Resilient fraud management systems: What it takes to effectively integrate AI agents

By: Harsha Angeri, VP, Corporate Strategy & Head, AI Business, Subex December 19, 2024

Collected at: https://www.rcrwireless.com/20241219/uncategorized/integrate-ai-agents

Fraud management is emerging as a pivotal area where generative artificial intelligence (GenAI) and AI agents are providing cutting-edge fraud detection and prevention techniques that enable telcos to more effectively fight fraud. The telecom industry has long been encumbered by reliance on manual, labour-intensive processes, which are inherently slow and require considerable human effort and time. Today, however, technological advancements have paved the way for the deployment of AI agents, which are among the most effective tools currently available to aid telcos in their battle against fraud.

While the benefits, such as deep automation, ability to orchestrate complex workflows, apply decision-making logic, and adapt to changing business requirements are undisputable, service providers continue to struggle. Two of the most pervasive challenges telcos face is in receiving fast and consistent ROI, and in developing implementation strategies that provide the focus on methodologies that will enable them to track scalability and performance, seamlessly integrate AI agents within their operations, and comply with regulations.

Unlock fast and consistent ROI

AI agents provide cutting-edge detection and prevention techniques, enabling service providers to outpace fraudsters. For AI agents to deliver tangible outcomes and faster ROI, the following best practices should be followed.

1. Track the scalability and performance requirements of AI agents: 1) Invest in small language models that can be tuned for specific applications, 2) Ensure AI agents can learn incrementally through updated data, transfer learning, etc., 3) Ensure AI agents have the versatility to integrate with processes and handle a variety of data formats and structures.

• Build the right integration strategy with legacy systems: 1) Use a phased AI integration strategy that begins with non-critical systems, 2) Leverage a hybrid data management strategy that migrates critical data out of legacy systems to cloud or modern databases, 3) Deploy AI agents as containers to assist in the integration with older systems, 4) Monitor AI agent performance to ensure integration remains consistent and issues are quickly addressed.
• Establish a framework for AI agents to comply with regulations: Navigate the emerging landscape of AI regulations and fraud management through built-in compliance checks, audit trails, and risk management.
• Track AI agent performance using KPIs: It is critical to measure outcome to ensure that AI agents are meeting their performance criteria. It’s also important to evaluate performance across fraud tasks, AI general health and performance, customer impact score, and integration health score.  

Aside from the above high-level metrics, telcos should also measure false negative rate over time, AI decision override rate, cost of fraud detection per transaction, customer impact score, AI scalability index, innovative fraud detection rate, integration health score, model drift KPIs, and detection lead time.

Fight fraud with AI Agents

AI agents have the ability to set goals, create tasks, complete these tasks, reprioritise tasks, and make decisions to achieve set outcomes. Governed by certain characteristics that support the key capabilities needed to fight fraud, GenAI characteristics and applicability include:

• Autonomy: The degree of autonomy spans from fully autonomous to semi-autonomous agents.
• Responsiveness: AI agents can respond to changes in their environment, as well as anticipate future scenarios.
• Adaptability: Typically occurring through human guidance or autonomous learning methods, AI agents can improve their performance based on their experiences.
• Perceptiveness: AI agents can sense their surroundings through physical and software sensors, as well as indirect data sources.
• Ethical and transparent decision making: Increasingly, AI agents are being designed with mechanisms to ensure their decision-making processes are both ethical and transparent.
• Social ability: AI agents can interact with other AI agents and human personnel.

Realise the benefits of AI agents in fraud management

Implementing AI agents in fraud management brings a multitude of benefits, including:

• Increased efficiency and coverage: Agents free analysts from repetitive tasks, allowing them to focus on more complex fraud scenarios.
• Cost savings: Realised not only in terms of time and labour but also in minimising the financial impact of fraud.
• Real-time fraud detection: AI agents’ ability to instantly detect and respond to potential fraud minimises the risk of revenue loss.
• Faster response and quicker ramp-up: Agents quickly adapt to new fraud patterns, ensuring faster response to emerging threats.
• Scalability: Agents are inherently adaptable, capable of handling increasing data volumes and adjusting to evolving fraud patterns, future-proofing a telco’s assets.
• Enhanced decision-making: Agents provide access to precise and timely data, enabling analysts to make well-informed decisions, improving fraud management strategies and enhancing the broader business intelligence and planning strategies.

The integration of AI agents in fraud management represents a pivotal advancement for telcos, enabling them to move from traditional methods to more effective, streamlined, and forward-thinking operations – paving the way for a new era of advanced, efficient fraud prevention and management strategy. However, to ensure faster and consistent ROI from AI investments, it’s important for telcos to be cognisant of best practices when implementing AI technologies.