How AI-Powered Anomalies Detection Works
At Datasolix, AI-powered anomaly detection is a key part of streamlining the close consolidation process. It ensures that financial data is accurate, compliant, and free from errors, improving the efficiency of financial closures. Here's a look at how Datasolix utilizes AI for anomaly detection in close consolidation
01
Machine Learning Techniques
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Supervised Learning: In cases where historical errors or anomalies have been labeled, Datasolix employs supervised learning techniques to train AI models on these known issues. This helps the system identify similar anomalies in the future.
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Unsupervised Learning: When labeled data is not available, AI uses unsupervised learning to spot outliers and unusual patterns in the data by identifying behavior that deviates from the norm.
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Advanced Algorithms: More complex deep learning models may also be used to detect nuanced patterns in large and complex datasets, enabling the system to uncover subtle anomalies that might be missed by traditional methods.
02
Establishing Normal Financial Patterns
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AI Model Training: Datasolix trains its AI models on historical financial data to understand typical financial behaviors such as cash flow patterns, revenue cycles, and expense trends. The system learns what "normal" financial activity looks like in order to detect any deviations from the expected patterns.
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Contextual Analysis: AI is also trained to account for external factors like seasonality, market conditions, or specific business operations, which can impact financial data, allowing for more accurate anomaly detection.
03
Real-Time Anomaly Detection
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Ongoing Monitoring: Once deployed, the AI system continuously monitors financial data in real time during the close consolidation process. It evaluates new data as it's entered, comparing it against the normal patterns it has learned to flag irregularities.
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Detecting Irregularities: AI detects a wide range of anomalies, such as unexpected spikes in expenses, unusual revenue fluctuations, mismatches between subsidiaries, or errors in intercompany transactions, among others. These anomalies can signal potential issues such as data entry errors, fraud, or compliance risks.
04
Risk Scoring and Prioritization
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Risk Assessment: Once an anomaly is detected, AI assigns a risk score to each issue based on its severity. For instance, a significant fluctuation in cash flow might be flagged with a higher risk score compared to minor inconsistencies in a single ledger.
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Prioritizing Alerts: The system then prioritizes alerts, ensuring that the most critical issues are addressed first, helping finance teams focus on high-risk anomalies that may have a bigger impact on the financial consolidation.
