Audit Interface Layout for Real-Time Anomaly and Fraud Review
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Solution Overview
Problem
Traditional auditing processes are error-prone, time-consuming, and inefficient, especially in complex modern businesses, due to the complexity of financial data and the reliance on human auditors, leading to reduced accuracy and efficiency in financial audits.
Innovation Solution
An AI-based auditing system that utilizes modules such as RPA, OCR, predictive analytics, NLP, and machine learning to process and analyze financial data, generate anomaly and fraud alerts, and automatically create audit reports, integrating with blockchain for secure storage and smart contracts to verify compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional human auditing processes are used to review financial data, then auditors can exercise judgment and understanding, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system enables self-service auditing through automated data collection, processing, and analysis. The server automatically retrieves financial data from multiple databases, applies auditing rules and machine learning models, generates anomaly detections, and creates audit reports without requiring continuous human intervention, thus improving both accuracy and speed
Solution Approach 2:
The patent replaces manual human auditing with an automated computer-based system that uses machine learning models, predictive analytics, and rule-based engines to perform data analysis, anomaly detection, and report generation, eliminating human errors and significantly reducing auditing time
2Measurement precision
If more financial data is collected and analyzed to improve audit quality, then detection accuracy improves, but processing complexity and time increase
Solution Approach 1:
The auditing system is segmented into distinct functional modules: data collection module, data processing module, anomaly detection module, and report generation module. Each module handles specific tasks independently, managing complexity while enabling comprehensive data analysis through specialized processing pipelines
Solution Approach 2:
The server acts as an intermediary between multiple databases and the user terminal, centralizing data collection and processing operations. This intermediary architecture manages the complexity of interfacing with multiple data sources while providing streamlined access and consistent processing rules
3Reliability
If comprehensive financial data from multiple sources is integrated, then audit completeness improves, but data processing time and computational resources increase
Solution Approach 1:
The system merges data from multiple databases including client financial data, public financial information, and third-party data sources into a unified analysis framework. The server consolidates these diverse data streams and processes them through integrated machine learning models and auditing rules, achieving comprehensive audit coverage while maintaining efficient processing through unified data pipelines
Data Source
AI summary
Proposed is a method and system for displaying data sets on a user interface. The system may retrieve first, second and third datasets from a plurality of databases. The system may then analyze the datasets to determine anomalies and fraud indicators in the datasets. The system may then generate and display an anomaly alert and a fraud alert respectively on first and second areas of a user interface. The system may display a first set of review data on a third area of the user interface. The system may receive a second set of review data and generate a final audit report on a fourth area of the user interface. The system may display an indication of the final audit report on a fourth area of the user interface such that information contained in the first to fourth areas is simultaneously visible via a same display screen.