Banking

Unlock value in the Banking sector, with AI.

AI is reshaping how banks serve customers, manage risk and financial crime, and run end‑to‑end operations—but scaling it is harder than building a model. Regulatory constraints, data governance expectations, and workforce adoption gaps mean “pilot success” rarely translates into enterprise impact without the right operating foundations.

AI is saving banks money, but trust remains priceless. Many banks are already achieving cost savings through AI implementation, but security, data privacy and ethical risks remain major concerns. Building strong frameworks is key to staying compliant and scaling responsibly.

Antony Ruddenklau

Partner, Head of Financial Services

KPMG in Singapore

Make AI work for you.

Move from AI pilots to scalable AI solutions
with the KPMG AI Centre of Excellence.

The Government Sector value propositions through
KPMG’s four-door Framework

Identify high‑impact AI use cases across services, policy, risk, and operations
Prioritise initiatives based on public value, feasibility, and risk
Scale AI from pilots to secure, enterprise‑wide adoption

Embed governance, security, and regulatory alignment into AI by design
Manage model risk, data quality, privacy, and third‑party exposure, especially for sensitive public data
Build confidence with boards and regulators through transparent, auditable AI decisions

Enable officers to use AI confidently in day to day roles, not as isolated specialists
Redesign work and operating models so humans and AI collaborate effectively
Build AI literacy and adoption across frontline, policy, risk, and corporate functions

Build AI ready data and technology foundations aligned to government architectures
Move beyond fragmented tools to scalable, secure enterprise platforms
Enable AI to scale safely across agencies, functions, and channels

The Banking sector value propositions through KPMG’s Four-Door Framework

VALUE

AI Strategy & Value Realisation (Banking outcomes by design)

Prioritise 2–4 high-impact use cases tied to clear business KPIs and data feasibility (so value is measurable from day one)
Build the investment case and operating model (ownership, KPIs, run-model) to move beyond “innovation theatre"
Scale proven pilots into core workflows to deliver productivity, service uplift, and cost impact—not isolated demos

TRUST

Trusted & Responsible AI (Banking trust by design)

Embed governance, compliance and security by design so AI can meet regulatory expectations in a controlled way
Make AI decisions traceable and explainable (evidence, audit trail, transparency) when AI contributes to customer or credit decisions
Operationalise continuous monitoring to detect drift, toxic outputs, or data leakage—treating validation as a lifecycle control

PEOPLE

Human‑AI ways of working (adoption is the multiplier)

Close workforce and talent gaps so AI is adopted beyond specialists and can scale across the bank
Drive colleague adoption with training + incentives to make AI usage stick in day‑to‑day work
Embed human oversight and accountability so people remain responsible for outcomes and risk decisions

DATA & TECH

Built to scale (foundations that industrialise AI)

Fix fragmented data and legacy constraints that prevent accuracy, integration, and enterprise scaling
Modernise platforms and data architecture (e.g., enterprise cloud/hybrid foundations) to support scalable AI services
Move from point tools to secure enterprise platforms so AI can be deployed consistently across functions and journeys