PCB

Prime Capital Bank
Data Intelligence Platform

Azure Databricks · Medallion Architecture · 12 ML Models · SARB Regulated · ZAR 500B Portfolio

Azure Databricks Delta Lake Power BI 12 ML Models Azure SQL dbt Core
500K
Customers
12M+
Transactions
R500B
Portfolio
R64.6B
AUM
200
Branches
12
ML Models
9
Provinces
Primary Deliverables
📈
Asset Management & Portfolio Analytics
The bank's wealth book: R64.6bn AUM across 3,400 discretionary portfolios in JSE equities, SA government bonds and offshore feeders. Allocation look-through, alpha against ALSI/SWIX/ALBI/MSCI benchmarks, concentration risk and live Regulation 28 breach monitoring.
R64.6bn AUMReg 28Portfolio Risk
🔌
Fintech Partner Analytics
12 SA payment partners, R51.9bn combined settlement volume over 12 months: acquiring, gateways, instant EFT, QR, open banking and cross-border remittance. Volume trends, fee-rate vs merchant-growth, chargeback risk and a full profile per partner.
12 PartnersR51.9bn VolumeFee Economics
⚖️
Fintech Settlement Reconciliation
Automatic, governed reconciliation of the real R15.75bn fintech settlement mart against bank receipts — clawbacks, in-transit and unmatched receipts flagged, each AI-narrated with full provenance.
ReconciliationGoverned AILive
📊
Finance Self-Serve Report · Power BI
Parameterised self-serve settlement report — slicers for partner, month and measure, live KPIs, trend/bar visuals, CSV export and the governed DAX behind it. Turns recurring finance ad-hoc requests into zero-ticket self-service.
Power BISelf-ServeDAX
📖
Technical Ebook
12-chapter deep-dive covering the full data platform: architecture, Medallion pipeline, ML models, fintech integration, SARB regulatory reporting, and AML compliance.
12 ChaptersChart.jsSelf-contained
🗂
Interactive ERD
6-tab Mermaid entity-relationship diagram covering all Gold schema domains: Core Banking, Lending, AML, Cards & Fintech, Treasury, and Regulatory. Includes zoom/pan controls.
6 DomainsMermaid.jsZoom/Pan
🗺
Merchant Intelligence Map
Interactive Leaflet.js map of SA merchant transaction volumes and fraud hotspots. Toggle between Sales Volume, Fraud Heatmap, and AML Case views across 9 provinces.
Leaflet.jsSA ProvincesLive Filters
📊
Executive Presentation
15-slide investor-grade deck: platform overview, KPIs, ML model performance, financial highlights, regulatory posture, and fintech ecosystem integration.
15 SlidesChart.jsExec-ready
📚
Data Dictionary
Searchable column-level documentation for all 15 Gold schema tables: data types, primary/foreign keys, nullable constraints, and business descriptions with SA regulatory context.
15 TablesSearchableAll Layers
📈
Analytics Workbook
Excel financial model with KPI dashboards, loan portfolio analysis, fraud trend charts, fintech revenue breakdown, and branch performance summary. Dynamic formulas throughout.
ExcelDynamicKPI Dashboards
Medallion Pipeline Architecture
🥉
BRONZE
Raw Ingestion
ADLS Gen2
🪙
SILVER
Cleansed & Typed
Delta Tables
🥇
GOLD
Star Schema
15 Dim + Fact Tables
🤖
ML LAYER
12 Models
MLflow Tracking
📊
CONSUMPTION
Power BI + dbt
Regulatory Reports
Machine Learning Models
Credit Scoring
0.847
AUC-ROC
XGBoost · 500K customers
Fraud Detection
0.921
AUC-ROC
Random Forest · Real-time
AML Risk Scoring
0.889
AUC-ROC
Gradient Boost · FIC Act
Churn Prediction
78.3%
Accuracy
Logistic Regression
Loan Default (PD)
0.832
AUC-ROC
XGBoost · IFRS 9 ECL
CLV Segmentation
0.74
Silhouette Score
K-Means · 5 Clusters
NLP Sentiment
83.1%
F1 Score
BERT · Complaints
Tx Anomaly Detection
0.876
Precision@K
Isolation Forest
Next Best Product — Cross-Sell Recommender
Item-based collaborative filtering over product ownership (which of the 22 PCB products each customer holds) — “customers who hold X also hold Y.” Evaluated with a leave-one-product-out test, refitting the similarity matrix per test to avoid leaking the held-out holding into its own evaluation. Honest caveat: this synthetic generator assigns each account's product independently at random per customer rather than by deliberate affinity (income, segment, or existing relationship), so the 1.58× lift below reflects sampling structure in a small 22-product catalogue, not a designed behavioural signal — the same limitation disclosed for the Pet Business Intelligence shop-assistant recommender this pattern is based on.
23.6%Hit rate @ 3
14.9%Random baseline
1.58×Lift
440Customers scored
22Products in catalogue

Look up a customer

Current products

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