Featured banking data product

Modern Bank Data Platform

A consulting-style portfolio demo showing how a bank can turn fragmented source data into certified KPIs, governed AI insights, executive decision support, and a practical data product roadmap.

View Dashboard GitHub README GitHub Repo All Projects

Hiring signal

Data product leadership for banking analytics, governance, and AI readiness.

Executive KPIsMedallion architectureGovernance controlsAI evidence layer

Case study

Problem, solution, and impact

Problem

Regional and national banks often have strong reporting teams, but fragmented definitions across deposits, lending, complaints, risk, and digital channels make executive decisions slower than they should be.

Business challenge

Leadership needs growth, margin, risk, complaint, and AI-readiness signals in one place, with enough governance evidence to trust the numbers in board, risk, and product investment conversations.

Solution

Design a governed banking data product that moves source data through bronze, silver, gold, semantic, dashboard, and AI layers while surfacing quality, lineage, ownership, and policy controls directly in the user experience.

Business impact

The demo shows how a bank could reduce metric disputes, spot emerging customer issues faster, prioritize data remediation, and prepare AI use cases on top of certified data products.

Data strategy

Public and synthetic source strategy

Dataset	Business use	Portfolio-safe mode
CFPB Consumer Complaint Database	Complaint themes, product categories, response timeliness, issue severity.	Public CSV/API snapshot.
FRED economic indicators	Rate environment, unemployment, inflation, and macro overlays for executive analytics.	Public API or static extract.
FFIEC Call Report data	Peer benchmark ratios, deposits, loan categories, and regulatory context.	Future public-data connector.
Synthetic core banking data	Customer, account, transaction, loan, segment, risk, and governance facts.	Bundled portfolio seed data.

Architecture

From source systems to certified metrics and AI copilot

The architecture mirrors what a modern bank would need: medallion layers, semantic definitions, governance metadata, security controls, monitoring, and AI outputs grounded in trusted data.

Modern bank data product architecture

A governed path from operational systems to certified analytics and evidence-grounded AI.

Cloud medallion pattern

Source systems

Core banking, loan servicing, CRM, digital channels, CFPB, FRED, FFIEC, synthetic seed data

Bronze

Raw immutable extracts, schema capture, landing zones, ingestion logs, source freshness

Silver

Standardized customer, account, loan, complaint, balance, risk, consent, and region entities

Gold

Certified KPI marts, Customer 360, risk indicators, complaint analytics, executive aggregates

Semantic layer

Shared definitions for deposits, loan growth, NIM, delinquency, complaints, AI readiness

Executive dashboards

Operating review, risk committee scorecard, complaint trends, data quality scorecard

AI copilot

Evidence-grounded summaries, anomaly prompts, next-best investigation, governed retrieval

Governance and metadata control plane

These capabilities make the dashboard and AI layer defensible in a regulated bank.

Metadata

Dataset owner, refresh SLA, sensitivity, usage constraints

Business glossary

Certified KPI and critical data element definitions

Data catalog

Discoverable data products with approval status

Lineage

Source-to-metric traceability for audit and trust

Data quality

Rules, thresholds, exceptions, remediation owners

Master data

Customer identity resolution and householding

Security, monitoring, and AI controls

Controls that protect sensitive data and create audit-ready evidence.

Role-based access

PII classification

Audit logging

Retention policies

Model evaluation

Pipeline monitoring

Dashboard

Executive operating review

A realistic executive dashboard with balance sheet trends, credit and complaint indicators, customer segments, data quality, and AI-generated insights backed by evidence.

Executive operating review

Modern Bank Data Platform

Growth, risk, customer experience, governance, and AI-readiness in one certified banking data product surface.

Reporting period

Jun 2026

KPI certification

76%

Data products

Total deposits

+4.6% QoQ

$42.8B

Retail and commercial balances

Loan growth

+120 bps

+6.2%

Driven by middle-market lending

Net interest margin

+18 bps YoY

3.42%

Treasury-certified executive KPI

30+ day delinquency

-8 bps QoQ

1.42%

Consumer and small business loans

Complaint volume

-8.3% QoQ

1,098

CFPB-style monthly volume

Digital adoption

+2.7 pts YoY

68.4%

Active digital customers

Data quality score

+3.4 pts

94.1

Critical data elements passing rules

AI readiness score

+9 pts

Quality, lineage, consent, policy

Balance sheet momentum

Deposits, $BLoans, $B

Jan

Feb

Mar

Apr

May

Jun

AI-generated executive insights

Each insight is grounded in certified or review-ready data products.

Evidence required

Growth signal

High confidence

Commercial loan growth increased 6.2% this quarter, driven primarily by middle-market lending in healthcare, logistics, and professional services.

Gold loan mart, risk-grade completeness 97.4%

Customer experience alert

Medium confidence

Mortgage complaints increased 14% in the Southeast region, with escrow analysis and payment servicing as the fastest-growing themes.

CFPB-style complaint taxonomy, product mapping 94.1%

Digital adoption recommendation

Medium confidence

Investigate declining mobile engagement among customers over age 65; branch-assisted digital enrollment may reduce service calls and complaints.

Customer 360, channel events, consent coverage 99.2%

Complaint trend and severity

ComplaintsHigh severity

Jan

Feb

Mar

Apr

May

Jun

Credit / risk indicator	Value	Trend	Owner	Status
Commercial real estate exposure	$6.8B	+3.2% QoQ	Credit Risk	Watch
30+ day delinquency	1.42%	-8 bps QoQ	Portfolio Risk	Stable
Liquidity coverage proxy	118%	+4 pts QoQ	Treasury	Healthy
High-severity complaint themes	69	-5.5% MoM	Customer Experience	Improving

Customer segments and governed AI readiness

Segment economics are shown beside quality controls so leaders can see where data is ready for action.

Gold customer 360 mart

Mass market retail

1.42M customers · +2.4% growth

Digital service routing

Deposits

$13.4B

Loans

$8.2B

Digital

71%

Quality94%

AI readiness89%

Affluent households

214K customers · +3.8% growth

Relationship growth

Deposits

$11.7B

Loans

$5.4B

Digital

82%

Quality96%

AI readiness91%

Small business

82K customers · +5.1% growth

Renewal and fraud signals

Deposits

$7.8B

Loans

$6.6B

Digital

64%

Quality91%

AI readiness84%

Commercial clients

9.6K customers · +4.7% growth

Exposure monitoring

Deposits

$9.9B

Loans

$11.2B

Digital

58%

Quality88%

AI readiness79%

Critical data rule	CDE	Current	Owner	Steward
Customer identity match confidence	customer_id	96.8%	Customer Data Product	MDM Steward
Account balance freshness	current_balance	2.1 hours	Core Banking Platform	Deposit Ops
Loan risk grade completeness	risk_grade	97.4%	Risk Analytics	Credit Data Steward
Complaint product mapping	product_family	94.1%	CX Operations	Complaint Taxonomy Owner
Consent policy coverage	ai_eligible_flag	99.2%	Data Governance	Privacy Office

Product success metrics

Certified executive KPI coverage

Current 76%Target 90%+

Reduces metric disputes in operating reviews.

Critical data element pass rate

Current 94.1%Target 95%+

Improves trust in reporting and AI summaries.

Complaint theme time to detect

Current 7 daysTarget < 5 days

Creates earlier customer and operational risk visibility.

AI summary evidence coverage

Current 88%Target 100%

Ensures generated insights cite governed source data.

AI layer

Why AI needs clean, governed data

Product decision

The AI copilot is intentionally positioned after the gold and semantic layers. Executive summaries should only use approved data products with clear ownership, lineage, sensitivity classification, and quality thresholds.

That makes AI a trusted consumption experience rather than an ungoverned narrative generator.

Growth signal

High confidence

Commercial loan growth increased 6.2% this quarter, driven primarily by middle-market lending in healthcare, logistics, and professional services.

Gold loan mart, risk-grade completeness 97.4%

Customer experience alert

Medium confidence

Mortgage complaints increased 14% in the Southeast region, with escrow analysis and payment servicing as the fastest-growing themes.

CFPB-style complaint taxonomy, product mapping 94.1%

Digital adoption recommendation

Medium confidence

Investigate declining mobile engagement among customers over age 65; branch-assisted digital enrollment may reduce service calls and complaints.

Customer 360, channel events, consent coverage 99.2%

Governance

Enterprise controls that make the product credible

Rule	CDE	Domain	Current	Owner	Why it matters
Customer identity match confidence	customer_id	Customer	96.8%	Customer Data Product	Prevents duplicate households, broken journeys, and unreliable AI personalization.
Account balance freshness	current_balance	Deposits	2.1 hours	Core Banking Platform	Keeps liquidity, margin, and executive deposit reporting aligned to operating reality.
Loan risk grade completeness	risk_grade	Credit Risk	97.4%	Risk Analytics	Supports portfolio monitoring, stress analysis, and responsible AI-generated risk narratives.
Complaint product mapping	product_family	Complaints	94.1%	CX Operations	Connects complaint themes to accountable product owners and remediation plans.
Consent policy coverage	ai_eligible_flag	AI Use	99.2%	Data Governance	Ensures AI use cases respect customer consent, sensitivity, and approved policy boundaries.

Governance assumptions

PII classification: Customer identifiers, contact details, balances, transactions, and complaint narratives would be classified, tokenized, and access-controlled.
Retention: Complaint, transaction, model-output, and audit records would follow bank retention schedules and legal hold requirements.
Audit logging: Dashboard access, AI summary generation, source refreshes, rule failures, and certification changes would be logged.
AI governance: Executive narratives would require approved data products, evidence links, confidence labels, model evaluation, and human review for sensitive use.

Product management

Artifacts from an enterprise product team

Executive stakeholders

Executive committee: Allocate investment across growth, risk, operations, and AI readiness.
Chief Data Office: Prioritize data products, ownership, quality remediation, and catalog certification.
Risk and compliance: Monitor credit, conduct, liquidity, complaints, retention, and audit evidence.
Digital and retail banking: Improve adoption, complaint drivers, customer journeys, and service outcomes.

User personas

CFO / executive committee: Board-ready deposit, loan, margin, and risk trends with certified definitions.
Chief Data Officer: Funding and prioritization signals for data products, ownership, lineage, and quality remediation.
Director of Analytics: Reusable semantic metrics and trusted marts for dashboards, analysis, and self-service reporting.
Enterprise architect: A clear reference architecture connecting operational systems, governance, security, analytics, and AI.
AI product manager: Approved data products, consent-aware metadata, and evidence-backed executive AI outputs.

Quarter	Theme	Scope	Outcome
Q1	Executive KPI foundation	Certify deposit, loan, margin, complaint, and delinquency definitions; publish dashboard prototype.	One executive metric language
Q2	Governed data products	Add catalog ownership, CDE monitoring, lineage, and CFPB/FRED static ingestion snapshots.	Trusted source-to-metric traceability
Q3	Risk and CX expansion	Extend Customer 360, regional complaint themes, risk indicators, and role-based access patterns.	Earlier issue detection
Q4	AI decision support	Add governed executive narrative generation, retrieval guardrails, evaluation logs, and adoption telemetry.	AI outputs leaders can trust

Priority	User story	Acceptance criteria
P0	As a CFO, I need deposit and loan trends reconciled to certified definitions so board reporting is consistent.	KPI owner, definition, source lineage, refresh SLA, and variance notes are visible.
P0	As a Chief Data Officer, I need quality and lineage visible beside KPIs so platform risk is discussed in business terms.	Every executive KPI shows CDE coverage, quality score, owner, and lineage status.
P1	As a risk leader, I need complaint themes joined to product and segment data so emerging conduct issues are found earlier.	Complaint trends can be grouped by region, product family, severity, and customer segment.
P1	As an AI product manager, I need approved data products tagged by consent and sensitivity so pilots do not bypass governance.	AI summaries only use certified gold datasets with sensitivity and consent metadata.

Definitions

Glossary, KPIs, success metrics, and lineage

Business glossary	Definition
Certified KPI	A metric with approved business logic, owner, refresh target, source lineage, and known limitations.
Critical data element	A field that materially affects financial reporting, risk decisions, customer treatment, regulatory response, or AI output quality.
AI readiness index	Composite score for quality, freshness, lineage, sensitivity tagging, consent coverage, policy approval, and monitoring.
Gold data product	Curated business-ready dataset or metric layer designed for analytics, reporting, risk review, and approved AI use cases.
Complaint severity	Portfolio-safe score based on CFPB-style issue category, recurrence, response timeliness, customer impact, and operational risk.

KPI	Definition	Owner	Status
Deposit growth	Period-over-period change in end-of-day deposit balances by product, region, and segment.	Treasury	Certified
Loan growth	Period-over-period change in outstanding loan balances by product, segment, and risk grade.	Credit Risk	Certified
Complaint rate	Consumer complaints per 10,000 active customers, grouped by product family, channel, region, and severity.	Customer Experience	Certified
AI readiness index	Weighted score for data quality, freshness, lineage, sensitivity tagging, consent coverage, and policy approval.	Data Governance	In review

Success metric	Current	Target	Why it matters
Certified executive KPI coverage	76%	90%+	Reduces metric disputes in operating reviews.
Critical data element pass rate	94.1%	95%+	Improves trust in reporting and AI summaries.
Complaint theme time to detect	7 days	< 5 days	Creates earlier customer and operational risk visibility.
AI summary evidence coverage	88%	100%	Ensures generated insights cite governed source data.

Lineage examples

CFPB complaint issue -> complaint taxonomy -> product family -> regional complaint rate -> executive customer experience alert.
Core deposit account balance -> daily silver account snapshot -> gold deposit trend mart -> CFO operating review.
Loan servicing status -> credit risk silver table -> delinquency indicator -> risk committee scorecard.
Customer consent flag -> governed customer profile -> AI eligibility policy -> executive summary guardrail.

Tradeoffs

Product decisions, lessons learned, and future roadmap

Tradeoffs

Use static synthetic data for portfolio reliability; document live CFPB, FRED, and FFIEC integration points separately.
Prioritize executive operating decisions over deep transaction-level drill-down.
Expose quality, lineage, and ownership directly in the UI because trust is part of the product experience.
Treat AI as a governed consumption layer, not a replacement for metric certification and data stewardship.

Lessons learned

Executive dashboards are only persuasive when metric definitions, owners, and exceptions are visible.
AI readiness is a data product outcome, not a model feature.
Complaint analytics becomes more useful when joined to product, customer, region, and operational ownership.
A portfolio demo should show the tradeoffs a real product team would make, not just ideal-state architecture.

Future roadmap

Q1: One executive metric language
Q2: Trusted source-to-metric traceability
Q3: Earlier issue detection
Q4: AI outputs leaders can trust