A recent report released by PwC on March 17, titled "AI-Driven Renewal and Upgrade for the Financial Services Industry," highlights that artificial intelligence is now viewed as a key driver for business transformation by financial institutions, rather than merely a tool for efficiency gains. The survey, completed in January 2026, covered 201 banks, insurance companies, and asset management firms, and included in-depth interviews with 20 industry executives.

The report identifies core application scenarios for AI in the financial sector, spanning areas such as customer service optimization, fraud risk detection, and predictive analytics. Across banking, insurance, and asset management, the vast majority of respondents positioned AI as a central engine for strategic transformation.

Strategic importance has been widely recognized, yet investment levels have not kept pace. According to the report, 76% of financial institutions plan to leverage AI for business transformation and to create new revenue streams. Specifically, 41% regard AI as a strategic transformation engine, while 35% see it as a foundation for new value creation. Only 15% still treat AI purely as a cost-reduction tool.

Wang Jianping, Management Consulting Partner at PwC China, noted that financial institutions have high expectations for AI's potential to empower business. In their view, AI's value extends beyond operational efficiency to reshaping business models for an AI-native era, reinventing service experiences, and fostering innovative business models—an opportunity too significant to miss.

For instance, a senior executive from a Hong Kong-based bank stated, "We are not only pursuing efficiency gains through AI but also aiming to pioneer new value propositions and business models that do not yet exist in the market."

However, this strategic emphasis has not been fully matched by financial commitment. The report indicates that 61% of financial institutions allocate less than 10% of their overall technology budget to AI-related initiatives, resulting in a 30% to 40% funding gap in AI investment relative to technology spending.

Wang pointed out that institutions that have invested in AI have already achieved initial returns of 10% to 15%, mainly through reduced risk losses, improved compliance efficiency, revenue growth, and cost savings. While short-term gains are valued, institutions are also placing importance on AI's long-term benefits, such as enhancing market position, expanding strategic development space, and identifying new growth opportunities. The key challenge, however, remains whether current AI investment levels are sufficient.

AI adoption is flourishing across multiple core business areas, with human-machine collaboration becoming the dominant trend. Despite investment gaps, focused application in key scenarios is already yielding quantifiable returns and has quickly become a priority in enterprise-level AI deployment. The survey shows that customer service and chatbot implementation represent the most common AI application at 31%, followed by investment and asset management at 28%. Fraud detection, predictive analytics and modeling, and back-office process automation also play significant roles, at 24%, 23%, and 19%, respectively.

Notably, 57% of financial institutions reported that they are using AI to enhance both existing and new employee functions, indicating a preference for augmenting human capabilities rather than replacing staff.

Ni Qing, Asset and Wealth Management Industry Leader for PwC Mainland China, explained that different sectors emphasize distinct AI applications. Banks focus on risk control, anti-money laundering, and compliance; insurers prioritize agent capability enhancement, customer service, and claims processing; while asset and wealth managers apply AI to investment and portfolio management, as well as data and market analysis.

Meanwhile, applying AI under controlled risk conditions has become an industry consensus. In response to questions about balancing efficiency and safety, Chen Yan, Risk and Regulatory Management Consulting Partner at PwC, emphasized that beyond input and output considerations, financial institutions must prioritize AI governance. She likened AI governance to a "brake system"—without effective brakes, institutions cannot accelerate fully. Proper checks and balances are essential to ensure that the "AI race car" can navigate turns smoothly and sprint on straightaways.

Specifically, financial institutions should establish dedicated AI governance committees to ensure consensus at the management level. These committees should conduct comprehensive AI inventories, understand AI tools used across departments, strengthen risk awareness training, rationally control the pace of AI investment, avoid siloed development patterns seen in past IT projects, and implement strict access mechanisms to assess the maturity and risks of new AI technologies.

Talent and culture remain the biggest bottlenecks, while data governance requires urgent improvement. The report also highlights that large-scale AI adoption faces multiple constraints, with talent shortages and rigid organizational structures being the most significant barriers—far exceeding issues related to budget or technology.

Li Weibin, Management Consulting Partner at PwC China, stated, "A common challenge reported by respondents is the difficulty in recruiting interdisciplinary professionals who understand both business and algorithms. Training and upskilling existing employees, along with creating incentives that encourage AI as a transformation tool, are critical to building an AI-first culture. Equally important is senior leadership’s role in championing AI adoption."

Only 29% of financial institutions reported having successfully fostered an "AI-first" culture. It is worth noting that successful AI implementation relies not only on technical capability but also on cultural transformation. Traditional processes and functional silos continue to hinder the broader adoption of AI.

Beyond talent and culture, data remains a key constraint. Respondents identified the top three factors influencing AI budget allocation as data availability (30%), regulatory pressure (20%), and the need to maintain existing core systems (14%). Data security and privacy protection were cited as the foremost challenges in data management, leading 90% of financial institutions to rely on internal proprietary data to support their AI applications.

The report suggests that establishing a "regulatory sandbox plus federated learning" mechanism may enable cross-institutional value exchange within regulatory boundaries, offering a potential solution. A professor from a Hong Kong university also noted that financial institutions that maintain dialogue with regulators and actively explore sandbox solutions are likely to gain a first-mover advantage in large-scale AI adoption.

Looking ahead to the next five years, financial institutions anticipate fundamental reshaping of business models, driven by four major trends:

First, a shift from standardized products to AI-powered dynamic, real-time service models enabling hyper-personalization. AI will analyze customer behavior, preferences, and needs in real time, dynamically adjusting product recommendations and service solutions.

Second, AI will take on greater decision-making authority, acting as a super-collaborator with humans to advance highly automated and optimized decision-making. Routine decisions will increasingly be handled by AI, freeing human employees to focus on complex judgment, creative tasks, and customer relationship management.

Third, compliance management will evolve from reactive responses to embedded, real-time, proactive intelligent compliance. Future AI systems will monitor and alert in real time within business processes, integrating compliance requirements into every transaction.

Fourth, risk control will transition from traditional post-event analysis to real-time intervention and forward-looking prediction. By using machine learning models to analyze vast datasets in real time, financial institutions will be able to identify potential risks earlier.

To support effective AI implementation, the survey found that financial institutions are actively advancing four key initiatives: strengthening foundational data infrastructure and hybrid cloud architecture; accelerating talent development through large-scale AI upskilling programs; enhancing ecosystem collaboration with AI startups, fintech firms, and research institutions; and building robust risk protection systems with forward-looking AI governance frameworks that address explainability, algorithmic bias, data privacy, and ethical concerns.