An American & Swiss multinational financial services corporation needed to deploy AI that could continuously refine customer acquisition and up-sell strategies rather than relying on static models

SparkBeyond connected multiple datasets (including GDPR-compliant external sources) to analyze 100+ million potential drivers behind customer behavior patterns

Unlike static analytics, the system continuously evaluated new patterns and adapted targeting strategies as customer behaviors evolved

This adaptive approach improved target response rate translated to 7x greater returns from the campaign

SparkBeyond uncovered over 4 million CHF in potential bottom-line value for the business at an ROI of 15x in 5 months establishing a foundation for perpetually improving KPI performance

A major US Bank sought to move beyond static product recommendations for credit card customers to a system that could continuously learn and adapt based on changing behaviors

Implemented a dynamic decision engine combining web-browsing history with transactional data

Created personalization logic that continuously refined itself based on customer interactions

Generated and tested thousands of product recommendation rules that evolved with customer preferences

The decision personalization logic tree quickly mobilized several state-focused interventions, including:

• Identified and automatically prioritized high-converting customer journey touchpoints
• Dynamically adjusted targeting across web applications as user preferences evolved
• Systematically improved engagement with previously low-response customers through continuous offer optimization

A leading Indian private sector bank was struggling with attrition in deposits (savings and current account balances) at the rate of 15-20% p.a. for existing customers. They wanted to arrest attrition (loss in deposit balances) by continuously identifying high-risk customers

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• Deployed hypothesis generation that continuously evaluated millions of possibilities
• Leveraged intelligent features to identify and track evolving customer micro-segments
• Implemented automated data ingestion to ensure models reflected current conditions
• Created 30+ adaptive segmented models across 3 portfolios that improved with each iteration

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$80mn balance build (>25%) achieved within 60 days days through continuously refined retention campaigns, with ongoing discovery of over 5,000 intelligent insights and approximately 300 niche customer segments

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A South East Asian bank needed to replace static predictive models with a system that could continuously adapt to changing patterns in loan compliance and agent performance

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The platform continually analyzed 150 variables across 8 categories, constantly discovering new insights and updating predictions for loan compliance while simultaneously tracking shifting factors affecting agent performance

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Late Payments & Defaults

• Reduced cost of credit by dynamically optimizing collection visits by rank-ordering customers (according to their propensity to be late payers) in over a 12-month period

Agent Performance

• Proactively identified agents whose performance would improve or deteriorate, enabling pre-emptive action to be taken for each group

A leading global bank needed to replace rigid fraud models with a solution that automatically adapts to evolving financial crime patterns

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The platform continuously enriched internal data with contextual external sources, dynamically creating and refining risk clusters for SME clients

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Implemented an alert system that autonomously updates monthly with 95% accuracy based on emerging patterns such as:

• At least 7 transactions with amount higher than 30K
• Most frequent operation on the account is transfer to other accounts
• Most frequent title of transaction is “transfer to own account”

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