SumUp

Improved compliance efficiency and narrative accuracy in AML reporting.

IT Security & Data

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Finance & Insurance

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Natural Language Processing

Project Overview

LLM-driven evaluation is used to assess the accuracy and quality of free-text narratives in anti-money laundering (AML) reports.

Layman's Explanation

SumUp uses an AI-powered tool to evaluate text written for financial crime reports, helping ensure that these reports meet regulatory standards without human agents needing to review each one manually.

Analogy

In finance, evaluating an LLM’s text is like an auditor checking reports for completeness, ensuring each section is filled out accurately and all required information is included, without manually going through every word.

Details

SumUp developed an LLM-driven evaluation system to assess the quality of narratives generated in suspicious activity reports (SARs), specifically for AML purposes. Traditional NLP metrics often fail to capture nuances essential to these narratives, such as factual accuracy, topic relevance, and structural completeness. Instead, SumUp’s LLM-driven approach evaluates each text against custom benchmarks, such as whether it includes customer data, supporting evidence, and valid conclusions. It then assigns a score based on these criteria, allowing automated, qualitative assessment comparable to human review. This evaluation method also identifies potential improvements in generated text by flagging issues like missing data or fabricated facts, enabling data scientists to iterate and refine the text generation model effectively.

Project Novelty

This use of LLMs to evaluate narrative consistency and accuracy in financial crime reporting is innovative, addressing the unique challenges in AML reporting that traditional NLP metrics don’t adequately cover.

Project Estimates

Timeline:

7 months

Cost:

$150,000

Headcount:

4

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Block

Establish a centralized data governance framework using Unity Catalog to secure customer data and streamline compliance across Block’s subsidiaries.

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IT Security & Data

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Finance & Insurance

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Natural Language Processing

Product

Product
Banco Hipotecario

Banco Hipotecario

Improved customer engagement and acquisition, increasing profitability.

Build-Custom

Enhance customer acquisition and retention through machine learning-driven product recommendations and risk assessment.

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Banco Hipotecario

Enhance customer acquisition and retention through machine learning-driven product recommendations and risk assessment.

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Product

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Finance & Insurance

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Recommendation Systems

Engineering

Engineering
Banco Bradesco

Banco Bradesco

Increased operational efficiency and data, reducing time to insights.

Build-Custom

Streamline data analysis workflows by enabling both technical and nontechnical users to leverage AI-driven code assistance for faster, more accurate insights.

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.

Banco Bradesco

Streamline data analysis workflows by enabling both technical and nontechnical users to leverage AI-driven code assistance for faster, more accurate insights.

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Engineering

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Finance & Insurance

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Generative AI

IT Security & Data

IT Security & Data
Zest AI

Zest AI

Increased access to fair lending options and optimized risk management for lenders.

Build-Custom

Zest AI enables lenders to make fair, transparent, and compliant lending decisions through AI-driven credit risk assessment.

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Zest AI

Zest AI enables lenders to make fair, transparent, and compliant lending decisions through AI-driven credit risk assessment.

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IT Security & Data

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Finance & Insurance

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Classification