Fractional Analytics

HOW IT WORKS

I. Discovery & Assessment We understand your data maturity, team structure, and analytics gaps.

II. Fractional Plan We agree on hours, cadence, and priorities — your dedicated analytics lead, without the full-time hire.

III. Embedded & Working We join your team, attend your standups, and deliver analytics that move your business forward.

IV. Ongoing or Transition Stay as your long-term fractional function, or hand off cleanly to an in-house hire when asked

WHAT IT SOLVES

• Missing fintech domain expertise - Generic analysts don't understand payment economics, compliance analytics requirements, or fintech-specific challenges like interchange optimization, STP rates, or cross-border profitability - leading to analysis that misses critical nuances

• Specialized projects beyond current team - Your existing analysts handle day-to-day reporting well, but complex initiatives (building cost-to-serve models, optimizing payment routing, compliance automation) require specialized skills they don't have

• Strategic guidance vacuum - No senior person to pressure-test assumptions, design analytics frameworks properly, advise on build vs buy decisions, or translate data insights into strategic recommendations for leadership

• Investor-grade analytics capability - Board and investors expect sophisticated analysis (cohort retention curves, LTV models with retention assumptions, CAC payback by channel) that requires expertise beyond basic reporting

• Flexible expertise without long-term commitment - Access senior fintech analytics expertise when you need it - for specific projects, strategic initiatives, or ongoing fractional support - scaling up or down as needs change without HR complexity

Data & AI-adoption Strategy

• Data Infrastructure Design - Assess current systems, design scalable architecture, and plan integration strategy to unify fragmented data across PSPs, CRMs, and operational tools

• Data Governance & Quality Framework - Establish data ownership, quality standards, and documentation practices to ensure analytics and reporting are built on reliable foundations

• Analytics Capability Roadmap - Define what analytics capabilities to build internally vs buy, prioritize by business impact, and create phased implementation plan

• AI/ML Use Case Prioritization - Cut through hype to identify which ML applications deliver ROI for your business (fraud scoring, churn prediction, forecasting) vs expensive distractions

• Build vs Buy Assessment - Evaluate when to build custom models, leverage vendor solutions/embedded capabilities - with clear cost-benefit analysis for each decision

• Vendor Evaluation & Selection - Navigate analytics and AI vendor landscape with objective criteria, avoiding expensive pilots that go nowhere and redundant tool sprawl

• Regulatory & Ethical AI Compliance - Ensure AI adoption meets financial services regulations, maintains model explainability for audits, and avoids bias in decisioning

Data Infrastructure & Governance

• Data Architecture Design & Roadmap - Design scalable data infrastructure connecting PSPs, KYC vendors, CRMs, and operational systems; build integration strategy that grows with transaction volume without constant rebuilds

• Data Quality Standards & Monitoring - Establish clear data definitions, implement quality checks and validation rules, and create monitoring dashboards to catch data issues before they impact business decisions

• Governance Framework Implementation - Define data ownership, retention policies, PII handling procedures, and audit trail requirements aligned to financial services regulatory expectations

• Integration Strategy & Vendor Management - Evaluate and select ETL/integration tools, design maintainable data pipelines, and establish vendor integration standards to prevent fragile custom scripts and tool sprawl

• Data Warehouse & Platform Selection - Guide build vs buy decisions for data infrastructure (Snowflake, BigQuery, Redshift), BI tools (Looker, Metabase, Tableau), and analytics platforms based on your volume, budget, and team capabilities

• Documentation & Knowledge Transfer - Create data dictionaries, schema documentation, pipeline maps, and runbooks so your team can maintain and extend systems without dependency on one person

Ad hoc Analysis & Insights

• Strategic Question Answering - Provide rapid, rigorous analysis for critical business questions with data-driven recommendations in executive-grade format.

• Deep-Dive Investigations - Conduct thorough root cause analysis when metrics move unexpectedly (churn spike, declining conversion, geographic performance gaps) to understand drivers and recommend corrective actions

• Decision Support for Leadership - Partner with founders and executives on high-stakes decisions (market entry, pricing changes, product prioritization) by modeling scenarios, quantifying trade-offs, and stress-testing assumptions

• Cross-Functional Data Requests - Handle one-off analytical needs from sales, marketing, operations, and product teams - freeing your existing analysts from constant interruptions to focus on core responsibilities

• Exploratory Analysis & Opportunity Identification - Proactively dig into data to surface hidden patterns, untapped opportunities, or early warning signals that wouldn't emerge from standard dashboards

• Executive & Board Presentation Support - Translate complex analysis into clear executive narratives, build compelling data visualizations, and prepare analytical materials for board meetings and investor updates

• Rapid Turnaround Capability - Deliver high-quality analysis on compressed timelines when urgent questions arise, leveraging fintech domain expertise to move faster than generalist analysts

• Predictive Model Development - Build and deploy ML models for fintech-specific use cases: LTV prediction, churn risk scoring, customer segmentation, payment success probability, fraud propensity, and demand forecasting

• Model Design & Methodology Selection - Determine optimal modeling approach

  based on your data volume, use case requirements, and interpretability needs

• Data Preparation - Transform raw transaction, customer, and operational data into model-ready features to tackle fintech-specific challenges.

• Model Validation & Performance Tracking - Establish rigorous validation frameworks, backtesting protocols, and ongoing performance monitoring

• Production Implementation Support - Design model deployment architecture, create scoring pipelines, and build monitoring dashboards - ensuring models actually get used in operations, not abandoned after development

• Interpretability & Documentation - Build explainable models that satisfy regulatory requirements, create clear documentation for auditors, and translate model outputs into actionable business insights

• Strategic ML Roadmap - Advise on which models to build when, prioritizing by business impact and data readiness - preventing wasted effort on low ROI projects

Advanced Models (ML/Statistical)

• Analytics Training for Teams - Conduct workshops teaching non-technical teams how to read dashboards, interpret key metrics, and ask better analytical questions

• Self-Service Enablement - Design intuitive dashboards and reports that empower teams to answer routine questions independently without analytics team support

• Metric Definition & Documentation - Create clear, accessible documentation of how key metrics are calculated, what they mean, and when to use them - eliminating confusion and misalignment

• Best Practices & Frameworks - Establish analytical standards for the organization - how to structure analyses, present findings, and make data-driven recommendations

• Stakeholder Communication Skills - Train your existing analysts on translating technical findings into business language and crafting compelling narratives for different audiences

• Analytics Champions Program - Identify and develop analytics advocates within each function who can bridge the gap between their teams & the analytics function

Data Literacy Initiatives

• Executive Dashboard Design - Build high-level dashboards for leadership tracking core business health, risk indicators and operational KPIs at a glance

• Operational Reporting Automation - Create automated daily/weekly reports for ops, compliance, and finance teams - replacing manual Excel work with reliable, scheduled delivery

• Domain-Specific Analytics Views - Design specialized dashboards for payment performance, compliance monitoring, customer economics, and channel efficiency tailored to each function's needs

• BI Tool Implementation & Optimization - Select, configure, and maintain BI platforms (Looker, PowerBI, Tableau) with proper data connections, governance, and user access controls

• KPI Framework Development - Define the right metrics to track by function and growth stage, establishing consistent measurement across the organization

• Self-Service Analytics Infrastructure - Build data models and reporting templates that enable teams to explore data and create custom views without breaking things or creating inconsistent metrics

• Performance Monitoring & Alerts - Set up automated alerts when key metrics move outside acceptable ranges, enabling proactive response to issues

Reporting & Dashboards