AI-Powered Analytics
Unlock actionable insights from your financial data with predictive analytics and machine learning. Forecast cash flow, predict payment behavior, detect anomalies, and make data-driven decisions that optimize your financial performance.
Forecast cash flow with 95% accuracy up to 90 days into the future.
Identify unusual transactions and potential fraud in real-time.
Make data-driven decisions with AI-powered recommendations.
Predict and mitigate financial risks before they impact your business.
Get instant answers to complex questions with natural language queries.
Identify trends and opportunities before your competitors.
Our SOPs are built on years of industry experience and best practices from leading finance teams.
Establish data validation rules at ingestion points. Implement data cleansing procedures for historical data. Define master data governance policies. Monitor data quality metrics: completeness, accuracy, consistency, timeliness. Maintain data lineage documentation for audit purposes. Conduct quarterly data quality assessments.
Document model purpose, assumptions, and limitations. Split data into training, validation, and test sets (typically 70/15/15). Establish model performance benchmarks before deployment. Implement A/B testing for model improvements. Retrain models monthly with new data. Maintain model version control and performance history.
Generate baseline forecasts using statistical and ML methods. Incorporate business intelligence from sales and operations teams. Adjust forecasts for known events: promotions, seasonality, market changes. Review forecast accuracy weekly and investigate significant variances. Document forecast adjustments with business rationale.
Define anomaly thresholds based on historical patterns. Categorize anomalies by type: payment, invoice, vendor, customer. Establish investigation procedures for each category. Escalate high-risk anomalies to appropriate stakeholders. Document investigation outcomes and update detection models. Track false positive rates and tune thresholds accordingly.
Deep domain expertise built into every feature, based on years of industry experience.
Supervised learning trains models on labeled historical data for prediction. Unsupervised learning discovers patterns without predefined labels. Time series forecasting predicts future values based on historical sequences. Classification models categorize items: high/medium/low risk. Regression models predict continuous values: payment amounts, timing.
Statistical methods include moving averages, exponential smoothing, and ARIMA. Machine learning approaches use Random Forests, Gradient Boosting, and Neural Networks. Ensemble methods combine multiple models for improved accuracy. Leading indicators: sales pipeline, order backlog, payment history. External factors: seasonality, economic indicators, industry trends.
Statistical methods flag values outside standard deviations from mean. Isolation Forests identify outliers by random partitioning. Autoencoders learn normal patterns and flag reconstructions with high error. Clustering groups similar transactions and flags distant points. Time-series anomaly detection considers temporal context and seasonality.
Forecast accuracy measured by MAPE (Mean Absolute Percentage Error). Classification models use precision, recall, F1-score, and AUC-ROC. Cash flow forecasts target MAPE below 10% for 30-day horizons. Anomaly detection balances precision (minimize false positives) and recall (catch all anomalies). Model drift monitoring detects when performance degrades over time.