Artificial intelligence has transformed the world of banking and finance, in terms of customer experience and personalization of products. But it’s proving just as instrumental behind the scenes in improving fraud detection and regulatory compliance for finance firms big and small.
One of the key innovations driving those back-office functions is Agentic Retrieval-Augmented Generation (also known as Agentic RAG), which builds a bridge between data collection and analysis, and making the right decisions based on that data. This blog explores Agentic RAG banking in the context of AI fraud detection and regulatory compliance in the finance sector.
Agentic RAG refers to the practice of using AI agents to both retrieve information and generate results from them. Typically, agentic RAG will use large language models to collect information from different sources, be it structured or unstructured data; multiple agents can be deployed within the same system to cover multiple sources (for example, databases, web search results, email content and so on).
At face value, this might not sound all that different to traditional AI and machine learning models. However, there are three key differences:
Whereas traditional models can only work with what they’ve been trained on, agentic RAG can combine training knowledge with fresh data to generate more detailed, contextual insights.
This new information is incorporated automatically by agentic RAG by updating its knowledge database, without any need for model retraining or manual adaptation.
RAG models can indicate specific data sources that it has used to create its findings, which makes it easier to verify the accuracy of its output and identify anything that may be misleading.
Taking advantage of new technologies like Agentic RAG is critical at a time when the threat of fraud to banking and finance firms is unprecedented.
According to recent data, 60% of institutions have reported increased fraud attacks affecting both consumer and business accounts. As a result, payment fraud worldwide is the largest category of financial crime, with losses reaching more than $190 billion in 2023 alone.
At the same time, customers are increasingly aware of the risk of financial fraud, and so are looking to their financial providers to keep their funds and data safe. Those who don’t feel confident in their provider will quickly look elsewhere: Mastercard research has found that 91% of customers who experience fraud on a platform will use an alternative in the future. Even more concerningly, 86% of them will share their experiences with others, which can have a real impact on brand perception and customer retention.
In practice, Agentic RAG systems can transform the efficiency, accuracy and speed of fraud detection, even compared to traditional AI systems, in five key areas:
Similarly, agentic RAG can add new levels of depth and rigor to regulatory compliance, which is especially important in a sector so heavily and strictly regulated as banking. These functions include:
Agentic RAG can be complex to get right, especially for organizations who are still in the process of rolling out AI implementations more generally. From our experience, a gradual approach is the best way forward, validating each step before moving onto the next:
If all this sounds daunting, or you feel that you don’t have the in-house expertise and resources needed, then partnering with an experienced financial AI expert is the best way forward.
Contact the Ciklum team today and find out how we can tailor an agentic RAG solution to your fraud detection and compliance specifics.