The computer says Yes! AI accelerates efficient service delivery in the financial sector

In the financial sector, Lean and Six Sigma techniques have traditionally been used to streamline and standardize processes. The rapid rise of AI (artificial intelligence), however, offers transformative potential at the other end of the spectrum—complex cases. “In five years, this will be a standard tool for every bank and insurer. I'm convinced of that.”

Turner equips its clients with timely, relevant insights for their industries and for the strategy execution challenges they face. In this article, Frits Korthals Altes and Martijn Suiker—strategy execution experts in the financial services domain at Turner—share their views on the role of Lean and AI in strategy execution.

The computer says Yes! AI accelerates efficient service delivery in the financial sector

Straight Through Processing (STP) has brought major efficiency gains to the financial sector. Using Lean and Six Sigma, banks, insurers and financial institutions have simplified and automated a wide range of processes to reduce costs. Think of auto insurance, claims handling, or consumer loans—nearly every financial institution now works with rule-based processes that determine pay-outs, loan amounts and interest rates.

Where entire departments once handled these tasks—often relying on manual paperwork—process analysts now optimize and standardize. Process rules are designed upstream, ensuring flow throughout the chain for the majority of cases.

But according to Turner consultants Frits Korthals and Martijn Suiker, AI’s greatest impact is yet to come—specifically for complex exceptions that remain untouched by automation. “Standard processes are automated, but handling exceptions is not,” says Korthals Altes. “I’m talking about edge cases that take senior employees hours to resolve. AI can bring major efficiency gains in this space.”

Martijn Suiker adds: “Roughly 80% of cases are standard and take 20% of the time. The remaining 20% are complex and consume 80% of the effort. And while experts often enjoy working on complex cases—it’s where craftsmanship and judgement come in—these processes are costly. But if we can handle these cases more efficiently, we unlock major strategic opportunities. For example, we can now serve customer groups we previously excluded due to the complexity and associated cost of servicing them.”

A huge time saver

Frits Korthals offers a simple example: a chef breaks his arm and can’t work for six weeks.
“The chef needs a benefit. What happens? A junior specialist pulls the policy, summarizes the relevant clauses, consults internal guidelines and case law, then writes a report that’s reviewed by a senior colleague and sent to the customer. That can take hours. But with the right prompt, an AI program can deliver that report in five minutes. I may be exaggerating slightly, but the time savings are significant.”

Auto claims are already largely automated—but cases like this chef’s remain complex. Still, much of the effort could be handled more efficiently. “Some banks and insurers still have departments with dozens of employees managing these cases. AI can take over part of this work.”

Another example: property and fire insurance.
“There are insurers that decline to cover historic buildings, thatched-roof farms or homes in high-risk postal zones,” says Korthals Altes. “Too much risk. Sometimes brokers find workarounds, sometimes coverage just isn’t possible. That’s not great for customer satisfaction. AI can help here too—image recognition, for instance, can aid in property valuation, helping insurers say yes to more clients. That’s a real market opportunity.”

Nearly a million language models

Integrating these types of AI tools into operational processes is no small feat. “It’s essentially a new tool,” says Korthals Altes. “Until recently, exceptions were considered un-automatable—they required human judgement. Now, AI can help process complex cases faster, with fewer people. That inevitably changes your process model.”

It’s likely financial institutions will need fewer staff. But those who remain will need to develop new capabilities to work effectively with AI.

Turner’s role lies in change management and implementation—strategy execution, says Suiker.
“It starts with a shared ambition: what do we want to achieve with AI? Do we want to serve more complex segments? Or make services more accessible to underserved customers? Which processes must we adapt? These are delicate questions—especially when aligning business and technology.”

Turner applies its proven Strategy = Execution methodology, including MVP (minimal viable product) development to test and scale new ways of working. “Start small, align stakeholders, learn from what doesn’t work, and then scale and embed the improvements. Eventually, the organization can run it without us.”

Korthals and Suiker have spent recent years helping institutions implement AI solutions.
“Everyone thinks of ChatGPT, but there are nearly a million language models,” says Korthals. “Some are better suited to specific applications. ChatGPT can write a Shakespeare-style poem or a rap—but that’s not useful in finance.”

Data privacy is also critical. “You need to be cautious when feeding data into third-party systems. Fortunately, there are now secure solutions. But you still need skilled systems administrators and data specialists who know what they’re doing.”

The computer says yes

What will AI’s impact be in five years? “There are two developments,” says Korthals.
“First, we’ll be able to handle complex cases far more efficiently. Costs will drop—perhaps by half—and turnaround times will shrink. That means better customer service. That’s phase one.”

“Phase two: we’ll see these tools shift further upstream. Right now, we route 80% of cases through the standard process and flag 20% as complex. But in the future, AI will handle the complex cases too—eliminating the need for that distinction.”

Suiker agrees: “We’ll no longer need to label cases as standard or non-standard. With AI’s learning capacity, we’ll quickly determine how to handle any customer request. In five years, exceptions will no longer exist. We’ll just get to work.”

“It’s not just about cost savings—it’s about better service. That’s a major shift.”

From checkmarks to models

“Lean and Six Sigma emerged in the 1990s and became a Swiss Army knife for process automation,” says Korthals Altes. “Back then, even the idea of ticking a box met resistance—people insisted on a handwritten signature. Now, 25 or 30 years later, we’re handed a new tool. Implementing AI isn’t cheap, and it will take time. But I’m convinced: in five years, this will be a standard tool for every bank and insurer.”

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