Getting Real Value Out Of AI In Financial Services: 4 Use Cases

People are tired of talking about artificial intelligence (AI). They want action. Since the launch of ChatGPT, the financial services industry has been looking for ways to drive value with AI, but it's been a struggle to get real value out of AI experiments and pilot projects.

The banking industry prefers to avoid potential risks, so how can financial sector leaders move from AI experimentation to AI value while being mindful of risk tolerance? By implementing AI in the processes that run their businesses and financial products. AI can help improve most of them. Here's how financial organizations are doing this already.

Many financial leaders are investing in AI, but true value lies in strategic implementation. AI is transforming processes like onboarding, loan approvals, and KYC, making them faster and more accurate. Teams I work with have seen significant benefits by integrating AI into a wide range of routine tasks. For example:

Through a pilot project using intelligent document processing (IDP) with generative AI and robotic process automation (RPA), a multinational bank improved its document extraction accuracy rates from 66% to 97%. This improvement would also lead to a significant cost reduction, lowering total ownership costs from AUD 13 million to AUD 7 million.

Why it matters: Higher accuracy cut rework, reduced costs, and improved customer experience.

A leading mortgage lender enhanced efficiency in document processing and auditing while improving data extraction accuracy. Using IDP and generative AI, they reduced processing time from 3–4 minutes per document to just 30–60 seconds. This led to better customer experiences while letting their teams focus on more strategic activities or complex tasks that require human oversight. 

Why it matters: Efficiency in document processing improves productivity internally, while providing a better experience to banking customers.

A large Latin American bank automated critical financial document workflows to boost operational efficiency. In three months, they automated document classification and extraction with AI. This allowed them to bring the process in-house and cut outsourcing costs. As a result, they transitioned the process back to their in-house financial operations.

Why it matters: The bank was able to find substantial cost savings by eliminating outsourcing.

An Australian consumer financial services organization enhanced productivity by streamlining finance applications and loan eligibility. Since integrating AI into their workflows, they have seen quantifiable benefits, including a 70% increase in business volume, with 85% of mortgage applications now approved within a single day.

Why it matters: The bank is able to do more business faster, while improving customer services.

So how do financial sector leaders find this value? The challenge isn’t always about creating new uses for generative AI—it’s how these AI-powered models are deployed in existing banking operations. 

For the financial services sector, processes define operations, customer interactions, and competitive advantages. Embedding AI within these workflows gives it purpose, governance, and accountability. This ensures it doesn’t just improve productivity but leads to concrete business benefits while enhancing risk management.

When AI is introduced in isolated projects, it typically requires custom integrations, extensive training, and specialized infrastructure, making deployment slow and costly. Embedding AI into an existing, structured process speeds implementation, allowing organizations in the financial industry to leverage AI with minimal effort and greater efficiency.

Processes inherently provide structure. They define goals, critical roles, and workflows. When AI is integrated into a process, it continuously drives tasks forward instead of passively waiting for human intervention. For example, AI's deep learning can automatically classify and route emails based on content and sentiment analysis, handle errors, and follow escalation protocols. Instead of having to read thousands of emails, employees can focus on work that requires human intelligence.

AI thrives on high-quality data. The more relevant data AI has access to, the more valuable its actionable insights become. By embedding AI within a process, it can gain seamless access to enterprise-wide data in a regulated manner. Technologies like data fabric ensure AI receives only the necessary data at the right time with a single source of truth, minimizing operational risk, improving regulatory compliance, and eliminating the need for costly data migration.

Unstructured AI poses inherent risks, especially in financial services. Embedding AI within a process ensures strong guardrails, such as human oversight for approval steps tied to high-risk actions, AI activity logs for auditing, and built-in escalation paths to mitigate errors. This structured approach ensures AI operates within compliance and risk management frameworks.

Many organizations in the banking sector struggle to determine whether AI is delivering quantifiable benefits. Within a process, every AI action is tracked. This lets IT and finance teams measure performance, determine levels of efficiency, and continuously optimize workflows. AI is no longer a “black box” but a transparent, accountable tool for growth that provides deeper insights that lead to better-informed decisions.

A process-driven approach allows AI to scale efficiently. Instead of fragmented AI pilot projects, organizations can use low-code platforms to integrate AI across multiple departments. This creates a unified AI strategy that drives consistent, enterprise-wide efficiency gains.

Sometimes the most impactful AI uses are the essential, daily processes on which financial firms run. You don’t need a moonshot AI strategy—just a process-driven approach that delivers value from day one.