Point Predictive Case Study
How ClickIT Scale Fraud Detection From Python Sync to Go/SQS
Services
Software Development
Industry
AI & Fintech
As digital platforms scale, the primary barrier to closing enterprise deals isn't a lack of features; it’s a lack of verified trust”
The Client Project
Point Predictive provides AI-driven fraud detection and risk intelligence solutions for the lending industry, processing millions of loan applications and billions of data points for major financial institutions.
ClickIT helped modernize the platform’s architecture by migrating critical services from synchronous Python workflows to an asynchronous Go/SQS infrastructure, reducing processing bottlenecks, improving system resiliency, and enabling enterprise-scale fraud detection in real time.
The Strategic Decision at Stake
Before the optimization journey began, the leadership team faced a critical crossroads:
Could they continue managing infrastructure with synchronous, direct database calls and manual log retrieval, or would it ultimately slow down the deployment?
Without addressing infrastructure stability and integration bugs:
- Lending cycles stalled due to critical bugs in identity and income verification.
- System performance was limited by tightly coupled UI-to-database logic across 53+ endpoints.
- Data pipelines lacked observability, making it difficult to effectively track fraud rule performance.
The Challenge
The company needed to improve its app and infrastructure to handle growing demand without compromising the speed of its [Ai+Ni] assessments.
Key constraints included:
- Integration Reliability: Resolving persistent bugs in the BorrowerCheck workflow (pass code mappings and SSN zero-padding).
- Data Processing at Scale: Moving from synchronous direct database calls to a resilient, SQS-based architecture.
- Infrastructure Gaps: Parallelizing CloudWatch log retrieval and optimizing ECS/Step Functions for faster processing.
- Reporting Limitations: Building the necessary SQL-based dashboards to track case creation and rule metrics.
As volume grew, these issues led to:
- Operational friction in processing multi-integration customer IDs.
- Suboptimal throughput during high-traffic periods for the Intellicheck service
Our Approach: Introduced Go for the refactored service, replacing Python
To address the scalability and reliability gaps, we introduced Go for the refactored service, replacing Python’s synchronous DB calls with an asynchronous SQS-driven architecture.
This decoupled workload spikes from the database layer and allowed for:
All Technologies Used
We implemented a tech stack focused on high-concurrency and data orchestration:
Everything was done on time and with excellent standards in both communication and code delivery!”
– Director of PointPredictive
The Strategic Outcome
Risks Reduced
- 90% of fraud detection
- 80% automation rates
- Resolved SSN zero-padding and mapping bugs that caused verification failures.
- Async SQS processing in Go removed database bottlenecks in the Intellicheck service.
Problems That Stopped Existing
- Unstable Integrations: BorrowerCheck now correctly processes identity/income verification across all platforms.
- Tight Coupling: The migration of 53+ endpoints removed the direct UI-to-database logic dependencies.
- Trust Barriers: The stabilized infrastructure removed performance friction from the lender's decision-making process.
This case demonstrates that:
By modernizing the platform’s architecture, Point Predictive transformed fraud detection from a scalability bottleneck into a competitive advantage, building a high-performance infrastructure that supports real-time risk analysis at enterprise scale.
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