Hours
Regulatory data request response
Unified
Platform replacing fragmented silos
Feature store
Training-serving consistency
5 days
Sponsor bank data deadline met
Client Snapshot
Industry
FinTech
Solution
Data Solutions | Cloud Solutions
Complexity
High
Delivery
Architecture + Implementation
The Problem
Most growth-stage FinTechs accumulate data infrastructure debt faster than product debt. The first analytics solution is a Redshift warehouse built by the first data engineer. The second is a Kafka stream someone deployed for fraud. The third is a Snowflake account the ML team set up independently. By Series B, the platform has three data systems that disagree on the same metric, no governed data model, and a regulatory reporting function that pulls numbers from four different sources and hopes they reconcile before the sponsor bank audit.
Sponsor bank program agreements typically require regulatory data production within five business days of request. Most growth-stage FinTechs need two weeks. The gap creates program agreement risk at every audit cycle and the fragmented data infrastructure that causes the delay also undermines AI model reliability through training-serving skew: features computed differently between model training environments and production scoring engines.
Ready to Start?
Schedule a Data Infrastructure Assessment
Get a candid analysis of your current data fragmentation, regulatory response gaps, and platform modernization roadmap.
5 days
typical sponsor bank program agreement deadline for regulatory data production. Most growth-stage FinTechs need two weeks with fragmented data infrastructure. The gap is a program agreement risk at every audit cycle and the same infrastructure problem that causes the regulatory response delay also causes AI model training-serving skew and inconsistent product metrics.
How PiTech Delivers
01
Data Platform Architecture Design
Unified data platform architecture designed for the FinTech’s scale trajectory, product mix, and regulatory requirements simultaneously. Cloud-native stack selection (Databricks, Snowflake, or equivalent) with streaming and batch processing patterns. Architecture designed once not re-architected every Series round.
02
Regulatory Data Model Implementation
Regulatory reporting data structures built alongside the analytics model BSA/AML transaction data, HMDA fields if applicable, and sponsor bank program reporting requirements inform the data model design from the start. Regulatory data requests answered from structured, governed data not from ad hoc query reconstruction.
03
AI/ML Feature Store Infrastructure
Feature computation and serving infrastructure ensuring consistent feature engineering from model training through production scoring. Features computed once, versioned, and served from the same pipeline eliminating training-serving skew that degrades production model performance relative to validation results.
04
Product Analytics and Governance Layer
Real-time funnel, cohort, and engagement analytics for product teams built on the same data foundation as regulatory and AI use cases. Data catalog and quality monitoring at appropriate FinTech-scale governance enough structure to prevent data chaos without creating bureaucratic overhead that blocks product velocity.
Proven Outcomes
Hours
Regulatory data request response time in unified platform engagements
Feature store
AI training-serving consistency delivered as platform infrastructure
18+ yrs
Financial services data engineering regulatory and compliance depth
Proven Outcomes
18+
Years in Regulated Industries
What You Gain
Hours
Regulatory data request response vs. two-week manual assembly
Unified
Single platform replacing fragmented warehouse and streaming silos
Feature store
Training-serving consistency eliminating AI model performance degradation
5-day ready
Sponsor bank data deadline compliance from governed data infrastructure
What's Included
Data platform architecture
Data platform architecture
Cloud-native unified platform with streaming and batch processing for all use cases
Regulatory data model
Regulatory data model
BSA/AML, sponsor bank reporting, and applicable regulatory data structures built from design
Product analytics layer
Product analytics layer
Real-time funnel, cohort, and engagement analytics for product teams
AI/ML feature store
AI/ML feature store
Feature computation, versioning, and serving for fraud, credit, and personalization models
Data catalog and governance
Data catalog and governance
Metadata management, data ownership, and quality monitoring appropriate to FinTech scale
Investor reporting data layer
Fair lending monitoring module
Investor reporting data layer
KPI computation and reporting infrastructure for board and investor updates
Data observability
Data observability
Pipeline monitoring, data quality alerting, and freshness tracking across all data sources
Frequently Asked Questions
When should a FinTech invest in a unified data platform?
The inflection point is when data requests from product, compliance, and finance return different numbers for the same metric because each team is querying a different system with different transformation logic. This inconsistency is the direct operational cost of fragmented infrastructure. Most FinTechs should address it at Series A; by Series B, data infrastructure debt is visible to sophisticated investors during diligence.
What cloud data stack does PiTech recommend for growth-stage FinTechs?
PiTech recommends Snowflake or Databricks for the warehouse layer depending on the FinTech’s primary use case emphasis (analytics vs. ML), with streaming powered by Confluent Kafka or cloud-native alternatives. Stack selection is driven by the founding team’s existing expertise, primary use cases, and cost profile not by vendor relationships.
How does a feature store improve AI model performance in production?
Feature stores eliminate training-serving skew the problem where features computed during model training differ from features computed in production scoring, causing models to perform differently live than they did in validation. When fraud detection, credit underwriting, or personalization models produce different results in production than in backtesting, training-serving skew is the most common cause. Consistent feature computation from a shared store is the most impactful single investment in AI reliability.
Can PiTech work with a FinTech's existing data engineering team?
Yes. PiTech typically works in a collaborative model PiTech provides architecture design, platform selection, and build acceleration; the client’s data engineering team owns the ongoing operation and development. Knowledge transfer is a defined program deliverable, not an afterthought. Most FinTechs use PiTech to accelerate the initial platform build, then take ownership of ongoing development.
How does the data platform handle PCI DSS requirements for payment data?
PiTech designs separate data zones for cardholder data within the unified platform tokenization of card data before it enters the analytics layer, access controls restricting cardholder data access to PCI-scoped systems, and audit logging satisfying PCI DSS Requirement 10. The regulatory data model includes PCI scope management from design so cardholder data handling is governed from the start.
Data infrastructure built for scale produces returns on every product, compliance, and AI investment that follows. PiTech builds it right.
Contact PiTech to begin with a data fragmentation and regulatory response capability assessment.
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