Strengthening Instant Payment Fraud Prevention with AI and Network Data

Instant payments, which allow money to be moved quickly and conveniently, have fundamentally transformed the financial industry. However, with increased speed comes the risk of increased fraud. This phenomenon is especially pressing in the UK, where fraudsters stole a staggering £1.17 billion through unauthorized and authorized fraud in 2023. Projections suggest that by 2028, Authorised Push Payment (APP) fraud losses will escalate to £636 million, with losses from instant payments alone expected to peak at £500 million. As fraud tactics continue to advance, the challenge for fraud prevention experts intensifies. To combat these sophisticated threats, financial institutions must adapt by leveraging the latest technological advancements in data analysis, AI, machine learning, and collaboration within the industry.

The Role of ISO 20022 in Fraud Prevention

The utilization of the ISO 20022 payment messaging standard is pivotal. ISO 20022 is not merely an industry standard but a transformative tool for fraud prevention. It allows banks to enhance data quality and gain deeper transaction insights by introducing a streamlined structure that enriches communication across payment systems. Unlike older formats, ISO 20022 messages include comprehensive details such as payee and payer information, purpose of payment codes, legal entity identifiers, and device-specific data.

This additional information helps banks identify potential fraud signs, such as unusual user behavior when initiating a payment. Although many financial institutions are still early in their implementation of ISO 20022, its potential extends far beyond compliance to significantly improve transaction pattern analysis and instant payment security. By adopting ISO 20022, banks can better detect anomalies and prevent fraudulent transactions before they occur.

Moreover, the enriched data provided by ISO 20022 facilitates more accurate and timely fraud detection. Financial institutions can leverage this data to build more sophisticated fraud detection models, which can analyze transaction patterns in real-time. This proactive approach to fraud prevention is essential in the fast-paced world of instant payments, where every second counts. Enhanced data insights mean banks can pinpoint and thwart fraudulent activities much more effectively.

Leveraging Network Intelligence for Real-Time Fraud Detection

Leveraging third-party data, or “network intelligence,” is essential for real-time fraud prevention. Massive amounts of payment data processed daily can reveal patterns indicating fraudulent activity that single-institution data cannot detect. For instance, ACI Worldwide processes £11 trillion in payments every day, from which substantial trends and insights can emerge. Network intelligence incorporates data from various sources, such as banks, merchants, billers, and payment infrastructures.

However, data privacy concerns necessitate anonymized data sharing. Signal sharing offers a solution where banks share anonymized transaction data with third parties, who then return a risk score or alert without compromising user privacy. Integrating insights from beyond financial data, including negative data from collaborations with social media platforms, can further uncover suspicious behavior and improve instant payment security.

Despite the promise of network intelligence and signal sharing, their adoption remains limited. In the UK, banks can pause suspicious payments for up to four days to investigate fraud, but this approach conflicts with the instant nature of payments and does not utilize signal sharing fully. Standardized fraud prevention methods across the UK and EU are needed to address these challenges, and updating outdated processes is imperative. Only by embracing network intelligence can banks keep pace with increasingly sophisticated fraud techniques.

Furthermore, network intelligence allows financial institutions to spot emerging fraud patterns across the broader financial ecosystem. By tapping into a collective pool of anonymized data, the sector can harness unprecedented insights and create a formidable defense against fraud. Institutions must prioritize the integration of network intelligence to transition from reactive to proactive fraud prevention strategies.

The Power of AI and Machine Learning in Fraud Prevention

AI and machine learning are integral to real-time fraud prevention. These technologies can process large volumes of payment data instantaneously and detect suspicious patterns. AI-powered solutions compare current activities with historical transaction data to identify potential fraud accurately. In practice, AI has proven effective: the US Treasury recovered over £3.1 billion in improper payments using AI, including £784 million directly attributed to AI.

AI’s continuous learning capabilities ensure systems remain responsive to emerging fraud tactics, providing a proactive stance against financial losses. By combining AI models with extensive datasets and industry-wide anonymized insights, financial institutions can discern legitimate transactions from fraudulent ones, reducing false positives and enhancing customer experiences. This not only improves security but also maintains the convenience and speed that customers expect from instant payments.

Furthermore, AI and machine learning can adapt to new fraud patterns as they emerge. This adaptability is crucial in the ever-evolving landscape of financial fraud, where criminals are constantly developing new tactics. By staying ahead of these threats, financial institutions can protect their customers and their bottom line. The investment in AI and machine learning technologies is essential as they help to accurately identify and mitigate fraudulent activities, ensuring a safer financial environment.

By utilizing AI models, banks can streamline their fraud detection processes and allocate resources more effectively. The precision and speed offered by AI allow for more focused investigations, ultimately creating a more secure payment ecosystem. The value of AI goes beyond immediate security, fostering long-term trust and confidence in the financial industry’s ability to safeguard its assets and customers from fraud.

The Need for Industry-Wide Collaboration

Leveraging third-party data, known as “network intelligence,” is crucial for real-time fraud prevention. The vast amount of payment data processed daily can reveal patterns of fraudulent activity that individual institutions might miss. ACI Worldwide, for example, processes £11 trillion in payments each day, offering valuable trends and insights. Network intelligence includes data from various sources like banks, merchants, billers, and payment infrastructures.

However, data privacy concerns require anonymized data sharing. Signal sharing is a solution where banks share anonymized transaction data with third parties who return a risk score or alert, maintaining user privacy. Expanding insights beyond financial data, such as negative data from social media collaborations, can identify suspicious behavior and enhance instant payment security.

Despite the potential of network intelligence and signal sharing, their use is limited. In the UK, banks can pause suspicious payments for up to four days to investigate, but this approach conflicts with the instant nature of payments and underutilizes signal sharing. Standardized fraud prevention methods across the UK and EU are necessary to tackle these issues, and updating outdated processes is essential. Only by embracing network intelligence can banks keep up with increasingly sophisticated fraud techniques.

Additionally, network intelligence helps financial institutions identify new fraud patterns across the financial ecosystem. By accessing a collective pool of anonymized data, the industry can gain unprecedented insights and build a strong defense against fraud. Institutions must prioritize integrating network intelligence to shift from reactive to proactive fraud prevention strategies, ensuring a more robust protection system.

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