Bank Data, Automation, and Analytics Integration Boosts Customer Identification and Fraud Detection

In today’s fast-paced banking landscape, institutions are recognizing the importance of integrating their data, automation, and analytics capabilities to better identify and verify customers. By combining internal and external data sources with artificial intelligence (AI) and machine learning (ML), banks can produce rapid insights and correlations between credential attacks, account takeovers, and criminal money movements.

Unified Risk Management Fosters Digital Trust

The optimized approach to fraud risk also results in a better customer experience, which is critical for shaping customer behavior and enhancing business outcomes. By segmenting fraud and security controls according to customer needs and using automation and digitization, banks can enhance the customer journey and foster digital trust.

Holistic View of Financial Crime

To effectively manage financial crime, banks must adopt a holistic view of the evolving landscape, emphasizing independent oversight and challenge through clear roles and responsibilities. This requires integrating business, operations, security, and risk teams for efficient intelligence sharing and collaborative responses to threats.

Key Questions for Banks

As banks design their journey towards a unified operating model for financial crimes, fraud, and cybersecurity, they must ask key questions about:

• Processes and Activities: What are the key processes or activities for customer identification and authentication?
• People and Organization: Who are the relevant stakeholders in each line of defense?
• Data and Technology: What data should be shared across divisions, and can it sit in the same data warehouses? What tools and frameworks should converge, and how?

Leading Examples

Some banks have already made significant progress towards integrating their risk functions. For example:

• A leading US bank has established a holistic “center of excellence” for end-to-end decision making across fraud and cybersecurity, resulting in significant efficiency gains.
• A global universal bank has combined all operations related to financial crimes into a single global utility, achieving a more comprehensive approach to risk management.

Conclusion

By integrating data, automation, and analytics capabilities, banks can enhance customer identification and verification, reduce false positives, and foster digital trust. As the financial services industry continues to evolve, a unified approach to risk management will be critical for success.