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AI in financial services is moving from experimentation to execution. For banks and insurers, the biggest challenge is no longer proving that AI can work; it's deploying AI inside regulated, high-stakes operations with the governance, auditability, explainability, and human oversight required for production.
This article defines the last mile of AI in financial services: the execution gap between building AI models and running them safely, reliably, and at scale in live banking and insurance workflows. It explains how enterprise debts across data, process, technology, and talent slow AI adoption; where the last mile appears in banking and insurance; and how financial services leaders can close the gap by combining domain expertise, operational readiness, governance-first design, and measurable business outcomes.
The demo always works. The model performs. The pilot impresses the board. Everyone leaves the room convinced. Then the technology meets live banking or insurance operations – real customers, real regulations, real risks – and it stalls.
Most financial institutions are stuck in the same place: the distance between what AI can do and the value it delivers in day-to-day operations.
Banks and insurance companies today know where AI can create value. Over the past two years, they've launched programs, modernized platforms, and invested in data. Their challenge now is making AI deliver.
At Genpact, we call this operational reality the last mile.
What the last mile really means
Financial services firms have become very good at building AI models. They're still learning how to run them inside regulated businesses. That gap – between building and running, between the lab and the ledger – is the last mile.
Most AI strategies are written for the first nine-tenths of this journey – the model, the platform, the proof of concept. Almost none are written for the last one.
We can define it because we live in it. Genpact runs mission-critical banking and insurance operations for leading financial institutions across millions of transactions and thousands of decisions every day. We have deep experience observing where AI breaks down – and exactly what it takes to make it hold.
In banking, the last mile is felt most acutely in financial crime, compliance, customer experience, and operating models under pressure. In insurance, it shows up in underwriting, claims, finance modernization, and rising exposure.
Same execution problem. Different symptoms.
The hidden barriers slowing progress
Building and training a strong model is genuinely hard work, and most institutions have gotten much better at it. But in our experience, the model itself is rarely what determines whether AI creates value in production.
The real drag is complexity buried inside every large enterprise: fragmented data, disconnected processes, aging systems, and talent models built for another era. Genpact's research with HFS identifies four enterprise debts: data, process, technology, and talent.
These debts compound. They slow decision-making, increase risk, and make scaling AI far harder than deploying it. Our research found that the toll is enormous: $1.4 trillion in unrealized value in banking and $1.1 trillion in insurance, with 88% of banking and capital markets leaders and 83% of insurance leaders feeling the weight of these enterprise debts today.
Closing the gap in practice
In banking, the last mile is most visible in financial crime and compliance, where transaction monitoring teams face growing volumes of alerts, evolving typologies, and increasing regulatory scrutiny. Legacy processes and fragmented workflows make it difficult to investigate alerts efficiently while maintaining the transparency and auditability regulators expect.
This is where governance-first execution matters as much as model performance, turning a strong model into something regulators, auditors, and customers can trust. It's what Genpact's Banking Analyst Suite and riskCanvas were built for. Their agentic AI capabilities help financial institutions modernize transaction monitoring and fraud workflows, combining AI-driven analysis with auditability and human oversight designed in from the start.
The proof is in the outcome. Everest Group named Genpact a Leader in banking operations in 2025 and in financial crime and compliance for five years running.
In insurance, the last mile is most visible where fragmented workflows, rising risk exposure, and margin pressure converge. Underwriters still spend up to 40% of their time on administration rather than judgment, costing the industry an estimated $17 billion to $32 billion a year. Submission-to-quote takes 8 days on average, with 12 more days before bind. And nearly half of underwriters say their pricing models are unfit for purpose.
That's why Genpact's Insurance Policy Suite is built domain-first. Its agentic AI capabilities are informed by underwriting workflows and exceptions, automating pre-bind from submission through bind, with explainability built in. In production, that's meant to deliver up to 75% less cycle time at bind and up to 90% touchless submission processing.
Analyst recognition from NelsonHall, HFS, and ISG across claims, underwriting, and core operations reinforces the broader insurance story.
Two industries, one common challenge: the difficulty of connecting people, process, data, and AI inside regulated environments where accuracy and trust can't be compromised.
Where AI meets operational reality
Real operations bring different demands. Regulatory scrutiny. Governance. Legacy dependencies. And an endless stream of exceptions.
This is where most initiatives lose momentum. A model that tested well now has to support an underwriting decision, investigate a financial crime alert, or resolve a customer dispute. Accuracy alone isn't enough. The output has to be explainable, auditable, resilient, and trusted.
This is where the real work begins. The leaders who get this right share these four key habits:
- They build governance in from day one
- They take operational readiness as seriously as technical readiness
- They measure success in business outcomes – productivity, experience, growth – not the number of pilots launched
- They treat AI as something to be owned, not switched on. Value comes from running it well, long after go-live
The last-mile advantage
For decades, Genpact has operated inside the banking and insurance work where compliance, accuracy, and performance are measured daily.
These habits show up clearly in how we work with clients. Genpact's last-mile advantage has three layers:
The operation:
Deep experience running complex financial services operations at scale. We helped a leading financial technology company with a 60% reduction in the time required to resolve each case and a 45% reduction in false positives.
At a US-based fixed annuity provider, running the build and operations directly let us stand up a cloud-native annuity business and take it to market in just 10 months – on 80% of the planned budget, with market response beating targets by more than 25%.
The context:
Process, data, and regulatory know-how built across millions of decisions. At a progressive financial services innovator, we rolled out Genpact's Transaction Monitoring Analyst to help handle Servicemembers Civil Relief Act (SCRA) workflows, dropping end-to-end case processing time by approximately 75%.
At a global reinsurer, understanding how submissions and risk actually flow let us build a risk evaluation solution powered by AI and machine learning. This solution responded to a third of ceding‑carrier requests within two hours and created the potential to lift top-line revenue by 12% to 15% per year, with sharper underwriter productivity.
The outcomes:
A leading Australian wealth management company experienced a 40% reduction in the cost of compliance, freeing up resources for strategic initiatives while maintaining high standards of risk governance.
The new battleground
The next generation of financial services leaders won't be defined by access to AI. The technology is everywhere now.
What separates leaders from followers is the ability to embed AI into live operations – sharpening decisions, earning trust, meeting regulatory requirements, and delivering at scale. The winners won't be defined by the boldest strategies. They'll be the ones who close the execution gap and turn AI into measurable outcomes.
