By Vilas Madan, Senior VP and Growth Leader of APAC for EXL
The banking industry has always been defined by its ability to adapt. It’s seen a shift from paper ledgers to digital systems and the rise of mobile banking, but in 2025, the industry is poised for its most profound transformation yet. Driven by the disruptive power of artificial intelligence, generative AI is rewriting the rulebook. It offers banks the chance to reimagine their very DNA, from how they engage with customers to how they safeguard compliance and scale operations.
Generative AI is creative, predictive, and adaptive, attributes that introduce as much complexity as opportunity. For an industry rooted in trust and precision, the challenge lies in embracing this new frontier without compromising core values. The banks that succeed will be the ones that leverage AI not merely to optimise, but to innovate in ways we have yet to fully comprehend.
Why Generative AI Is Set to Shake Up Banking
The Need for Innovation in a Saturated Industry
Generative AI is here to stay. This tech brings capabilities that go beyond automation, introducing intelligence that learns, reasons and adapts. In markets like Australia, banks, particularly the Big Four, have been early adopters of new technologies. However, the broader challenge lies in adopting AI as a core operating model to unlock its full potential.
Banks have historically leveraged AI for years, especially in areas such as fraud detection, underwriting, and marketing. Basic AI-powered chatbots, using keyword triggers and workflows, such as Commonwealth bank’s Ceba and Westpac’s Kasisto, have been used for years to handle queries, model credit risk and underwrite customers more effectively.
But generative AI is a paradigm shift. It has the potential to unlock entirely new possibilities, such as automating compliance controls, streamlining KYC processes, enhancing fraud identification and delivering hyper-personalised customer experiences at scale and with accuracy. Banks that successfully integrate generative AI will gain a competitive edge by delivering personalised services, improving operational efficiencies and mitigating risks.
Pressure to Compete in the Digital Economy
Similar industries, such as insurance, retail, and healthcare, are already leveraging AI to increase efficiency, reduce risk and drive revenue. AI-powered recommendation engines are helping retailers increase their share of customers’ wallets, while healthcare providers are using AI to triage critical cases.
While many Australian banks have already begun experimenting with and adopting AI, these efforts are often limited in application. The true challenge lies in incorporating AI into their core operating model to fully realise its potential and scale its impact. Market leaders are already doing this, positioning themselves to capitalise on AI-driven efficiencies and scale them.
The urgency stems to follow suit due to the foundational work required to scale AI effectively. AI starts with data transformation—a process that demands significant time, resources, and advanced talent, not yet readily available in the market. To remain competitive and future-ready, banks must act swiftly, embedding AI deeply into their strategies and ensuring they are equipped to handle the demands of a rapidly evolving digital economy.
Key AI Trends That Will Define Banking in 2025
1. Enhanced Customer Experience Through AI
Generative AI will continue to revolutionise customer interactions in banking. Banks will soon be able to anticipate your needs, providing personalised recommendations based on your financial goals. Generative AI can analyse vast amounts of customer data to identify preferences and predict future behaviors. This allows banks to present the “next best offer” or suggest tailored financial products in real-time.
We are already seeing early signs of this transformation. Conversational AI is replacing outdated IVR systems, making customer service more seamless and intuitive. But this is just the beginning. The future holds the promise of AI-driven customer-facing applications that can handle complex inquiries, provide instant resolutions, and offer proactive financial advice.
A key advancement in this space is sentiment analysis, which enables AI systems to detect customer emotions during interactions. This capability is particularly valuable in hardship situations, allowing AI-driven applications to adapt responses with empathy and relevance, ensuring a more supportive and humanised customer experience.
2. Risk Mitigation and Compliance at Scale
Compliance has traditionally been a challenging area for banks due to the complexity of regulations and the high stakes involved. Generative AI can automate compliance processes, enabling banks to move from sample-based testing to comprehensive, always-on monitoring. For example, AI can analyse 100% of a bank’s transactions for anomalies, flagging potential issues in real-time.
One exciting development is the concept of “proactive controls.” AI can transcribe and analyse marketing calls to ensure that promises made to customers align with actual product offerings. If discrepancies are detected, compliance triggers can be activated within hours, not weeks. Such advancements not only reduce compliance risks but also build trust with customers and regulators.
3. Operational Efficiency Gains
Generative AI is poised to drive efficiency across middle-office and back-office operations. Initially, banks will adopt AI for tasks such as document ingestion, KYC/AML investigations, and fraud detection. Over time, these capabilities will expand to include real-time assistance for customer-facing representatives, providing them with policy nudges, disclosures, and next-best-action recommendations.
One promising area is the use of generative AI to streamline onboarding processes. In fact, a recent US survey revealed over two-thirds of organisations have incorporated AI into their onboarding procedures. By automating document review and verification, banks can reduce processing times from days to hours, creating a far superior customer experience.
4. Talent Transformation in Banking
As generative AI becomes more prevalent, banks will need to rethink their talent strategies. Banks face an acute skills shortage, with enormous demand-side pressures on a tight talent pool. For example, the Australian Government recently mandated that all agencies must designate accountable officials for AI governance, further straining the supply of advanced AI expertise.Success will hinge on the ability to attract and retain not only AI experts but also data engineers who can operationalise AI solutions effectively.
Upskilling existing teams will also be critical to ensure employees can work alongside AI systems. To achieve this, banks must consider equipping teams with advanced tools like sentiment analysis. These tools can enhance customer interactions by detecting emotions during conversations and enabling personalised, empathetic responses, particularly in emotionally sensitive scenarios like hardship. This capability will be pivotal for improving both customer experience and loyalty.
Banks must also decide on the right organisational structure to support their AI agendas. Centralised, decentralised, or hybrid models will need to align with the bank’s size, complexity, and strategic goals.
How Banks Can Stay Ahead in the AI Race
1. Set a Clear AI Agenda
Banks must place AI at the core of their operating model, ensuring it drives their transformation agenda. Prioritising AI use cases that deliver maximum value with the greatest certainty of success is essential. EXL’s AI:OS™ (Artificial Intelligence Operating System) provides a structured framework to achieve this by embedding AI and cloud capabilities into business operations.
AI:OS enables banks to identify high-impact opportunities, align processes with analytics-driven insights, and accelerate transformation. By making AI central to operations, banks can unlock greater efficiency, enhance customer experiences, and ensure sustainable value creation.
2. Invest in Data and Infrastructure
Generative AI requires robust connected data ecosystems and advanced computational capabilities. Banks must make significant investments in data quality, governance, and infrastructure to support AI adoption. This includes preparing for the unique compute demands of generative AI models.
3. Strengthen Governance and Controls
AI systems are not without risks. To mitigate issues such as bias and hallucinations, banks need robust governance frameworks. Controls must be embedded at three critical stages: development, approval, and production. During the development stage, input data must be rigorously tested to ensure it is free from bias. Once deployed, AI models must be continuously monitored for accuracy and relevance.
4. Leverage Lessons from Other Industries
Banks can draw inspiration from industries that are further along in their AI journeys. For example, insurers are using AI to automate claims adjudication, while retailers are employing recommendation engines to drive cross-sell opportunities. These use cases highlight the potential for AI to deliver cost savings, improve risk management, and enhance customer engagement.
5. Roll Out Generative AI in Stages
Implementing generative AI across an entire organisation can be daunting, and banks must approach this transformation with a phased strategy to minimise risk and maximise adoption. Banks should focus on three key stages for AI rollout:
- Internal Efficiency: Use AI to automate middle-office and back-office functions, such as document processing and fraud investigations.
- Rep Assistance: Empower customer-facing representatives with AI-driven nudges and real-time recommendations.
- Customer Interactions: Build AI-powered applications that can engage directly with customers, providing instant resolutions and personalised advice.
By adopting this phased approach, banks can systematically scale their generative AI initiatives, ensuring a balance between innovation and risk management.
In 2025, You Can Lead or Be Left Behind
Banking is uniquely positioned to be reshaped by generative AI. The opportunity to revolutionise customer experiences, streamline operations, and drive innovation is immense, but so is the urgency to act. Banks that delay risk irrelevance in an increasingly competitive landscape. Success will require bold leadership, strategic investments and partnerships, and a commitment to innovation.
Those that lead this transformation will define the next era of banking, setting new standards for trust, efficiency, and value, emerging as leaders in tomorrow’s AI-driven financial ecosystem.
