Network features banking automation by improving context, boosting shell company detection, and enhancing entity resolution across risk domains.

The rapid emergence of Artificial Intelligence (AI in Banking) 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 banking automation within the financial 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.

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.

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.

Entity resolution is changing the game for banking

Entity resolution leverages advanced financial technology 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.

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.

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