AI is no longer a future bet in banking, financial services, and insurance (BFSI) — it is reshaping the industry today. McKinsey & Company estimates that Gen AI alone can generate an additional $200–$340 billion in annual banking value, equivalent to 9–15% of operating profit [1]. These gains come from smarter decisions, streamlined operations, and redesigned processes that elevate customer and employee experience.

The implication is clear, and the path is actionable: focus on value-backed use cases, align risk and compliance early, and enable adoption with data readiness and change management. However, technology is only part of the story; operating model, governance, and talent determine whether pilots scale and therefore whether impact enduresSummary

The article examines AI’s expanding role in banking, financial services, and insurance, noting material value creation and practical use cases already delivering results. It outlines how institutions can capture benefits by embedding AI with speed, trust, and domain insight, while balancing governance, risk, and execution discipline.

Key Points:

  • Defines AI’s purpose in banking, financial services, and insurance as modernizing processes, decisions, and engagement.
  • Highlights business value through efficiency gains, revenue growth, faster benefit realization, and improved risk outcomes.
  • Emphasizes winning approach by embedding AI with speed, trusted governance, domain expertise, and scalable execution.
  • Discusses challenges and opportunities including regulatory expectations, talent readiness, change adoption, and broadening enterprise impact.

CONTENT

  • Where is AI working?
  • How to make it work?
  • Conclusion

Where is AI working?

AI is already delivering measurable impact across financial services. Banks and insurers are shortening turnaround times, elevating accuracy, and unlocking always-on experiences. By combining OCR, rule-based automation, and predictive models, organizations streamline loans, customer service, portfolios, and claims, and therefore reduce costs, mitigate fraud, and in several cases outperform human-only approaches.

To make the landscape easier to grasp, the domains below are organized by common financial-service functions where high-impact, real-world results have been reported.

  • Loan processing: AI is reshaping loan origination by automating document checks and accelerating decisions, and it turns multi-day approvals into same-day outcomes. Optical Character Recognition (OCR) scans and extracts data from applications to minimize manual collection and human error. Algorithms then apply pre-defined rules and predictive models to approve or reject requests and suggest suitable loan options, thereby elevating speed and consistency.
    • Typical automations: document verification, OCR-based data extraction, rule-based decisions, and personalized option recommendations.
    • Illustrative results: a top international bank co-developed an AI-powered digital lending platform with FPT and accelerated processing from 6 days to under 24 hours. The platform’s virtual assistant provides product information, compares products, and recommends options based on customer preferences for a paperless experience.
  • Customer service: AI chatbots now handle routine queries—such as balance checks and transfers in banking, or policy review and claims support in insurance—and they do so with human-like interactions. Unlike traditional, scripted bots, AI virtual assistants complete more complex tasks with higher accuracy, and they operate 24/7, which improves satisfaction while freeing agents for nuanced cases. This combination enhances efficiency and consistency across channels. o Self-service at scale: an Asian insurance group launched a customer super app featuring an integrated AI chatbot for policy review, claims submission, and health monitoring, enabling resolution of 87% of customer queries on the first call.
  • Portfolio management: With the ability to analyze vast, unstructured data, AI surfaces patterns and anomalies that humans may miss, and it adjusts strategies earlier in the signal chain. By correlating non-obvious variables with future performance, models can support or even exceed human analyst outcomes. Evidence suggests material outperformance in simulated head-to-head tests.
    • Study highlights: Stanford researchers trained an AI analyst on market data (1980–1990) to correlate 170 variables, then asked it to assess and quarterly adjust ~3,300 actively managed portfolios (1990–2020). The model generated $17.1M per quarter of alpha, versus fund managers’ $2.8M, indicating AI outperformed 93% of managers by ~600% on average.
  • Claims process (insurance): AI is enabling end-to-end claims transformation, and executives are confident in automation, AI, and ML-based analytics to improve fairness and speed. OCR ingests applications and documents with fewer errors, while rules-based engines standardize approvals and rejections. Algorithms also flag anomalies and fraud, which is challenging at scale for human assessors, and this ultimately reduces costs and leakage.
  • Process enhancements: OCR-driven intake, automated decisioning with pre-defined rules, and AI-based fraud detection.

How to make it work?

To make AI work in financial services, align initiatives with business priorities, invest in people and change, tailor solutions to each regulatory and market context, and demand explainability throughout. These principles, informed by FPT’s work with global institutions, help translate bold AI visions into measurable, trusted outcomes.

As AI matures, the winners will be those who embed it thoughtfully, combining speed, trust, and domain understanding. Drawing on FPT’s experience supporting global financial institutions, four lessons stand out for turning ambition into impact, and they build on one another to guide strategy, delivery, and governance.

1. AI works best when tied to business needs—not hype:

The possibilities of AI are exhilarating; however, they can be disorienting. A survey by Ernst & Young LLP reveals that 99% of financial-services leaders say their organizations are deploying AI, yet 20% lack confidence in capturing its potential [3]. Barriers include missing digital roadmaps, legacy systems, and weak data and technology foundations. Therefore, anchor AI to core business goals and the broader transformation agenda.

2. Change management and training are critical:

Fully embedding AI in culture and day-to-day work requires more than technology; it demands new skills and mindsets, and resistance is predictable. With 75% of employees fearing that AI could eliminate certain jobs [4], leaders must invest in comprehensive training and change management. Set clear goals, define implementation roadmaps, and communicate openly and consistently to build confidence.

3. One-size-fits-all doesn’t work—context matters:

Regulatory environments, customer segments, and technology footprints differ by market, and thus strategies must be context-specific. Even within multinationals, local branches should adapt plans to fit market dynamics and compliance. For example, AI-driven credit scoring must respect privacy regimes: the EU tightly restricts non-essential personal data, while the US permits broader use. Consequently, banks like HSBC and Bank of America build region-specific AI variants at 40–60% higher cost than a single standardized model [5].

4. Explainability is non-negotiable in regulated environments:

In regulated environments, explainability is non-negotiable, and performance trade-offs are real. In the UK, 63% of banks struggle to meet explainability standards without sacrificing model performance [5]. Meanwhile, the regulatory landscape keeps shifting—regulatory changes have surged by 500% since 2008 [6]. Institutions must monitor AI solutions continuously and update controls to stay strictly aligned with changing rules.

Conclusion

AI in financial services has moved from promise to proof, delivering measurable gains in risk, operations, and customer experience. The differentiator now is disciplined execution—pairing rapid experimentation with robust governance and deep domain insight.

Start with high-value use cases, modernize data foundations, and build trust through transparency, controls, and human oversight. Institutions that scale responsibly will capture durable advantage while meeting rising expectations from regulators and customers.

Key Takeaways:

  • Prioritize high-impact, measurable use cases.
  • Strengthen data quality, access, and governance.
  • Embed model risk, compliance, and transparency by design.
  • Form cross-functional teams and iterate toward scale.

Frequently Asked Questions

What is the financial impact and value of AI in the banking sector?
McKinsey estimates that Gen AI alone can generate $200-340 billion annually for banking (9-15% of operating profit). AI drives significant cost reductions through automation and improved efficiency across financial operations.

What are the best practices for successful AI implementation in financial institutions?

Successful AI implementation requires combining speed with trust, deep domain understanding, thoughtful embedding of AI solutions, and learning from proven experiences of global financial institutions that have achieved tangible impacts.

What are the most successful AI use cases and applications in banking today?

AI shows high-impact results in loan processing, risk assessment, fraud detection, customer service automation, and regulatory compliance. These applications demonstrate tangible transformation potential with measurable business outcomes.

Author Balamurugan Jegatheesan