AI in Banking: How to lead without compromising compliance

AI in banking is no longer optional, but it is a double-edged sword. Regulatory demands, especially in strict markets like Switzerland, have kept many leaders hesitant. Adopt AI too fast and you risk non-compliance. Move too slow and all of your competitors will pull ahead. 

But what if you didn’t have to choose between innovation and regulation?

In this case study, we will show how one of the leading banks in Switzerland transformed its operations with AI. With the help of AI, they managed to achieve 95%+ reduction in manual reviews, real-time compliance at scale and zero disruptions to their customers. It all started with defining the right use cases and choosing the right AI partner. 

The leadership challenge was compliance at scale

Our client, a leading Swiss bank, was determined to lead the industry in applying AI. But leadership came with complexity. Strict Swiss regulations, frequent external audits, and high-volume transaction environments posed major challenges. Compliance, the biggest challenge, was just the entry point. To lead the way, our client needed AI that could operate in real time, adapt to evolving threats, and integrate seamlessly into critical workflows. 

We helped them prioritize their most impactful use cases: detecting fraudulent transactions at scale, uncovering complex money laundering patterns, screening new clients through robust KYC workflows, and gaining visibility into anomalous behaviors and network relationships. Together, we have built a portfolio of AI solutions that satisfied all regulatory expectations and elevated internal operations to a new level. 

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How we helped: Pillars of AI-driven compliance in banking

To turn these priorities into measurable results, we built a series of AI-driven compliance solutions, each addressing a critical challenge while meeting the strictest Swiss banking regulations.

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Real-time risk scoring & transaction monitoring

Evaluating transactions in real time, while staying compliant with strict Swiss regulations, was a major challenge for our client. Due to the high volume of daily transactions, manual validation was  overwhelming, draining the team and limiting scalability.

We built a real-time rule-based engine. It evaluated multiple parameters such as the transaction amount, destination of transaction, and various other contextual data against pre-defined compliance rules. 

Based on the evaluation, every transaction got assigned a severity score. The score determined what level of manual validation was required. The system was automatically flagging suspicious activity and involving human reviewers as needed. 

As a result, the volume of manual transaction reviews got significantly reduced (up to 95%). This allowed compliance teams to focus on high-impact cases without slowing down processing speed.

Next-gen KYC with LLM-generated risk summary

As the client base grew, the employees got overwhelmed with assessing the risk associated with new clients, both individuals and business entities. It took hours of work to manually screen all available databases. Our client wanted a scalable solution that would reduce the manual work. 

We developed a KYC screening solution that pulls data from multiple structured and unstructured sources, including Google, Yandex, WorldCheck, Dow Jones, and various sanction lists. This data is processed and summarized using a large language model (LLM), which produces a human-readable summary and a calculated risk score for each potential client. The solution flagged all high-risk clients for additional reviews. 

The outcome was transformative. Our client reduced onboarding times and improved decision consistency. This led to faster and safer client acquisition.

Advanced anti-money laundering pattern detection

Money laundering schemes are becoming more sophisticated as you are reading these lines. With the increased sophistication, it is becoming increasingly hard to detect money laundering schemes, especially through single-transaction analysis. Now the schemes involve multiple transactions and entities (this includes individuals and companies). Getting ahead of the new fraudulent activity is a race against time. We always need to be a step ahead and envision, create and deploy tools that will be able to detect not only existing fraud schemes but also future ones. 

We implemented a graph-based analysis system that models the relationships between clients and transactions. This system identifies suspicious transaction chains and network behaviors, such as circular flows of money or anomalous intermediaries. The system is using both pre-defined laundering patterns and advanced statistical anomaly detection. This dual approach enables the detection of threats that do not match known patterns, significantly enhancing the bank’s ability to uncover emerging and sophisticated financial crimes.

The solution in place enabled our client to detect complex financial crimes that traditional systems missed. This enhanced the bank's ability to stay ahead of evolving threats.

Behavioral profiling 

Manual reviews led to a lack of ability to distinguish between typical and atypical client behavior in real time. This led to false positives in fraud detection, and let’s face it, no bank client wants to get an online purchase denied because there has been a mistake in the system. In this day and age, clients’ expectations regarding safety are high and rightfully so. 


We built a transaction-level behavior model that assesses whether a transaction fits within a client's historical behavior patterns. This included typical spending categories, geographic locations, and amount ranges. Significant deviations from standard spending habits immediately got flagged for additional human review. 

The system reduced false positives and increased the accuracy of fraud detection while maintaining real-time processing capabilities.

Client registry automation & CRM synchronization

Data accuracy is the backbone of bank operations. Our client had to find a way to keep the client data up to date. A recurring problem was that many entities failed to report changes, such as board member updates or changes to legal entities; even the slightest change as an address change, could cause serious problems. Manual checks were no longer an option. 

We developed an automated monitoring system that continuously queries official commercial registries across all operating jurisdictions, compares records against existing client data, and flags discrepancies in real time. The platform accelerates detection and reporting while maintaining a human-in-the-loop model for both reviews and updates, an intentional safeguard to ensure regulatory compliance and decision quality. Over time, this foundation positions the bank to selectively automate updates once governance thresholds and risk controls are fully satisfied.

Work that took weeks now takes minutes; the manual verification is kept only for operation-critical data. This resulted in improved data accuracy and ensured compliance with regulatory obligations on data integrity.

The impact: Precision, performance, and compliance

By embedding AI across risk operations, the bank reduced manual workload, uncovered complex threats faster, and ensured that compliance systems could evolve with regulation, not fall behind it.

Each developed and deployed solution addressed a key bottleneck:

• Risk scoring moved from reactive to real-time.
• Client screening became faster and more consistent.
• AML detection expanded beyond single transactions into full behavioral networks.
• Data accuracy was no longer dependent on human upkeep.

The results speak for themselves:

• 95%+ reduction in manual transaction reviews
• Over 1 million transactions analyzed in real time
• Zero disruption to customer experience

This case study shows that the AI change went above and beyond metrics; it was an organizational shift. Compliance became an enabler, not a blocker. Risk teams became proactive, not reactive. Our client managed to position itself not just as a follower of regulation, but as a leader in using AI responsibly and effectively in the banking industry. 

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