COMPLIANCE
What is a large language model ?
While GenAI and large language models are interconnected concepts , they differ subtly . GenAI represents a broader category of AI that crafts original content , while a large language model focuses on generating pertinent , humanlike text .
If an AI model is trained on an extensive library of books , it learns sentence structure , word meanings and even linguistic subtleties . Armed with this understanding , the AI model can generate new paragraphs , narratives or even full articles that seem like they were crafted by the same human author . AI can become an inspirational collaborator , helping authors churn out fresh ideas and creative content .
GenAI and large language models have an array of applications across diverse fields . They can find uses in art , literature , music , design and numerous other areas where creativity is crucial . They push the boundaries of what is possible , expanding the limits of human imagination .
How are large language models applied in different industries ?
Owing to their flexible nature , large language models have found myriad uses across various sectors . A few notable applications include :
• Content creation and copywriting : Initial drafts of marketing content , legal briefs , product documentation , business letters and more .
• Chatbots : Siri , Alexa , customer service agents , help desk support tools , etc .
GenAI and large language models in the fight against financial crimes
What implications does this have for your institution ? Quite frankly , there are many implications . Here is how AI language models can serve as potent tools in the sphere of financial crime detection , such as money laundering and fraud :
• Suspicious activity reporting : One of the most time-consuming and detail-oriented aspects of the investigative process is compiling , analyzing and drawing conclusions from various data sources . Investigators spend weeks combing through transaction data , adverse media searches and entity aggregated data . AI models can automatically synthesize these disparate data sources and generate narratives or descriptions about suspicious activities detected in these sources of information . These narratives can be included in suspicious activity reports ( SARs ), making the reporting process more efficient , comprehensive and less time-consuming .
With all the advantages GenAI and large language models bring to the table , it is crucial to acknowledge that there are potential risks associated with this rapidly evolving technology
• Automated compliance : A time and resource-consuming aspect of compliance is change management . Addressing regulatory changes across various jurisdictions and aligning them to existing policies at an FI requires a welltrained workforce that constantly aligns regulatory guidance with internal policies . AI models can automatically scan for regulatory changes , compare them against existing policies and generate updated compliance policies based on the latest regulatory guidance by jurisdiction . This ensures that FIs always adhere to the best practices in AML .
• Financial crime investigative support : Financial crime investigations require the collection and analysis of various sources of information , analysis of transactional trends and behaviors , as well as the investigation of counterparties . All this can be time-consuming and wrought with errors . GenAI can be used to aid investigators by allowing them to ask questions about the data , compile transactional trends and summarize adverse media information . This aids the investigator in analyzing relevant information quickly and easily .
• Scenario generation for simulation : As financial crime becomes more complicated and unforeseen events come into the picture , creating greater opportunities for crime — think COVID-19 — GenAI models can create realistic scenarios for testing and training purposes . By generating potential money laundering scenarios , these language models can help train both machine learning models and human investigators , enhancing preparedness for different forms of financial crimes .
• Synthetic data generation : One of the major risks to compliance programs is a data breach . This is why most FIs maintain very strict data access and encryption controls on their AML data . AI can help mitigate security risks by generating synthetic data that mimics real financial transactions . This data can train machine learning models , particularly when real-world data is sensitive , scarce or challenging to obtain due to privacy constraints .
92 acamstoday . org