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AI & Data Analytics for Improved Customer Success & Operational Efficiency

In less than six weeks, Devron built a predictive AI model with over a 90 percent accuracy rate, vastly improving SPS's customer service and operational efficiency.

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SPS is the leading outsourcing provider of innovative services in business processing and data management. Building on its Swiss foundations and global footprint, SPS is the trusted partner for process optimization and intelligent automation.

Case Study at a Glance

Data Landscape

Use Case

Operational Efficiency


Outsourcing: Business Processing & Document Management Services


  • Unpredictable workload volumes
  • Staff utilization challenges
  • Inconsistent efficiency levels


  • Fully trained AI model in less than 6 weeks
  • Over 90 percent accuracy in forecasting workload volumes
  • Improved customer service and operational efficiency

Data Challenges

Pain Point Summary

Inability to predict the daily workload volumes, resulting in staffing utilization challenges and inconsistent efficiency levels.

Data Description


SPS experienced a varying and unpredictable daily volume of its client's loan documentation for processing. This presented a significant challenge to the business regarding staffing utilization and SLA compliance and resulted in inconsistent levels of efficiency.

Disparate data sources, inconsistent/incomplete data, and limited in-house data science resources provided significant headwinds to making more data-centric and AI-driven decisions as an organization.


SPS was looking to develop predictive analytics to improve staffing alignment and utilization. Specifically, they wanted to predict daily volumes for the upcoming five rolling business days. SPS's goal was to use these predictions to determine staff requirements by zone and better steer the client on downstream processing needs.

Data Challenges

Data Challenges

3 Disparate Data Sources
Heterogenous Schema
Sensitive Data (PII)
No items found.

Data Description

Data Description

Tabular Format
Megabytes of Data
Internal Data
External Market Data
No items found.


Working with Devron's data science experts and using its federated machine learning and data science platform, SPS developed a predictive AI model for determining critical daily work volumes. The team leveraged Devron to analyze disparate, private datasets without moving data, including loan package volumes, processing stage details, and broader external market data, such as interest rates, to better predict loan documentation volumes.

SPS also analyzed workload sources and employee efficiency to provide insight to its customer to streamline the operation jointly.


In less than six weeks, SPS had a predictive AI model that forecasted daily volumes with over a 90 percent accuracy rate, vastly improving customer service and operational efficiency.

Fully Trained AI Model
Model Accuracy
No items found.

Data Challenges

SPS seeks out innovative ways to improve operational performance for its clients. The partnership and solution provided by Devron was an accelerant for our AI and data science efforts as it expedited data access while maintaining our strict data privacy standards.

With the ability to unlock new value from our data, SPS will continue to expand its AI capabilities for client service and internal efficiency.

Dan Moscatiello

CEO of SPS North Americas

Data Description


Customer Service & Satisfaction

Proactive insight into workload volumes allowed SPS to manage service levels better and set customer expectations for downstream processing requirements.

Business Operations Improvement

SPS gained greater visibility into staffing requirements across the different work functions and zones and better-aligned resources.

Customer Guidance & Advice

SPS obtained new insight into their customer's customers, highlighting accounts consistently requiring greater resources. As a result, SPS was able to help its customers identify pricing and service level adjustment opportunities.

Employee Effectiveness & Efficiency

SPS discovered deeper insights into employee efficiency, better-analyzed staff performance, and identified areas for additional training.

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