Generative AI in Finance Use Cases, Risks and Solutions

Generative AI in finance is already making waves in banking

 

Globally, 58% banks are fully embracing its transformative potential, while 67% of BFSI enterprises in APAC have already deployed Gen-AI

 

In fintech-first markets like Singapore, leading banks are going beyond pilots and experiments — they’re using GenAI to redefine how finance works. From personalized wealth management to automated compliance – this technology is shaping a new frontier in customer experience and operational efficiency.

 

This blog explores 5 live use cases of generative AI in finance, with real-world examples from Singapore’s top financial institutions. We also map out the risks involved and how smart mitigation, governance, and design can make generative AI in fintech both scalable and safe for financial companies in 2025.

 

Let’s dive in!

 

What Makes Gen AI Personalization a Game-Changer in Banking?

Unlike traditional AI, generative AI in fintech doesn’t just process financial data – it translates it into context-aware, human-like responses. This means banks can now deliver hyper-personalized customer insights at scale, from budgeting nudges to investment suggestions in natural language that actually make sense to users.

 

For example, instead of showing a generic spending chart, a finance assistant can enable personalization with AI with a response like – “You spent 18% more on dining this month. Want to explore a savings plan to offset that?”

 

This shift from static data to conversational, actionable insight is what makes personalization with Gen AI a true differentiator — one that drives both user satisfaction and business results.

5 Proven Use Cases of Generative AI in Finance

The use of AI in banking and finance is leading to major transformations, and Gen AI is a key part of that.

 

Below are 5 real-world use cases of Gen AI in banking and finance being adopted by financial institutions across Singapore and beyond — each delivering both speed and strategic advantage.

 

1. Personalized Financial Advice

Generative AI in fintech translates raw financial data like a customer’s transaction history, investment patterns, and risk appetite into human-readable, tailored recommendations.

 

It bridges the gap between complex data and everyday financial decisions – making wealth advice more accessible, timely, and relevant.

 

Example: In Singapore, DBS and OCBC have begun rolling out gen-AI-powered banking apps and assistants that offer 24/7 tailored financial guidance, contextual spending insights, and investment nudges for individual customers.

2. Conversational AI for Customer Service

What it does: Generative AI in finance powers multilingual, real-time virtual assistants that understand customer queries, summarize previous and real-time interactions, and respond with contextual intelligence.

 

Conversational AI in financial services enables 24/7 customer support that’s fast, scalable, and increasingly human-like — without sacrificing tone or accuracy.

 

Gen AI chatbots are also being used internally as agent assists for customer service officers – helping relationship managers with client histories & preferences or banking staff to resolve queries faster, access knowledge in real-time, and reduce handling time.

 

For Example: DBS Bank has recently launched a CSO Assistant that is trained on local languages, offers voice telephony & speech recognition as well. It is being used by 25000+ employees and is expected to reduce call handling time by 20%

 

3. Hyper-Personalized Onboarding

Gen AI automates the drafting of regulatory documents like Source of Wealth (SoW, a statement explaining how a customer acquired their wealth) narratives and KYC summaries by interpreting client data and compliance rules.

 

It streamlines onboarding processes that typically take hours – enabling faster, more accurate client activation with built-in compliance alignment.

 

For Example: Bank of Singapore, with OCBC Group, piloted a MAS-supported Gen AI system that auto-generates SoW write-ups and onboarding summaries for HNWIs (high-net-worth clients), reducing manual effort while meeting regulatory standards.

 

4. Credit Risk Assessment & Loan Underwriting

Traditional credit scoring relies heavily on machine learning models for structured data like income, transactions, and repayment history.

 

Generative AI here adds more value by synthesizing unstructured data such as application notes, news, or reviews – and generating plain-language credit memos and risk narratives for underwriters.

 

It helps risk teams explain decisions clearly to regulators and stakeholders while making underwriting faster and more transparent.

 

McKinsey predicts that 80% of credit risk organizations globally are expected to implement Gen AI technologies soon.

5. Automated Report & Document Generation

Gen AI automates the creation of internal and client-facing documents — including compliance reports, investment research summaries, and service communications.

 

It significantly reduces time spent on drafting, allowing banking staff to focus on high-value analysis and decision-making while maintaining consistency and tone.

 

In fact, generative AI in finance is also being used for governance and risk reporting as it can generate policy summaries, compliance narratives, regulatory reports, and internal audit documentation – a major MAS focus area in Singapore.

 

For Example: OCBC Bank rolled out a Gen AI tool across 30,000 employees globally, enabling teams to generate investor reports, draft emails, and prep documents using an Azure OpenAI-powered assistant. Result? Employees reported at least 50% faster productivity!

 

What are the Risks of Generative AI in Finance and How to Mitigate Them?

Gen AI promises speed, personalized AI banking features, and operational lift, but when it comes to finance, that potential comes with high-stakes risks.

 

Below are key risk categories banks must plan for, and how to mitigate them with strategic interventions.

 

1. Hallucinated Outputs

Risk: LLMs can generate outputs that sound confident but are factually incorrect or completely fabricated.

 

Fixes:

• Use retrieval-augmented generation (RAG) — a technique that pulls in verified external information like updated compliance documents, regulations, and such aside from its trained data. This grounds Gen AI responses in real data.
• Add a human-in-the-loop review layer, especially for customer-facing or compliance-critical interactions.

 

2. Regulatory Non-Compliance

Risk: Gen AI outputs may inadvertently violate MAS or global financial regulations (e.g., KYC/AML).

 

Fixes:

• Create Gen AI guardrails using pre-approved templates, decision trees, and logic flows.
• Maintain audit logs of every Gen AI recommendation.
• Train models using regulatory-compliant data sets.

 

3. Data Privacy & PDPA Violations

Risk: Chatbots and report generators may expose PII (personally identifiable information) or breach consent boundaries.

 

Fixes:

• Apply masking and anonymization before data ingestion.
• Limit Gen AI access to only required data fields.
• Incorporate PDPA checks and privacy flags into workflows.

4. Bias in Risk & Lending Narratives

Risk: Historical bias in data can be amplified by Gen AI, especially in credit assessments or wealth profiling.

 

Fixes:

• Separate Gen AI functions into insight generation vs. decision-making.
• Conduct third-party fairness and bias audits regularly.

 

5. AI Explainability Gaps

Risk: LLMs can’t always explain how they arrived at a conclusion. Use of such “black box” models is a serious issue for financial institutions under regulatory scrutiny.

 

Fixes:

• Require all outputs to cite source content.
• Use models designed with explainability-first architecture.
• Integrate audit trail tooling into the UX design.

How Banks Can Implement Generative AI Safely in Singapore

Singapore is one of the most innovation-friendly financial hubs globally, but it enforces clear lines on responsible AI.

 

Leading banks and fintechs in Singapore are already realizing the importance of ethical AI and taking measures. For instance, DBS Bank uses the PURE (purposeful, unsurprising, respectful, explainable) principle framework in all its AI and Gen-AI initiatives.

 

Here’s how financial leaders can scale Gen AI safely:

 

1. Co-Create with Regulators

If you’re handling on Generative AI Singapore projects, work with MAS via sandbox pilots, FinTech Regulatory Accelerators, or Digital Finance initiatives to ensure early alignment on compliance.

 

2. Design for Trust from Day One

Don’t bolt on explainability later. Aside from using reliable Explainable AI models, you must also build transparency into the UX design, data pipelines, and reporting logic from the start.

3. Align Legal, Tech, and Design Teams

Create an AI Governance Taskforce that brings together legal, design, risk, and tech stakeholders. Make compliance a design and dev requirement – not a bottleneck.

 

4. Use Layered Model Architectures

Use Generative AI in finance only where human review and grounding are guaranteed. For risk-sensitive tasks, combine traditional machine learning models with Gen AI summarizers.

5. Operationalize Governance

Document model boundaries, assign internal ownership, and train frontline teams. Transparency isn’t just policy – it’s practice.

 

Generative AI in Finance: Designing User Experiences

It’s easy to get caught up in the automation hype. But in finance, user trust is the real differentiator. This is one of the reasons why AI in financial services fails without good UX design.

 

Designing for Gen AI isn’t just about what the AI model can generate – it’s about how responsibly and transparently that output is delivered to customers, regulators, and internal teams.

 

To build human-first, AI-driven banking experiences:

 

• Use personalization in design (eg: dynamic dashboards) without crossing into surveillance
• Make disclosures visible and user-friendly
• Design fallback paths and human handover options
• Test tone and language for cultural sensitivity

Why Human Oversight Matters for Generative AI in Finance

Gen AI isn’t just disrupting finance – it’s reshaping it.

 

But without robust governance, human oversight, and transparent interfaces, the same tools can introduce new vulnerabilities.

 

As Gen AI becomes even more advanced, human oversight will remain non-negotiable – especially in contexts like credit, compliance, and fraud. Machines can generate results, but it takes humans to validate, explain, and own the decisions that matter.

 

At ProCreator, we believe great design is a key part of this line of innovation and defense.

 

We’re an AI design and development company in Singapore that helps leading banks and fintech companies bring their Gen-AI innovations alive in the user interface. We make sure that the UX is truly ready for your Gen-AI models, and bake in the necessary design compliances for customer trust.

 

Result? Your Gen AI-enabled apps and websites turn out human-first, explainable, and audit-ready.

 

Reach out for a business consult at ProCreator today.

 

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