In 2025, financial institutions are under immense pressure to reduce costs while maintaining operational efficiency and service quality. AI is proving to be the ultimate tool in this endeavor, automating repetitive tasks, optimizing data consumption, and improving decision-making processes. According to NVIDIA’s fifth annual State of AI in Financial Services report, financial institutions have consolidated their AI efforts to focus on core applications, signaling a significant increase in AI capability and proficiency.
For Olga Zueva, an expert in banking transformation, financial strategy, and AI-driven operating model optimization, using smart technologies is necessary. She led a market data cost optimization initiative at leading international banks, identifying inefficiencies in AI-powered financial terminals and renegotiating vendor contracts, which resulted in 5–10% annual savings, which is tangible for multimillion-dollar financial institutions. Her expertise in demand-right-sizing frameworks helped financial institutions rethink how they procure and utilize market data.
This article explores how AI transforms cost optimization in the financial sector, highlighting key industry trends and real-world applications.
Market Data Optimization
Market data is a massive expense for financial institutions, yet many banks overpay for redundant feeds and analytics platforms. AI-driven optimization tools can track usage patterns, eliminate inefficiencies, and renegotiate vendor contracts to reduce costs significantly.
Olga led a cost optimization initiative for a major global bank to help them cut unnecessary costs without losing access to essential financial data. She designed and led the implementation of an AI-powered system that analyzed how employees used market data tools. To build this system, she first conducted a detailed audit of data usage patterns, working with finance, procurement, and front-office teams to map out subscription costs and actual utilization across departments. The system quickly revealed that many subscriptions were unused or duplicated across teams. For example, one group of traders had access to premium analytics tools but hadn’t used them in months, wasting money. In another case, many employees had individual Bloomberg Terminal licenses, even though they didn’t use real-time market data often. This suggested they could save money by sharing access instead. The system also found that some staff were paying for data packages that didn’t match their job needs, like traders using tools meant for quants. Based on this, the bank restructured its subscriptions to better match what people needed through dynamic licensing, role-based access controls, and real-time governance dashboards —saving costs without losing key tools.
The bank saved millions by eliminating these unnecessary expenses and renegotiating vendor contracts while still providing employees with the market insights they needed to make smart financial decisions.
“I remember a trader telling me, ‘We’re spending a fortune on market data, but half of these tools go unused. That stuck with me. When we rolled out AI tracking, we found that tons of unused or duplicated subscriptions cost the bank millions,” Olga recalls.
Revolutionizing Compliance
AI is reshaping how banks handle compliance by automating time-consuming tasks, especially in areas like Know Your Customer (KYC) — the process of checking who a customer is (collecting and verifying identity documents, understanding where their money comes from, and making sure they’re not involved in suspicious activities), and Anti-Money Laundering (AML). These tasks have traditionally involved a lot of manual work: going through paperwork, reviewing transactions, and running repeated background checks. Now, AI can take over much of this routine work, allowing compliance teams to spend more time on deeper risk analysis and strategic decisions. As a result, financial institutions can improve both their efficiency and their ability to detect fraud.
At a large financial institution, Olga pioneered the redesign of AI-driven KYC automation models, developing a novel framework that integrated intelligent risk scoring to dynamically adjust due diligence requirements. Her model automatically approved low-risk clients while flagging high-risk customers for enhanced scrutiny, reducing onboarding times by 40%, which is a significant breakthrough in compliance efficiency. Unlike traditional rule-based systems that applied uniform verification steps to all customers, this model leveraged machine learning to adapt risk assessments in real-time, minimizing unnecessary manual reviews while maintaining regulatory rigor.
Additionally, Olga led the introduction of AI-powered anomaly detection into AML monitoring, creating a first-of-its-kind system that continuously learns from evolving fraud patterns. Her innovation prioritized high-risk alerts, auto-cleared low-risk cases, and significantly reduced false positives, allowing investigators to focus on genuine threats rather than being bogged down by unnecessary manual reviews. These innovations led to about a 10% reduction in labor costs, optimizing compliance operations without compromising regulatory integrity.
“Many think that AI replaces people, but it’s not true—it frees them,” says Olga. “Instead of getting stuck in routine compliance checks, specialists can focus on uncovering deeper risks and strengthening the bank’s security framework.”
Beyond Cost-Cutting
While AI helps reduce expenses, its true power lies in revenue optimization. AI-powered predictive analytics allow financial institutions to analyze consumer behavior, personalize offerings, and enhance customer engagement. Tiger Brokers, for example, leveraged AI to refine its market insights, improving profitability in its trading services.
"Nobody wants to be bombarded with irrelevant banking offers," says Olga. "AI allows banks to shift from a one-size-fits-all approach to something far more personal—offering customers only what truly fits their needs."
And that is what she achieved while developing an AI-powered system for a major Russian retail bank to help them better understand and serve their customers. Under Olga’s leadership, the system used smart analytics to predict the most relevant financial products — such as loans, savings plans, or investment options — for each customer based on their behavior and transaction history.
Instead of relying on generic promotions, the system allowed the bank to deliver personalized, real-time recommendations, improving customer experience while maximizing sales efficiency. The AI-driven approach resulted in a 25–30% increase in conversion rates for client offers, doubled response rates to marketing campaigns compared to mass promotions, and significantly reduced acquisition costs. Additionally, improved customer targeting led to higher cross-sell opportunities, a 15% increase in customer retention, and overall revenue growth in the retail banking segment.
This AI-powered transformation set a new benchmark for personalized banking, with the model later being expanded across other product lines and customer segments, further driving efficiency and profitability.
The Future of AI in Banking
The next phase of AI-driven financial optimization will go beyond automation, integrating self-learning algorithms that proactively adjust spending and workforce structures. Generative AI and deep learning models will soon predict cost trends, optimize procurement, and autonomously manage financial operations.
With over a decade of experience at the intersection of finance, technology, and AI, Olga remains committed to driving innovation in banking cost optimization. She believes AI will not only make financial institutions leaner but also more resilient.
“The future of AI in finance will make institutions stronger, more precise, and ready for anything. The banks that embrace this shift will lead the next financial revolution,” Olga claims.
