The rapid emergence of Artificial Intelligence (AI) has rightfully shifted attention in the banking sector, largely due to its ability to streamline and improve operations. There are several reasons that the banking industry is primed for the intervention of AI, including the exponential increase in complexity and volume of data, mounting pressure for informed and meticulous business decision-making, as well as required transparency. While generative AI can competently address many of these issues, it is not the only vehicle that is capable of driving automation within the banking sector.
Context is key for AI
Carefully identifying input data points is of the utmost importance for successful risk modeling, perhaps even more so than the choice of algorithm or model itself. Considering the banking industry’s rigid regulatory requirements for both explainability and transparency, banks face constrained scopes for model selection. Given this constraint, input data is often the primary determining factor of success or failure of the model – underscoring the importance of maximizing contextual relevance of input data.
One way to infuse a significant volume of information into models, while upholding explainability and transparency, is through network-based features. This can be done by utilizing bespoke document-entity networks to create features that identify the interconnected relationships between individuals and businesses. In practice, leveraging these network-based features (which expose these previously mentioned relationships) can enhance performance of machine learning powered shell company detection models by 20%.
The outputs these models produce (predictions related to shell companies and the entities involved in their formation) can improve risk detection across several domains – Know Your Customer (KYC), Anti-Money Laundering (AML), Supply Chain Intelligence (SCI) and fraud mitigation being a few.
It is crucial that banks implement and leverage a composite tech stack that includes a range of both deep learning and machine learning techniques. These techniques, when coupled with large amounts of structured and unstructured data, are where the magic is made. This approach ensures that models are adaptable, accurate, and effective. Banks should avoid putting all their eggs in one basket in terms of relying on one AI model, technique, or approach. A well-rounded, holistic approach with multiple inputs and streams of information is best to avoid limitations in terms of perspective or performance.
Network features are the future
With the ability to model entity relationships across various contexts, networks offer versatile frameworks in which to understand conspicuous relationships hidden within data. For example, networks can depict payment transactions between parties engaged in financial crime. These depictions allow banks to analyze specific patterns and uncover risks that would otherwise go unnoticed looking at transactions one by one. When these networks are supplemented with data from known cases of fraud, learning models can be trained to spot future potential instances of fraud earlier on.
An especially important network to model corporate risk within is organizational legal hierarchy, including directors, shareholders and subsidiary parties. Basic attributes including network size, density of connections and layers within the hierarchy are just some of the chrematistics that can be used to slice and dice data within supervised learning models. Breaking down networks by characteristics such as these offers enhanced ability to mitigate risk.
At this point in the process, graph analytics begins to enter the scene. Graph analytics allows investigators and analysts to visualize and uncover hidden connections within disparate data sets that may indicate potential financial crime. This technology is both intuitive and scalable, ensuring teams can cover a wide base of data without letting any entities slip through the cracks.
Entity resolution is changing the game for banking
Entity resolution leverages advanced AI and Machine Learning techniques to dissect, cleanse and standardize data, unlocking unification of entities across disparate datasets. The process of entity resolution includes gathering related records, aggregating attributes for each entity and making connections between data points and sources. When compared to traditional record-to-record matching techniques, entity resolution is more efficient and effective.
To avoid having to link source records, organizations can create and implement new entity nodes, which serve as connection points for data. While entity resolution is largely known for linking internal data, it can also integrate valuable external data sources (like corporate registries, watchlists, negative news), solving a previously challenging issue.
Introducing entity resolution into the equation offers a significant improvement for banks transitioning from batch-based procedures to nearly real-time product-and-service offerings across omnichannel service frameworks. In addition to counter-fraud efforts, this approach can be leveraged when analyzing all customer interactions via numerous touchpoints (such as physical branches and call centers), resulting in the best possible customer experience.
The critical role of generative AI
Large Language Models (LLMs) will likely continue to grow in usage and popularity within the banking sector throughout the next year. Generative AI brings a level of intuition and conversationalism to banking interfaces, making it easier for analysts tasked with risk identifications. For organizations as a whole, benefits are also significant, for the advantages of LLMs and AI assistants span from junior personnel to some of the most seasoned investigators. That said, some of these assistants may be LLM-agnostic, offering greater flexibility to businesses to employ their preferred models (whether proprietary, open source, or commercially available such as ChatGPT). When combined with a composite AI stack, this technology will integrate with and support entity resolution and graph analytics, unlocking previously unseen potential.
It is important to remember that generative cannot be implemented in isolation to wider automation efforts. The insights generative AI is capable of generating are only as good as the data and context it has access to and is trained on. For banks looking to implement generative AI, it is crucial to start with thinking more broadly about their AI automation tech stack and the ways different technologies fit into it.
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