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Anomaly Detection Tool

Import financial data showing daily interest accrued ontwo products for the first 10 days in January 2023: Import the org-approved product interest rates. Note:these are yearly interest rates. Infer the daily interest rates from the yearly rates, andassign them as variables. Note: these variables will beplotted as lines and used as benchmarks to detectanomalies. Create the scatterplot to detect anomalies, which willdeviate from the linear relationship between (1) dailyinterest accrued and (2) daily account balance. Infer the daily interest rates from the dummy data. Purpose: this workflow detects outliers when performing re-calculation procedures during the audit, i.e., when re-calculating interest revenue/expenses, or performing similaranalysis where rates are applied to account balances. (In theory, the workflow can be used to detect anomalies where a linear relationship exists between two variables).Interpreting the scatterplot visualization:1. each circle corresponds to one interest expense re-calculation wherein a rate is applied to an account balance2. the straight lines correspond to the daily interest rates, i.e., the slope of each line = yearly interest rate / 3653. accurate expense re-calculations will fall upon the straight lines4. outliers will fall outside the lines Add dummybanking dataAdd approved interest ratestableScatterplotInfer dailyinterest ratesDaily ratesto variablesInfer dailyinterest rates Table Creator Table Creator Python View Math Formula Table Columnto Variable Math Formula Import financial data showing daily interest accrued ontwo products for the first 10 days in January 2023: Import the org-approved product interest rates. Note:these are yearly interest rates. Infer the daily interest rates from the yearly rates, andassign them as variables. Note: these variables will beplotted as lines and used as benchmarks to detectanomalies. Create the scatterplot to detect anomalies, which willdeviate from the linear relationship between (1) dailyinterest accrued and (2) daily account balance. Infer the daily interest rates from the dummy data. Purpose: this workflow detects outliers when performing re-calculation procedures during the audit, i.e., when re-calculating interest revenue/expenses, or performing similaranalysis where rates are applied to account balances. (In theory, the workflow can be used to detect anomalies where a linear relationship exists between two variables).Interpreting the scatterplot visualization:1. each circle corresponds to one interest expense re-calculation wherein a rate is applied to an account balance2. the straight lines correspond to the daily interest rates, i.e., the slope of each line = yearly interest rate / 3653. accurate expense re-calculations will fall upon the straight lines4. outliers will fall outside the lines Add dummybanking dataAdd approved interest ratestableScatterplotInfer dailyinterest ratesDaily ratesto variablesInfer dailyinterest ratesTable Creator Table Creator Python View Math Formula Table Columnto Variable Math Formula

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Created by: a725899
Created at: 2023-05-29
On NodePit since: 2023-05-30
Last update: 2024-04-27
Created with KNIME version: v4.7.0

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