Financial Services
AI-First Operating Models in Banking: Designing for Scale, Speed, and Resilience
Banking Models in the Age of AI
The financial services industry has reached a tipping point. With AI capabilities maturing rapidly and customer expectations evolving in real-time, banks are being forced to look beyond digital transformation. The shift is now toward building an AI-first operating model—one that integrates intelligence across the value chain, automate at scale, and enable hyper-personalisation.
Scaling AI in banking requires rethinking how banks operate, how decisions are made, and how technology, people, and processes come together. The stakes are high: McKinsey estimates that GenAI alone could add up to $340 billion in annual value for the banking sector. The institutions that build AI-first capabilities today will define the next decade of growth and resilience in banking.
What an AI-First Operating Model Looks Like
AI-first banks reimagine their core through four strategic levers:
- Proactive risk and fraud management
- Hyper-personalised customer engagement
- Smart lending and credit decisioning
- Product innovation and targeted marketing
Fraud detection has shifted from reactive checks to real-time intervention. Modern AI systems scan thousands of data points per second—flagging anomalies, unusual behaviors, and transaction patterns.
This is critical, especially as INTERPOL flags a global spike in financial cybercrime. AI brings agility to fraud prevention by learning continuously, adapting to new attack vectors, and mitigating threats before they escalate.
For banks, this shift reduces risk exposure and strengthens customer trust. For regulators, it demonstrates operational maturity and control.
A key aspect of the AI-first model is the ability to deliver intelligent, context-aware interactions across the customer lifecycle.
Today’s customers expect more than 24/7 access—they expect advice, recommendations, and support tailored to their unique financial journeys. AI-powered virtual assistants and digital advisors are meeting this need by offering real-time insights, proactive nudges, and contextual offers.
This not only improves CX but also optimises cost-to-serve by shifting routine queries away from human agents—freeing up frontline staff for higher-value conversations.
Traditional loan processes often relied on narrow credit score bands and static document checks—missing out on borrowers with alternative credit histories or nuanced risk profiles.
AI-first banks use intelligent underwriting engines that tap into a broader data universe: income patterns, spending behaviour, digital footprints, and even alternative data sources.
The result is faster approvals, more inclusive lending, and smarter risk assessment. This precision benefits both sides: customers get tailored credit options, and banks reduce default risks while expanding portfolios.
Customer needs are dynamic, and so should product design be. AI enables banks to move from one-size-fits-all offerings to targeted, needs-based solutions—whether that’s a new savings product for young professionals or custom wealth advisory for mid-career investors.
AI-powered marketing engines also shift from demographic-based targeting to real-time, behaviour-driven engagement—resulting in higher conversion rates and stronger lifetime value.
Addressing Organisational and Ethical Considerations
While the benefits of AI in banking are clear, the transformation cannot succeed without addressing three fundamental risks:
- Workforce displacement and role redesign
- Data privacy and security
- Bias and fairness in algorithms
As automation takes over repetitive tasks, new skills will be needed across customer service, risk, compliance, and technology. Banks must invest in reskilling and redesign job roles to empower employees to work with AI, not against it.
As AI systems become more data-hungry, ensuring data protection is non-negotiable. Robust governance frameworks must be in place to manage customer consent, ensure transparency, and guard against breaches. Cybersecurity must evolve alongside AI to secure data pipelines and decision systems.
Algorithms reflect the data they are trained on. Left unchecked, they may amplify bias or reinforce exclusion. AI-first banks must implement rigorous validation frameworks and ethical AI governance—ensuring fairness in lending, marketing, and decision-making.
As Jim Marous puts it, customer-centricity is now a survival metric. Fairness and explainability are not optional—they are foundational to trust in an AI-driven future.
Design Principles for AI-First Banking
To successfully embed AI into the fabric of banking operations, leaders must adopt a few core design principles:
- Embed AI in core operations, not just front-end functions
- Modular, scalable architecture
- Data as an Enterprise Asset
- Human-AI collaboration at the core
Moving beyond isolated pilots to integrated AI capabilities across lending, compliance, finance, and customer service ensures sustained value.
AI-first operating models demand composable technology such as cloud-native, API-first, and scalable across lines of business.
Fragmented data blocks transformation. Banks need enterprise-wide data lakes and real-time data streaming to support intelligent automation and decisioning.
AI augments, not replaces. Designing workflows where humans validate, train, and intervene ensures balance, especially in regulated environments.
Looking Ahead: What’s Next in AI-First Banking?
Several forward trends are shaping how the next wave of AI-first banks will operate:
- Voice as the new interface: From mobile-first to voice-first banking. Expect AI-powered voice interfaces for tasks like fund transfers, queries, and financial advice.
- AI co-pilots for bankers: GenAI will soon assist relationship managers with contextual insights, compliance-ready content, and portfolio recommendations.
- Explainable AI for compliance: As regulators demand greater algorithmic transparency, explainable AI will become critical in audit trails, decision rationales, and fairness reviews.
Building a Future-Ready Bank
For banks, the move to AI-first isn’t a technology upgrade—it’s a fundamental operating shift. It changes how decisions are made, how services are delivered, and how value is created.
But speed alone won’t guarantee success. Responsible design, scalable execution, and human alignment are equally essential.
Infosys BPM’s financial services enable financial institutions to build AI-first operating models that are resilient, scalable, and ethical. From intelligent process automation to AI-led customer engagement, our solutions help businesses achieve next-gen efficiencies and create lasting value.
