AI Challenge - AI Centered Fraud Detection using Document Understanding and Task Mining

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AI Centered Fraud Detection using Document Understanding and Task Mining

Use Case Description

A financial institution can use AI to automate the detection of fraudulent transactions. This can be achieved by leveraging document understanding to extract transactional data and task mining to identify patterns of suspicious activity. The AI system can then flag transactions for further review by a human analyst. By focusing on AI-centered fraud detection, financial institutions can save significant amounts of time and money by automating the identification of suspicious activity, reducing the number of false positives and false negatives, and improving overall detection accuracy.

AS-IS WORKFLOW, TO-BE WORKFLOW

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Other information about the use case

Industry categories for this use case: BFSI, Manufacturing, Marketing Sales, Banking, Insurance, Public Sector

Skill level required: Intermediate

UiPath Products that were used: UiPath Studio, UiPath AI Center, UiPath Document Understanding, UiPath Orchestrator, UiPath Task Mining

Other applications that were used: -

Other resources: -

What is the top ROI driver for this use case?: Minimize risk and ensure compliance in operations

1 Like
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Hi @Talari_VamsiKrishna thank you for your submission. Do you have any other details to share with us for understanding more about this use case? Thank you.

3

Hi @loredana_ifrim,

Thank you for taking the time to review my use Case; I am truly grateful.

Here is a detailed overview of my use case -

AS IS Process:
The current process for fraud detection in financial institutions typically involves manual review of transactions by human analysts. This process can be time-consuming and prone to errors, leading to the potential for fraudulent activity to go undetected. Therefore, to improve this process, UiPath can be used to develop a prototype for an AI-centered fraud detection system.

TO BE Process:
The proposed process for AI-centered fraud detection using UiPath can be broken down into the following steps:

Step 1: Document Understanding
The first step is to use Document Understanding to extract transactional data from various documents, such as invoices, receipts, and bank statements. This can be achieved through a combination of OCR (Optical Character Recognition) and NLP (Natural Language Processing) techniques. UiPath provides pre-built activities for document understanding that can extract data from structured and unstructured documents with high accuracy.

Step 2: Task Mining
Next, Task Mining can be used to identify patterns of suspicious activity in the extracted transactional data. Task Mining is a technique used to identify patterns of activity from users’ interactions with an application. By analyzing the transactional data, the AI system can identify patterns of behavior that are indicative of fraudulent activity. UiPath Task Mining captures the user interactions in the background to understand the business processes and detect anomalies.

Step 3: Flagging Suspicious Transactions
After identifying patterns of suspicious activity, the AI system can flag transactions for further review by a human analyst. This can be achieved by sending notifications to the analyst or by integrating the AI system with an existing fraud detection system. UiPath provides pre-built connectors to various external systems, making it easy to integrate the AI system with the financial institution’s existing systems.

Step 4: Review and Analysis
The flagged transactions are then reviewed by human analysts, who can further investigate and determine whether the flagged transactions are indeed fraudulent. Any false positives or false negatives can be corrected, and the AI system can be further trained to improve the overall detection accuracy.

Step 5: Continuous Improvement
The AI system can be continuously improved by incorporating feedback from the human analysts and using machine learning techniques to learn from past transactions. This will improve the accuracy of the AI system and reduce the number of false positives and false negatives over time.

Conclusion:
In conclusion, UiPath can be used to develop a prototype for an AI-centered fraud detection system, which can help financial institutions save significant amounts of time and money by automating the identification of suspicious activity, reducing the number of false positives and false negatives, and improving overall detection accuracy. The proposed process for AI-centered fraud detection using UiPath involves using Document Understanding to extract transactional data, Task Mining to identify patterns of suspicious activity, flagging suspicious transactions for review by human analysts, reviewing and analyzing the flagged transactions, and continuously improving the AI system.

It’s my USECASE just got this idea by watching various Financial issues. Hope it will got featured and helps in automating BUSSINESS TASKS.

Looking forward to see your comments.

4

Hi loredana_ifrim,

Thank you for taking the time to review my use Case; I am truly grateful.

Here is a detailed overview of my use case -

AS IS Process:
The current process for fraud detection in financial institutions typically involves manual review of transactions by human analysts. This process can be time-consuming and prone to errors, leading to the potential for fraudulent activity to go undetected. Therefore, to improve this process, UiPath can be used to develop a prototype for an AI-centered fraud detection system.

TO BE Process:
The proposed process for AI-centered fraud detection using UiPath can be broken down into the following steps:

Step 1: Document Understanding
The first step is to use Document Understanding to extract transactional data from various documents, such as invoices, receipts, and bank statements. This can be achieved through a combination of OCR (Optical Character Recognition) and NLP (Natural Language Processing) techniques. UiPath provides pre-built activities for document understanding that can extract data from structured and unstructured documents with high accuracy.

Step 2: Task Mining
Next, Task Mining can be used to identify patterns of suspicious activity in the extracted transactional data. Task Mining is a technique used to identify patterns of activity from users’ interactions with an application. By analyzing the transactional data, the AI system can identify patterns of behavior that are indicative of fraudulent activity. UiPath Task Mining captures the user interactions in the background to understand the business processes and detect anomalies.

Step 3: Flagging Suspicious Transactions
After identifying patterns of suspicious activity, the AI system can flag transactions for further review by a human analyst. This can be achieved by sending notifications to the analyst or by integrating the AI system with an existing fraud detection system. UiPath provides pre-built connectors to various external systems, making it easy to integrate the AI system with the financial institution’s existing systems.

Step 4: Review and Analysis
The flagged transactions are then reviewed by human analysts, who can further investigate and determine whether the flagged transactions are indeed fraudulent. Any false positives or false negatives can be corrected, and the AI system can be further trained to improve the overall detection accuracy.

Step 5: Continuous Improvement
The AI system can be continuously improved by incorporating feedback from the human analysts and using machine learning techniques to learn from past transactions. This will improve the accuracy of the AI system and reduce the number of false positives and false negatives over time.

Conclusion:
In conclusion, UiPath can be used to develop a prototype for an AI-centered fraud detection system, which can help financial institutions save significant amounts of time and money by automating the identification of suspicious activity, reducing the number of false positives and false negatives, and improving overall detection accuracy. The proposed process for AI-centered fraud detection using UiPath involves using Document Understanding to extract transactional data, Task Mining to identify patterns of suspicious activity, flagging suspicious transactions for review by human analysts, reviewing and analyzing the flagged transactions, and continuously improving the AI system.

It’s my USECASE just got this idea by watching various Financial issues. Hope it will get featured and help in automating BUSINESS TASKS.

Looking forward to hearing from you.

Thanks and Regards
VamsiKrishna Talari

1 Like
5

Thank you @Talari_VamsiKrishna!

Don’t forget to share it on your social media followers to cast their vote for your use case :wink: !

Stay connected :raised_hands: .

6

Sure and Thanks :slightly_smiling_face:

1 Like
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Hi @Talari_VamsiKrishna thank you for submitting your use case. :wink: Don’t forget to share it on your social media followers to cast their vote for your use case! Votes will be counted till February 23rd, 2023

Stay connected :raised_hands: .

8

Hello ,

Can i get reference of it i mean can you share it in Git Hub or can you upload it in youtube. It will be helpful