Platform Intelligence Report

Value, Impact &
Lessons Learned

Quantified business outcomes, key takeaways, and strategic insights from the Unified Data Platform journey across all banking use cases.

Delivering Value & Impact

Measurable outcomes from the cloud-native transformation with SciKIQ Data Fabric

Cloud
Native Architecture
Migration from on-premise to Azure Landing Zone with SciKIQ Data Fabric
IaC
First in Region
First to deploy SciKIQ Data Fabric with Terraform IaC in the region
3 BU
Business Units Served
GC, GE & Corporate with dedicated compute resources
Intangible
Strategic Benefits
Accelerated innovation, improved agility, enhanced data governance & team upskilling

Competitive Landscape

How SciKIQ differentiates from traditional reconciliation and data fabric vendors in the financial services space

Financial Close Management
Best-in-class for account reconciliations and financial close automation. Strong in compliance workflows and GL integration.
  • Account reconciliation automation
  • SOX compliance & audit trails
  • Task management & workflows
  • Limited real-time data fabric
  • No embedded GenAI / NLQ
Gap: No unified data platform; requires separate ETL & data warehouse
Record-to-Report Automation
Strong in transaction matching and intercompany reconciliations. Good ERP integrations with SAP and Oracle.
  • Transaction matching engine
  • Intercompany eliminations
  • Basic ML matching rules
  • Limited to structured data
  • No data fabric architecture
Gap: Siloed tool; doesn't connect to broader data ecosystem
AI-Native Financial Close & Reconciliation
Unified data fabric with embedded GenAI. Goes beyond reconciliation to provide end-to-end financial intelligence.
  • 167+ pre-built connectors
  • GenAI NLQ & auto-commentary
  • AI/ML auto-matching (90%+)
  • Full data lineage & governance
  • 30-45 day deployment
Advantage: Only solution combining data fabric + recon + GenAI in one platform
Multi-Source Reconciliation
Flexible rule-based matching for high-volume transaction reconciliation. Good for banks with complex matching needs.
  • Configurable matching rules
  • Multi-source support
  • Basic exception management
  • No data quality layer
  • No AI/ML capabilities
Gap: Rules-only approach; no machine learning or predictive matching
Banking Core Integration
Deep integration with Fiserv core banking. Ideal for banks already on Fiserv stack but limited outside ecosystem.
  • Native core banking integration
  • Payment reconciliation
  • Fiserv ecosystem only
  • No multi-vendor support
  • No GenAI / analytics
Gap: Vendor lock-in; limited flexibility for multi-core environments
Enterprise Consolidation
Robust for large enterprises with complex consolidations. Heavy implementation and Oracle-centric architecture.
  • Multi-entity consolidation
  • IFRS/GAAP compliance
  • Long implementation (6-12mo)
  • High TCO
  • No embedded AI matching
Gap: Heavy, expensive; 6-12 month deployments vs. 30-45 days
Capability BlackLine Trintech ReconArt Fiserv Oracle SciKIQ
AI/ML Auto-Matching Basic Limited Add-on 90%+ rate
GenAI / NLQ Interface Native
Unified Data Fabric Separate Integrated
Pre-built Connectors 40+ 30+ 20+ Fiserv only Oracle stack 167+
Deployment Time 3-6 months 3-4 months 2-3 months 2-4 months 6-12 months 30-45 days
Auto-Commentary (NLG) AI-generated
Data Quality Layer Basic Basic Add-on Built-in
End-to-End Lineage Multi-hop
Cloud-Agnostic IaC Terraform

Why SciKIQ Wins

Key differentiators that set us apart from traditional reconciliation vendors

Unified Platform
One platform for data fabric, reconciliation, and analytics — no point solution sprawl
AI-Native
GenAI embedded from day one — NLQ, auto-commentary, and ML matching as standard
Speed to Value
30-45 day deployment vs. 6-12 months for legacy vendors. Pre-built, not custom-built
167+ Connectors
Connect to any source — SAP, Oracle, core banking, payment switches, and more

Banking Intelligence Impact

Quantified business value delivered across all platform use cases

# Use Case Business Problem What the Platform Does Quantified Impact
1
Revenue Assurance & Leakage
 Millions of transactions with complex fees; 5-8% revenue leakage Single truth layer; de-duplicates; automates reconciliation across schemes, processors, cores & GL/ERP
PHP 1.65B/year
recovering 1% of PHP 165.1B revenue
2
Real-Time Fraud & Auth
 Peak latency causes timeouts and false declines; poor fraud data feeds Real-time data spine; near real-time normalization, enrichment & routing; scales to billions/day
100-200ms reduction
fewer timeouts, higher approval
3
Compliance & AML
 Incomplete data for AML/KYC/PSD2/GDPR; slow manual lineage tracing Governed data foundation; full lineage & audit; ISO 20022-ready messages
Days → Minutes
AML investigation time
5
Dispute & Chargeback
 Reconstructing txn journey across acquirers, schemes, processors; slow correlated logs Single correlated timeline; rich metadata & audit; search APIs for case management
>400 staff-hrs/mo
30min → 5min per dispute
6
Auto-Commentary (NLG)
 Manual commentary on financial statements takes days; inconsistent quality AI-generated variance explanations for Balance Sheet & P&L with confidence scoring
80% time savings
days → minutes per report
7
Auto Matching & Recon
 Manual reconciliation of thousands of open items across banking systems AI/ML engine with exact, tolerance, aggregate & partial matching algorithms
90% auto-match
70% reduction in manual effort
8
Accounting Hub
 Fragmented accounting across multiple systems; manual journal entries Consolidated chart of accounts, automated posting rules, multi-entity support
Single truth
for all accounting entries
9
Data Strategy
 No unified metadata; data quality issues across domains; opaque lineage CDO function with metadata repository, quality monitoring & end-to-end lineage
99.5% quality
target across all 6 domains
10
Intercompany Flow
 Complex inter-entity transactions; manual elimination entries for consolidation Automated intercompany flow tracking, balance aging & elimination entries
95% faster
consolidation cycle
11
Marketing & Sales
 Siloed customer data; reactive marketing; missed cross-sell opportunities Customer 360 with AI cross-sell, campaign tracking & channel optimization
3x conversion
on cross-sell campaigns
12
RPA Dashboard
 25,000+ balance sheet accounts reconciled manually; repetitive operations 15 bots automating reconciliation, reporting, KYC & operational tasks
37+ FTE saved
PHP 18M+ annual savings
13
Control Tower
 No central view of automation portfolio; ad-hoc deployments Centralized CoE with automation inventory, pipeline & citizen developer program
PHP 100M+/year
across 45+ automations
14
SOX Reporting
 Manual SOX testing; tracking deficiencies in spreadsheets; audit anxiety Automated control testing, deficiency tracking & remediation management
100% readiness
zero material weaknesses target

Lessons Learned

Key takeaways from the enterprise data fabric implementation journey

Networking
  • SciKIQ's cloud-agnostic deployment works across multi-cloud environments with peered routable IP VNETs
  • Restricting the Control Plane's firewall by DNS or IP address space helps limit exposure for the SciKIQ fabric layer
Architecture
  • Decoupling business units, compute, and resources allows for agility, reduced dependency, and scalability
  • The Packaged Data Workspace, with bundled business capabilities, helps in managing dependencies and onboarding
Framework & IaC
  • A framework and metadata-driven approach helps scale the solution
  • Everything as configuration or code makes the solution declarative and maintainable
  • Leveraging Level Dependency Injection and Key Association Patterns addresses team dependencies
  • A collection of deployments worked well with the Iterate on Everything pattern
  • Azure CAFs Framework provides a foundation to build upon
  • Terraform is the preferred tool for cloud-agnostic and highly flexible IaC framework vs. Bicep or ARM
Resource & Quota Planning
  • Resource quota limits with Microsoft need to be planned for certain services
  • SciKIQ compute and connector limits should be managed in accordance with cloud provider quotas
  • Specifically, Postgres and Cosmos have been shown to require careful quota planning

Technology Stack

Unified Data Platform components powering the banking transformation

Cloud Platform
Azure Landing Zone SciKIQ Data Fabric Azure Data Factory EventHub Azure Monitor
Data & Storage
Storage Account (ADLS) MS-SQL DB Postgres Redis Cosmos DB
Infrastructure & DevOps
Terraform (CAFs) Airflow Key Vault Log Analytics Azure Active Directory
Security & Networking
Azure Firewall VNET / Subnet Virtual WAN DNS
Compute
Azure VM SciKIQ Compute Dedicated per BU
Common Services
Firewalls IP Address Mgmt Observability On-premise Networking
One Platform.
Every Banking Capability.
AI
AI Analyst

Welcome to the Banking Intelligence AI Analyst. I can help you with:

  • Revenue leakage analysis & recovery strategies
  • Fraud pattern detection insights
  • Compliance & AML risk assessment
  • Data quality recommendations
  • Executive-level briefings

Ask me anything about your banking data platform.