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Banking and Financial Services

Get closer to customers, drive operational efficiency and enable employees.

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Digitization initiatives are focused on fundamentally changing the entire banking technology stack, including reimagining the business with a customer-centric lens. Collaboration with external partners such as fintechs, advisors, third-party developers and other technology partners allows banks to join new value chains to create a banking ecosystem. If implemented correctly, this can wholly redefine how customers use banks to drive even greater value.

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Banking operations for a customer-obsessed industry

Let us help you find and implement the right technologies to improve your customer experience, optimize data and transform your business model.

Enhance the customer experience

Design the contact center of the future
Simplify and automate processes
Implement Agile product development & measure its impact
Design & implement the right Op. Model to drive speed to market
Design & implement the right CX strategy

Improve efficiency and leverage third parties

Modernize application & cloud strategy
Design next-generation sourcing & supplier ecosystem models
Monetize assets
Baseline key technology metrics against the market
Assess, recommend and implement automation

Increase employee
productivity

Measure, benchmark & track employee experience
Design & implement new ways of working
Assess, recommend & implement the right collaboration tools
Training as a Service
Measure, benchmark & track transformation

The market has moved from ambition to accountability.

AI investment is accelerating, but results remain uneven. Only one in four initiatives is meeting revenue impact expectations, at an average spend of $1.3M per use case. Enterprises are no longer asking whether AI works. They are being asked to prove that it pays.

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What We Deliver

AI strategy, governance and intelligence, built for execution.

Autonomous Enterprise

Operations built for autonomous execution, not retrofitted for it.

We help you identify where AI agents deliver the most value, restructure workflows around them and build the accountability models that keep autonomous execution auditable. The enterprises that win won't be the ones that reacted. They'll be the ones that designed for it first.

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Autonomy-Level Pricing

Pricing that reflects how AI-enabled services are actually delivered.

We give enterprises transparent, benchmarkable pricing models that tag each resource unit with the autonomy level used to deliver it. As AI capability advances, your pricing keeps pace. Both buyers and providers can quantify what that progress is worth.

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AI & Software Intelligence

Build-versus-buy decisions grounded in what AI is actually delivering.

We bring analysis of more than $2.6 billion in tracked AI spend to every sourcing decision. Procurement, technology and finance leaders get the independent intelligence to rationalize vendor portfolios and hold providers accountable to measurable outcomes.

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AI Governance

Governance that accelerates AI adoption rather than constraining it.

We embed controls at the point of data creation, define accountability for autonomous actions and build adaptive frameworks that keep pace with AI without impeding it. Enterprises that get this right don't just manage risk. They build the trust that lets them scale faster.

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AI Strategy

AI investment aligned to where impact is most achievable.

We ground strategy in research across 2,400 enterprise use cases, aligning investment to where impact is proven and designing the data, talent and governance foundations that move AI from pilots into the workflows that drive commercial results.

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AI Maturity Index

A clear view of where you stand and a roadmap to where AI starts delivering.

We benchmark your AI readiness against peers across 75 countries, identify the dimensions holding you back and give you a personalized roadmap to close the gap.

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The market today

Enterprise AI has moved out of IT and into the revenue line.

AI investment is shifting decisively toward revenue-generating functions. CRM automation, sales enablement and forecasting have replaced chatbots and IT productivity tools as the leading use case priorities, reflecting enterprise recognition that productivity gains alone do not satisfy board-level scrutiny. At the same time, use cases in production have doubled since 2024, and the portfolio is diversifying rapidly, with over 300 distinct function and industry-specific use cases now in active deployment.

ISG research across 2,400 enterprise use cases shows that the strongest AI returns are currently concentrated in compliance, risk management and quality control, not in the growth and cost outcomes most enterprises originally set out to achieve

The gap between where enterprises are investing and where AI is actually delivering is the defining commercial tension of 2025. Organizations that close it by targeting functions with structured, revenue-attributable data and clear ROI measures will establish performance benchmarks that compress the window for competitors still cycling through pilots. The standard is being set now.

Where enterprises are feeling the pressure
  • Business outcomes are lagging AI ambition
    Enterprises are scaling Al faster than they are realizing value from it. The number of use cases in production doubled between 2024 and 2025, yet only one in four initiatives is meeting revenue impact expectations, and broad cost savings remain elusive. At an average spend of $1.3M per use case, the ROI gap is sharpening board-level scrutiny and forcing a harder question: are we building Al for impact, or for activity?
  • Data infrastructure exposing deferred investment
    Al does fail in isolation. It fails on the foundations beneath it. Most enterprises are running modern Al on architectures built for reporting and compliance. Generative and agentic Al demand real-time contextually rich, governed data at the point of use. Without it, pilots stall and value dissipate before it reaches the business.
  • The barrier to scale is organizational, not technical
    Organizational readiness as the bigger constraint on Al adoption, not talent or tooling. Workflows haven't been redesigned. Decision rights haven't shifted. Enterprises that treat Al as a pure technology deployment, without investing in the human side of adoption, consistently report underwhelming ROI.
  • Agentic AI is outpacing governance
    As Al moves from generating outputs to executing tasks autonomously, the governance gap widens. Agentic Systems introduce a new class of risk that static compliance frameworks were never designed to catch. Governing what Al does, not just what it produces, is now a business-critical requirement.

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Trends Shaping the Financial Services Industry

Important Factors for Digital Success

Client Stories

Using RPA to Drive Efficiency & Improve Accuracy

Jul 20, 2021, 15:34
Developing automated processes in funds visibility and client performance reporting in an Asset Management firm who wanted to become self-sufficient in RPA.
Title : Using RPA to Drive Efficiency & Improve Accuracy
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A leading Asset Management firm wanted to begin their automation journey in order to drive increased reporting accuracy and improved funds visibility. Manual processes were prone to errors which could result in significant reputational damage and introducing new share classes was taking far too long.

But the client also needed to be sure that the chosen RPA tool (Blue Prism) would interact effectively with internal applications and other complex legacy systems.

Finally, the Asset Management firm wanted to better understand the benefits of automation versus the risks of implementation.

The ISG solution was a phased approach, recognizing the understandably cautious nature of the organization, and the complexity of their environment. Phase 1 was to help the client identify two candidate processes to test the accuracy and efficacy of RPA. To test accuracy, preparation and distribution of fund performance reports was selected, and to test efficacy, the funds visibility process was chosen.

ISG’s team of expert advisors designed the two automations based on the specific objectives for each, being careful to ensure all stakeholders were aligned and committed to the agreed outcomes.

ISG then provided RPA training, support and coaching for the client to drive towards automation self sufficiency.

  • Thanks to ISG’s proven approach, stakeholders (Line of Business, IT, Operations and Program Management Office) were all aligned and committed to the same goals
  • Versus the existing manual-heavy processes:
    • The funds visibility process case completion time was reduced by 20%, and associated risk reduced to zero
    • The faster visibility of new share classes (or changes to existing ones) also drove up revenue opportunities
    • The preparation and distribution of client performance process time was reduced by 93%, and associated risk reduced to zero
  • The client has become mostly self-sufficient in RPA
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  • Banking & Financial Services
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Featured Event

TechXchange: Banking, Financial Services & Insurance 2021

Watch the ISG TechXchange: Banking, Financial Services and Insurance replay to discover the trends that are defining the financial services industry today and in the future.

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Meet Our Banking and Financial Services Team