How to use Generative AI to Automate Banking Workflows?

In the modern day hyper-competitive banking and finanncial services landscape, speed, accuracy, and personalization have become inevitables. Customers expect responses in seconds, personalized offerings, and seamless service — and banks must keep upto the speed and expectations.

Processes are configured through workflows. Typically in banking, even if the basic construct of the processes remain intact, there are always changes, tweaks and additonal steps in the processes. This is primarily based on regional nuances in business and regulation. It also has secondary angle to it, in terms of certain way banks handle their processes, without compromising the regulations. Under such pretext, it is nearly impossible to have exact 'point soultions' that fit into banks' requirements without any 'tailoring' needed.

This is where Generative AI (GenAI) can step in to resolve, not just as a buzzword, but as a real driver of workflow automation.

Having worked closely in the banking and financial services sector for close to two decades, I’ve been closely experienced how traditional workflow automation — mostly rule-based (and rigid) — often falls short. Banks now need intelligent, adaptable automation, where GenAI can help make that possible.

Let's explore how you can use Generative AI to automate banking workflows effectively, backed by real-world insights in more details, in my view:

1. Identifying the Repetitive and Language-Heavy Processes

Not every process in abnks can an ideal fit for GenAI. Focus on workflows that are:

• Repetitive

• Text/Language-heavy

• Require human-like reasoning

Not every process in banks can be good fit for GenAI. Focus on workflows that are repetitive, text-heavy, and require human-like decision-making.

For example:

• Customer service query handling

• Loan document summarization

• Compliance report generation

• Product recommendation engines

• Writing Emails for operations or approvals

➡️According to a 2023 McKinsey report estimatation that up to 30% of banking tasks — especially those involving documentation and communication — can be automated using Generative AI, leading to productivity gains between 9% to 15% across banking operations.

The key is to find processes where understanding and generation of language can reduce human effort.

2. Build Small, Targeted Use Cases First

Instead of launching a large AI project across departments, start small, with the low hanging fruits, that is where there is relatively more chances of success.

For example:

• Use a chatbot powered by GenAI to answer frequently asked questions (FAQs) for retail banking customers.

• Implement a GenAI-driven tool that drafts first versions of regulatory reports for compliance officers to review.

• Utilise loan document analyzers that extract, summarize, and highlight key risks automatically.

➡️According to a Gartner 2024 prediction, enterprises that adopt AI incrementally through 'targeted pilots' are 3x more likely to realize significant operational gains compared to those that attempt large, organization-wide deployments immediately.

Quick wins not only build confidence internally but also offer measurable ROI early in the journey. In case of failure, you course-correct, learn and move on quickly.

3. Integrate GenAI with the Existing Systems

The real power is exhibited when GenAI doesn’t sit in isolation but works inside your current workflows coherently.

For example:

• Embed GenAI into your CRM platform to auto-generate client meeting notes.

• Connect GenAI to your core banking system to automate pre-approval email drafts.

• Integrate with workflow engines (like Pega, Appian) to generate real-time summaries during case management.

➡️Accenture's Future of Banking report (2024) notes that banks integrating GenAI with legacy systems and customer journeys are seeing up to 20% faster process turnaround times — without massive core replacements.

A smooth core/ backend integration can ensure employees percieve GenAI as a helpful assistant, not an extra tool they have to manage separately.

4. Set Guardrails: Accuracy, Compliance, and Human Oversight

In banking, compliance isn’t optional. You have to configure GenAI to operate with:

• Content filters to avoid hallucinations or incorrect outputs to make the AI models more human-centric

• Approval checkpoints where humans validate critical outputs to refine the AI models

• Audit trails so every action the AI takes can be traced and reviewed

➡️The European Banking Authority (EBA) has issued guidelines suggesting that AI use in financial institutions must maintain explainability, accountability, and auditability to ensure regulatory compliance.

Imagine GenAI contributing to augmenting human decisions, not replacing them entirely — especially in regulatory and financial operations.

5. Monitor, Measure, Learn, Improve

Once any use case(s) is/are live, it’s vital to monitor their performance with the following criteria:

• How much time is saved?

• Are error rates reducing?

• Are employees spending more time on high-value tasks?

➡️A BCG study on AI Adoption in Financial Services found that continuous feedback loops and performance monitoring improved GenAI project success rates by over 25%.

Set clear metrics from day one, and treat GenAI automation as a continuous improvement cycle — not a one-time deployment.

Final Thoughts

Using Generative AI to automate banking workflows is more than about cutting costs or achieving efficiency. It’s about freeing up human potential, speeding up decision-making, and eventually delivering better customer experiences.

Banks that approach GenAI thoughtfully — starting small, integrating smartly, setting up guardrails, and learning fast with the fast evolution— will be the ones that leap ahead in the next wave of banking innovation.

The future isn’t just going to be digital. It’s going to be intelligent/congnitive.

References

• McKinsey, The Economic Potential of Generative AI: The Banking Sector Report, 2023

• Gartner, AI Adoption Patterns in Banking, 2024

• Accenture, Future of Banking: Harnessing GenAI, 2024

• European Banking Authority (EBA), AI Governance Guidelines, 2024

• BCG, AI Adoption in Financial Services, 2023

⚡Please feel free to comment or disucss.

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