# "Show Me What's Wrong!": Enhancing Fraud Detection Analysis by Combining Charts and Text

DevFeed: ["Show Me What's Wrong!": Enhancing Fraud Detection Analysis by Combining Charts and Text](<https://devfeed.tech/articles/show-me-what-s-wrong-enhancing-fraud-detection-analysis-by-combining-charts-and-text-26299.md>)

Original publisher: [Read original article](<https://medium.com/feedzaitech/show-me-whats-wrong-enhancing-fraud-detection-analysis-by-combining-charts-and-text-22ecfb342fb0?source=rss----e11168e7fe6b---4>)

Author: Beatriz Feliciano

Published: 2024-11-22T18:35:14Z

Content type: article

Language: en

Sources: [Feedzai](<https://devfeed.tech/sources/feedzai.md>)

Topics: [Transactions](<https://devfeed.tech/topics/transactions.md>), [data](<https://devfeed.tech/topics/data.md>), [synthetic-data](<https://devfeed.tech/topics/synthetic-data.md>), [Tool](<https://devfeed.tech/topics/tool.md>)

Tags: [data](<https://devfeed.tech/tags/data.md>), [data-analysis](<https://devfeed.tech/tags/data-analysis.md>), [data-visualization](<https://devfeed.tech/tags/data-visualization.md>), [financial-fraud](<https://devfeed.tech/tags/financial-fraud.md>), [fraud](<https://devfeed.tech/tags/fraud.md>), [fraud-detection](<https://devfeed.tech/tags/fraud-detection.md>), [fraud-investigation](<https://devfeed.tech/tags/fraud-investigation.md>), [image](<https://devfeed.tech/tags/image.md>), [interface](<https://devfeed.tech/tags/interface.md>), [research](<https://devfeed.tech/tags/research.md>), [synthetic-data](<https://devfeed.tech/tags/synthetic-data.md>), [transactions](<https://devfeed.tech/tags/transactions.md>)

## AI overview

The article describes a fraud-analysis tool that combines charts and text to help analysts review suspicious financial transactions. It explains that tabular review can make it difficult to identify patterns and anomalies within a 1-to-5-minute review window, and presents an interface using synthetic data to help analysts prioritize investigation areas.

## Source excerpt

Every year, millions of people fall victim to financial fraud. In 2023, the losses tied to this type of crime were estimated at US$159 billion just in the US, with some people losing all of their retirement savings to scammers. However, the impacts of this issue stretch beyond someone's finances. It can also impact a victim's life in many dimensions. Detecting and quickly acting upon suspicious transactions is essential to tackle this problem. Finding Fraud Through Data Tables To review the data of alerted transactions, analysts look at information in tabular format (similar to what is presented in Figure 1), scrolling through it to assess past activity patterns of the alerted person and comparing those with the alerted event. "How much money was spent on average on past transactions?" or "Is that significantly different from the amount on the current alert?" are some questions they might try to answer during their review. Figure 1: Image of a table that analysts typically use to review the data of alerted transactions. The issue with this approach is that finding groups of patterns and anomalies in tabular data can be overwhelming since it requires an increased cognitive load from analysts to interpret the data effectively. This becomes even more complex since these professionals must review and classify the alerted transaction in a short time -- between 1 and 5 minutes. Revamping the analysis To solve this problem, we present a tool that combines charts and text to guide the analysis of financial transactions. As presented in Figure 2, the tool (populated with synthetic data) is divided into three regions that provide different levels of information detail -- from the most high-level to the most detailed. The goal is that the analyst can scan the charts and prioritize their review towards specific areas of the alert. Figure 2: Proposed interface composed of multiple regions: the Knowledge Area Console (A) to detect suspicious areas of the analysis; the Knowledge Are