Forecasting Monthly Balances in Wealth Management

Arrayo was engaged by the Wealth Management division of an international financial institution to demonstrate the capabilities of using AI for deposit activities.

Challenge

In wealth management, accurate forecasting of financial metrics is crucial. Our client, a leading financial institution, faced challenges in forecasting the monthly balance of sweep deposits—automatic transfers of funds between accounts. The main challenges were:

• Data Complexity: Sweep deposit data is influenced by numerous factors, including market conditions, client behavior, regulatory changes, and internal policies.
• Time Series Dynamics: The data showed strong temporal dependencies and seasonality, which traditional methods struggled to capture.
• Scalability and Integration: The solution needed to integrate into the existing infrastructure and scale with increasing data volumes.

Delivery

We developed a hybrid forecasting model combining time series analysis and machine learning. Our approach included:

• Feature Analysis:
  • Initial Study: Analyzed features to identify those influencing the balance most, using statistical methods and machine learning techniques. Key influencers included interest rates, economic indicators, and transaction patterns.
• Model Development:
  • Time Series Analysis: Utilized ARIMA and exponential smoothing to model temporal patterns.
  • Machine learning Integration: Incorporated machine learning models to capture non-linear relationships and interactions between features.
• Model Evaluation and Validation:
  • Cross-Validation: Used cross-validation techniques to ensure robustness and generalizability.
  • Hyperparameter Tuning: Conducted extensive hyperparameter tuning to optimize model performance.
• Trend Explanation:
  • Forecast Generation: Generated forecasts with detailed explanations of driving factors.
  • Visualization: Developed dashboards for easy interpretation and visualization of forecast results.

Value

Implementing the hybrid forecasting model delivered significant value:

• Improved Forecast Accuracy: Enhanced accuracy by capturing both linear and non-linear patterns, leading to more reliable predictions.
• Enhanced Decision-Making: Provided deeper insights into trends and potential fluctuations, enabling proactive adjustments to investment strategies.
• Operational Efficiency: Automated forecasting reduced manual intervention, allowing the team to focus on higher-value tasks and streamline operations.
• Risk Management: Accurate forecasting helped manage financial risks by anticipating periods of low liquidity or potential cash shortfalls.

In conclusion, deploying a hybrid forecasting model revolutionized the client’s approach to managing sweep deposits, improving forecast accuracy, decision-making, operational efficiency, and risk management.