Fintech Company’s Deal Outcomes with 85% Accuracy

About the Company

Revenue: $5 billion in annual revenue as of 2023.

Global Presence: Operations in over 50 countries worldwide.

Employee Base: 20,000 employees globally.

Industry: Leading provider of financial technology solutions, focusing on payment systems, financial software, and digital banking services.

Challenge

At the cutting edge of the rapidly evolving fintech industry, the company faced significant challenges in scaling their operations due to increasingly complex market conditions and customer demands. Accurate forecasting of deal outcomes was crucial to adapt their strategic approach and maintain competitiveness.

Our Approach

Over a 10-week period, onegen.ai deployed a CRM AI optimization tool to build predictive modeling capabilities that could forecast the likelihood of deal wins and losses, integrating seamlessly with the client’s existing CRM systems. The solution involved:

Enriching CRM data with 120 external data sources, including economic indicators, market trends, and customer interaction data.

Developing an advanced, federated data model to generate actionable insights.

Utilizing machine learning to train models capable of predicting quarterly and annual deal outcomes, thus enhancing sales strategies and operational efficiencies.

Results

$35M increase in projected annual revenue due to higher conversion rates.

85% accuracy in predicting deal outcomes at the start of each quarter, allowing for strategic adjustments.

96% accuracy in daily sales forecasts, facilitating day-to-day operational planning.

Solution Architecture

Enhanced CRM with AI: Integration of AI to provide deep learning insights directly within the CRM environment.

Comprehensive Data Integration: Leveraged both internal and external data sources to inform the AI models, ensuring a holistic view of market conditions.

Robust Security Measures: Implemented stringent security protocols to protect data integrity and comply with international data protection regulations.

Project Highlights

Rapid Deployment: AI capabilities were integrated and operational within 10 weeks from project initiation.
Data Integration: Over 130 external data sources were integrated, enhancing the quality and breadth of data analysis.
Error Reduction: Forecasting accuracy improved from a baseline error of 40% to just 8% by the end of the AI training period.
Economic Impact: Projected to increase annual revenue potential by up to $40M due to better-targeted sales strategies and resource allocation.
Machine Learning Model Features: Developed over 2,500 unique features in the machine learning models to capture complex patterns and influences on sales outcomes.
Analysis and Scoring: Evaluated more than 100,000 opportunities using the new AI system, providing detailed insights into sales dynamics and customer behavior.