However, using traditional forecasting techniques over machine learning techniques have disadvantages including:

Triple Exponential Smoothing: Forecast models using this technique are easy to build, but there is a great risk of over-fitting for historical anomalies such as outages
ARIMA(Auto Regressive Integrated Moving Average): ARIMA cannot detect multiple seasonality in the data. This limits the use of this method of forecasting for long-term trends on high-frequency data.

The role of machine learning

Machine learning techniques avoid the pitfalls of traditional time-series forecasting. Two such highly effective techniques for call volume forecasting are deep learning time series (LSTM) and seasonal ARIMAX:

Deep Learning Time Series-LSTM (Long Short Term Memory): This approach is used for high frequency and long-horizons. It can be difficult to implement as it uses complex network structures like RNN (Recurrent Neural Network) and CNN (Convolutional Neural network), and choosing the optimal parameters is not simple.
Seasonal ARIMAX (SARIMAX): SARIMA supports the direct modeling of the seasonal component of the ARIMA series. When this method also takes into account additional exogenous dimensions it is called SARIMAX. The optimal hyper parameters for this model can be decided based on Auto ARIMA or grid search framework.

Additional measures to optimize call volume management

Along with optimizing contact center resources, insurers can increase FNOL efficiency by:

Enabling Digitization: Multi-channel intake and claims routing can influence cycle time and claim outcomes. An end-to-end omnichannel claims experience helps carriers to cut costs and settle claims quicker. Digital FNOL increases the volume of claims intake through digital channels and increases efficiency in managing the claim process. As a result, the contact center staff has relatively fewer FNOL call volumes to handle and can focus on capturing more complex claims.
Data-Driven Segmentation: Analytics-driven segmentation rules can quickly assess whether a claim is complex. Segmenting claims based on complexity can help insurers manage the FNOL process by reducing effort and increasing customer satisfaction. The faster a complex claim is detected, the quicker it can be assigned to an adjuster for appropriate care and handling, leading to better mitigation efforts and increased customer satisfaction.

The opportunity for insurers

Insurers can work with their internal analytics department and analytics vendors to augment their FNOL call volume management capabilities and manage their FNOL operations for a faster, more accurate claim resolutions by preparing for high call volume scenarios. Smooth FNOL call volume management not only improves customer satisfaction among policyholders but promotes loyalty and creates a scope for cross-selling other relevant policies relevant. From an enterprise angle, it introduces a sense of agility in an insurer's claim handling processes that eventually leads to a reduction in the number of claims.

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