Implementing AI Agents in Fraud Management: Best Practices

Unlock Faster RoI from AI Agents

AI agents have arrived, bringing with them a flurry of innovations and applications. The area of fraud management is emerging as a pivotal area where Generative AI and AI agents can bring in cutting-edge fraud detection and prevention techniques that will put telcos ahead of fraudsters.

Another blog considers the characteristics and applications of such AI agents for fraud management. This blog delves into some of the best practices that telcos can follow to ensure their investments in GenAI and AI agents deliver tangible outcomes and faster RoI.

Best practice 1: Track the scalability and performance requirements of AI agents.

Telcos deal with vast amounts of transaction and network data that need to be scoured for suspicious activity, which makes scalability of AI agents a key requirement. This includes high computing power to enable real-time processing, Strong fault tolerance to avoid significant losses from system downtime, and dynamic resource allocation to scale up and down – both vertically in terms of more power, as well as horizontally, in terms of more machines. Instead of using large, and costly, AI models, it may be worthwhile to invest in small language models that can be tuned for specific applications. Another useful practice is to ensure AI agents can learn incrementally through updated data, transfer learning, etc., thereby mitigating the need for retraining the AI agent. Finally, they should be able versatile to integrate with, process, and handle different data formats and structures.

Best practice 2: Build the right integration strategy with legacy systems.

The IT landscape of telcos typically comprises legacy systems, making integration with AI agents a challenge. To address this, telcos can use a phased AI integration strategy that begins with non-critical systems, allowing them to assesss the performance and scalability of AI without disruption. Since AI agents require access to large volumes of data, telcos can leverage a hybrid data management strategy that migrates critical data out of legacy systems to cloud or modern databases, thereby making it available for consumption by AI agents. There are also many other ways to bridge the gap between slow, archaic legacy operations and nimble, real-time AI agents. Legacy system wrappers for capturing input/output signals, middleware for real-time processing, data virtualization for integrated data views, and APIs to exchange data in any format are a few examples. Deploying AI agents as containers can also assist in integration with older systems. Finally, continuously monitoring AI agent performance will ensure that integration remains consistent and issues are addressed, as they arise.

Best practice 3: Establish a framework for AI agents to comply with regulations.

In 2023, there were 25 AI-related regulations in the US, up from just one in 2016, and the total number of AI-related regulations has grown by 56.3% (1). As the regulatory landscape for AI becomes more stringent, telcos must be aware of key ethical and data privacy concerns. Fraud management requires access to large volumes of data, raising concerns over data privacy, ethical interpretations, consent, and compliance. For instance, effective detection often involves profiling customers and transactions based on personal characteristics. Telcos must follow the right policies to avoid discrimination and unfair treatment to ensure compliance and customer trust. Handling personal data too must be done in accordance with consent, control, sector-specific configurations, and data privacy laws like GDPR. Built in compliance checks, audit trails, and risk management will be critical to navigate the emerging landscape of AI regulation in fraud management.

Best practice 4: Continuously track agent performance using KPIs.

As with any transformation program, it is critical to measure outcomes to ensure that AI agents are meeting their performance criteria. There are some specific KPIs that can be used to evaluate performance across fraud tasks as well as general health. Some fraud-specific KPIs include such as false negative rates, detection lead time, innovative fraud detection rate, and cost of fraud detection per transaction. In terms of overall AI agent health and performance, the key metrics are AI decision override rate, AI scalability, and model drift KPIs. Apart from these, it is also important to measure customer impact score and integration health score to ensure that business goals of customer satisfaction and seamless business operations are being met.

Conclusion

To ensure faster and consistent ROI from their AI investments, it is important for telcos to be cognizant of best practices when implementing AI technologies. In the case of AI agents, it is crucial to focus on methods that track scalability, performance, integration, and regulatory compliance. Currently, all of these remain challenging areas for telcos. To navigate this with precision, telcos must consider modern data management techniques that unlock data for AI agents to consume, strong integration techniques, and the right mix of KPIs that continuously track AI agent performance, model effectiveness, and losses averted. Such an approach can cement the business case for AI agents and create resilient fraud management systems that detect fraud and secure trust in today’s digital age.

Reference

1. Nestor Maslej, Loredana Fattorini, Raymond Perrault, Vanessa Parli, Anka Reuel, Erik Brynjolfsson, John Etchemendy, Katrina Ligett, Terah Lyons, James Manyika, Juan Carlos Niebles, Yoav Shoham, Russell Wald, and Jack Clark, “The AI Index 2024 Annual Report,” AI Index Steering Committee, Institute for Human-Centered AI, Stanford University, Stanford, CA, April 2024.

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Sukshitha Rao

Sukshitha Rao is a Product Marketing Specialist responsible for Fraud management portfolio at Subex. She is a postgraduate in management from Symbiosis Institute of Digital and Telecom Management with Marketing as her major. She has about two years of work experience in the IT industry.