Unifying Financial Crime, Fraud, and Cybersecurity: A Holistic Approach to Risk Management
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Banks are facing an increasingly complex and dynamic threat landscape, with evolving risks from financial crimes, fraud, and cybersecurity breaches. To stay ahead of these threats, banks must integrate their financial crime, fraud, and cybersecurity functions into a unified operating model.
Strategic Prevention: Redesigning Operations for Risk
To effectively tackle the evolving risk landscape, banks need to move beyond reacting to threats and instead predict risks before they occur. This requires redesigning customer and internal operations and processes based on continuous assessments of actual cases of fraud, financial crime, and cyberthreats.
Key Strategies:
- Continuously assess threat landscapes: Stay up-to-date with the latest trends and techniques used by malicious actors.
- Redesign operations and processes: Implement new procedures that proactively address emerging risks.
- Collaborate across functions: Foster a culture of collaboration between financial crime, fraud, and cybersecurity teams to share intelligence and best practices.
Efficiencies of Scale and Processes: Integration for Improved Threat Detection
Integrating the anti-fraud function can improve threat prediction and detection while eliminating duplication of effort and resources. By clarifying roles and responsibilities, banks can ensure no gaps are left between functions or within the second line of defense as a whole.
Benefits:
- Improved threat detection: Integration enables the sharing of intelligence and best practices across teams.
- Reduced costs: Elimination of duplication of effort and resources saves time and money.
- Enhanced collaboration: Improved communication and cooperation between teams leads to better decision-making.
Data, Automation, and Analytics: Enhancing Customer Identification and Verification
Integration enables banks to enhance customer identification and verification by combining internal and external data sources. Artificial intelligence and machine learning can also improve predictive analytics when supported by aggregate sources of information.
Key Strategies:
- Combine data sources: Leverage internal and external data to create a more comprehensive view of customers.
- Implement AI and ML: Use advanced technologies to enhance predictive analytics and customer identification.
- Continuously monitor and update: Regularly review and refine data sources and analytics to stay ahead of emerging risks.
Customer Experience and Digital Trust: A Unified Approach
A unified approach to fraud risk can result in an optimized customer experience, enhancing digital trust as a key differentiator for banks. Factors such as convenience, transparency, and control are important components of digital trust.
Benefits:
- Improved customer satisfaction: A seamless and secure customer experience leads to increased loyalty.
- Enhanced digital trust: Banks can differentiate themselves through their ability to protect customers from financial crimes and cybersecurity threats.
- Competitive advantage: A unified approach to fraud risk can provide a competitive edge in the market.
Examples of Successful Integration:
- Leading US bank: Set up a holistic “center of excellence” to enable end-to-end decision making across fraud and cybersecurity.
- Global universal bank: Combined all operations related to financial crimes, including fraud and AML, into a single global utility, achieving a more holistic view of customer risk and reducing operating costs by approximately $100 million.
By integrating their financial crime, fraud, and cybersecurity functions, banks can effectively tackle the evolving landscape of financial crime and prevent potential threats.