, emphasizing its transformative potential in early detection, broader insights, and operational efficiency. By leveraging collaborative data-sharing practices, financial institutions can address complex fraud schemes that span multiple organizations, such as synthetic identity fraud, account takeovers, and money laundering.This document highlights the benefits of shared intelligence, including enhanced machine learning models and cost savings, while addressing challenges such as privacy concerns, data quality, and stakeholder trust. It also outlines practical solutions including adopting interoperability standards, implementing secure data exchange platforms, and fostering collaborative ecosystems to strengthen fraud defenses.For technology buyers, the document provides actionable guidance on assessing risks, adopting automation, selecting appropriate technologies, and measuring ROI to maximize the impact of data-sharing initiatives. By addressing these aspects, the document underscores how financial institutions can unlock the full potential of collaborative fraud detection and prevention strategies."The future of fraud prevention lies in collaboration," says Sam Abadir, research director, IDC Financial Insights for Risk, Financial Crime, and Compliance. "By embracing secure data-sharing frameworks including vendor-provided software solutions and leveraging advanced analytics, financial institutions can shift from reactive to proactive strategies, enabling faster detection, stronger defenses, and a more unified fight against evolving threats."

Table of Contents

18 Pages

Executive Snapshot

Situation Overview

Introduction

The Role of Data Sharing in Fraud Detection

Benefits of Data Sharing for Fraud Detection

Early Detection and Prevention

Broader Insights

Enhanced Machine Learning Models

Cost Savings and Operational Efficiency

Challenges in Data Sharing for Fraud Detection

Challenges Abound

Privacy Concerns and Legal Constraints

Data Quality and Standardization

Trust Among Stakeholders

Solutions to Data Sharing Challenges

Strong and Scalable Reporting

Regulatory Compliance Strategies

Interoperability Standards

Secure Data Exchange Platforms

Collaborative Ecosystems

The Future of Data Sharing to Fight Fraud

Advice for the Technology Buyer

Learn More

Related Research

Synopsis

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