Industry
Banking
Banking Services Transformation: Unlocking Exponential Value
Highlights
- Over the last few years, BFSI firms have invested substantially in AI transformations and realized value. However, many AI initiatives fail to go into production.
- Inadequacies in architecture, governance, talent, and adoption as well as the nature of financial data are the cause of this failure. For BFSI firms, identifying the right mix of people, technology, data, and domain is critical to convert PoC value into long-term AI RoI.
- Banks and insurers must embrace a four-pillar framework to align organizational strengths across people, data, technology, and domain with the functioning of AI systems to increase AI RoI.
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AI adoption in BFSI
In the last decade, the banking, financial services, and insurance (BFSI) industry has become adept at using NextGen artificial intelligence (AI) technologies to extract insights from voluminous and complex data.
BFSI firms have invested heavily in AI-driven use cases and registered some early wins. However, many initiatives stall before reaching production-grade maturity largely due to gaps in architecture, governance, talent, and adoption, which is further compounded by the deeply domain-centric nature of financial data.
For BFSI executives, AI investment decisions hinge on four pillars: people, technology, data, and domain. The right mix of these ingredients determines whether prototype success will translate into positive returns on investment (RoI). However, use cases with the most successful or popular prototypes are not necessarily the most RoI-positive. Figure 1 maps BFSI use cases across four quadrants, evaluating each use case from the lens of business value and level of adoption, highlighting how popularity and value do not always align.
Figure 1
Mapping BFSI use cases across four quadrants
Figure 1
Let us examine a couple of use cases that are favored by the BFSI industry.
Contact center falls in quadrant 2: high adoption, low value, but has a much higher value potential. While the prototype can be ready in six months, scaling adoption and business RoI realization require tight coupling with customer relationship management (CRM) systems, robust systemic guardrails, exception handling, platform integration, and comprehensive ongoing maintenance. Firms that meet these demands can scale the same use case to quadrant 4: high adoption, high value, and realize substantial efficiency gains within 12-18 months.
Knowledge graph falls in quadrant 2: high adoption, low value. It is relatively easy to establish initial value by building a prototype using the community editions of various mature platforms such as TigerGraph and Neo4j. Integrating disparate data sources spread across the enterprise using graph databases helps establish the efficacy of using graphical constructs for complex multi-step queries. However, building production-grade knowledge graphs needs significant investment in entity resolution and ontology design as well as a scalable setup that will be cost- and time-efficient as the data scales. Data science and big data engineering expertise in knowledge graph and related technologies is also in short supply. Yet, careful planning and systematic execution of knowledge graph infrastructure can yield tangible long term value across a multitude of real world applications such as fraud detection where mapping relationships across multiple hops and identifying anomalies matter.
In addition to choosing the right use cases for AI adoption, BFSI firms must keep in mind that AI transformations differ fundamentally from past technology shifts. Traditional technology transformations involve low-stakes infrastructure upgrades with predictable outcomes. By contrast, AI transformations often automate parts of decision-making. Thus, a potentially high-value use case runs the risk of not advancing beyond the proof of concept (PoC) stage if the right design principles and operating model are not incorporated.
- A global bank used service and transaction data to develop an AI model to accurately predict customer churn. Yet, privacy concerns and lack of systematic integration with CRM systems led to delays in rolling it into production.
- An insurer built an AI-backed claims triage system which showed a 30% efficiency gain in testing. However, it could not be pushed into production due to regulatory issues around explainability.
These are not isolated instances, but a recurring theme: even technically successful PoCs often fail to scale due to operational, design, regulatory, and integration challenges, resulting in suboptimal returns on AI investments. This raises a key question: can a unified, objective framework, grounded in science and aligned with measurable key performance indicators (KPIs), be built to guide CXOs in AI investment decisions and link AI transformation efforts directly to RoI?
Designing a practical framework centered on measurable outcomes will necessitate synergizing AI with an organization's core strengths: people, technology, data, and domain. To build on prototype success and realize business RoI from AI, we recommend a four-pillar framework that aligns transformation efforts across these four dimensions with business value.
PoC value to RoI
In our view, successful AI transformations demand a particular alignment of organizational strengths across people, technology, data, and domain that can translate experimental success into enterprise-scale AI RoI.
These four interconnected dimensions form the foundation of scalable AI, each addressing unique but related challenges.
People are central to realizing AI RoI, both as enablers and end-users. BFSI firms face two key challenges: reskilling traditional roles to work with AI and scaling talent without creating silos. Many firms struggle to align quants, data scientists, and business experts, leading to PoCs such as alpha research, underwriting, and campaign models that cannot be scaled. As a result, AI is relegated to side initiatives, disconnected from mainstream operations.
To overcome these barriers, BFSI firms must shift from the ‘AI-as-a-project’ mindset and view it as an embedded capability. Achieving this will require:
- Creating hybrid roles that bridge business and technology.
- Launching company-wide AI literacy programs.
- Ensuring the entire workforce is AI-aware and value-focused.
- Embedding AI within business units, backed by shared governance and reusable tools.
- Shifting performance metrics from pilot and/or PoC completion to tangible business KPIs such as shorter underwriting cycles, higher cross-sell, and better fraud detection.
Many BFSI firms operate with legacy infrastructure, siloed incompatible apps, and limited compute resources, which hampers AI value realization. AI requires elastic compute, low-latency inference, and tools for monitoring model behavior. A flexible, integrated technology layer that evolves with the business determines whether AI remains a PoC or scales enterprise-wide. Firms must:
- Build strategic partnerships with external providers such as Google Vertex AI, Amazon SageMaker, and Azure Machine Learning Studio.\
- Adopt modular, cloud-native platforms, and open-source tools to accelerate experimentation and deployment.
- Choose the right architectures for storage, access control, model tracking, and audit readiness for regulatory compliance.
BFSI firms grapple with fragmented, inconsistent, or AI-unready data. Historical datasets often lack the structure needed for reliable models while organizational silos slow innovation. Data governance frameworks must support responsible innovation by enhancing focus on high-quality, purpose-fit data. Making this a reality will require BFSI firms to:
- Invest in robust, AI-grade pipelines that can validate data, detect drift, and enable low-latency access.
- Move to domain-specific data models, enhancing trust and reliability.
- Leverage external data such as geolocation, environment, social, and governance (ESG), and credit scores, to enhance fraud detection and risk modeling.
- Drive innovation through consent-based data-sharing, granular access, and synthetic data.
Even advanced AI models will fail to deliver value if they are not grounded in real-world workflows, especially in the BFSI industry, where regulation and domain-specific complexities are high. Many AI initiatives falter due to poor alignment with actual decision-making. For example, a fraud model that ignores regional norms or does not address compliance exceptions will not gain traction regardless of its accuracy. Broken feedback loops where business users’ inputs are not taken into consideration can compromise AI initiatives. Success will depend on:
- Embedding domain expertise across the AI lifecycle—from framing use cases to validating edge cases—to ensure that AI complements (not disrupts) business processes.
- Establishing clear model thresholds, human-on-the-loop interfaces, and subject matter expert (SME)-driven incident handling ensures that business users’ feedback informs model updates.
- Ensuring AI learns from domain feedback and AI outputs integrate into domain workflows to unlock synergies.
- Creating consistent domain taxonomies to improve data quality and governance and enable traceability.
A framework to maximize AI RoI
Once the four dimensions of people, technology, data and domain are aligned with AI initiatives, the next step is to actually embark on the transformation journey.
In our view, embracing the four-pillar framework (see Figure 2) will give structure to BFSI firms’ AI journey. The framework offers practical recommendations to firms on how to build on AI’s unique strengths, establish guardrails to contain potential risks, and put in place objective evaluation criteria to quantify RoI.
Figure 2: The four-pillar framework to enhance AI RoI
New technologies often bring a flood of appealing use cases, but only a few create true value. Selecting those with the right mix of depth and breadth is key.
- Depth – strategic fit: Assess alignment of the use case with business strategy and competitive edge.
- Unique selling proposition (USP): Identify USP by answering two questions: Will success in this area strengthen market leadership? Will competitors struggle to replicate it, even with similar tools? One approach is to prioritize use cases that leverage proprietary data or core strengths, not those that can be outsourced.
- Commoditization risk: Avoid choosing use cases that may soon become obsolete due to technology shifts, nullifying the unique advantage. For instance, smartphones rendered basic cameras redundant. Similarly, streaming analytics have replaced batch feeds for real time decision-making. If a use case lacks defensibility in the face of the changing technological landscape, it will not sustain value.
- Breadth – execution enablers: Consider the actors and mechanics that enable the use case to succeed.
- Change agents: Ensure collaboration between domain experts and AI practitioners. Domain specialists bring real-world context, while AI teams solve the corresponding data problem. Their mutual conviction in the value of the use case drives successful AI transformation.
- Quantifiable metrics: Quantify the specifics of what AI is solving. Specifics make assumptions testable, comparisons fair, and progress measurable, which brings clarity and accelerates development. For example, agreeing whether food is ’tasty’ is subjective, but ’undercooked’ is objective — clarity like this accelerates development.
- Feedback loop: Establish a robust feedback mechanism that can identify specific aspects of the AI system that need to be refined. This will create a self-improving loop with minimal deviations.
Most leading banks have found immense value through strategic alignment with current ongoing programs. A global systemically important bank (G-SIB) used the depth and breadth evaluation criteria to generate early warning signals to help identify potential credit defaulters. The bank was able to report tangible progress on the measurable KPI of dollars saved.
Choosing well-rounded use cases may demonstrate value in prototype settings but fail to translate into production outcomes. Prototypes operate on assumptions under ideal conditions, a far cry from the real world.
To scale benefits across the enterprise, BFSI firms must focus on the following factors:
- Upstream and downstream flexibility: Map the full workflow before and after the AI use case. Assess each stage's flexibility across data throughput, format, update frequency, compute costs, external dependencies, and manual interventions. AI use cases with adaptable workflows are more likely to deliver long-term value:
- AI can offer hourly liquidity profile updates instead of the traditional twice-daily schedule. But for this to improve business outcomes, downstream systems must act on hourly insights and upstream pipelines must deliver fresh data just as frequently. Without this, the prototype value will not convert into operational impact.
- A fraud detection model offering alerts every two hours is irrelevant if the bank only processes them daily.
- Build versus buy decisions: Evaluate whether components are modular or tightly coupled. Replacing specific workflows with third-party solutions instead of overhauling rigid legacy components can help preserve momentum besides being cost-effective.
- Client value and acceptance: Assess IT ecosystem readiness and the perceived value of implementing the model.
- A client operating with a model of 97% accuracy declined an AI-powered upgrade offering 99% accuracy given the effort required to adapt existing downstream processes. Thus even a technically superior solution will not yield returns if clients are unwilling or unable to adapt existing processes—perceived RoI matters as much as technical performance. Similarly, next-gen segmentation outputs may not be adopted if IT integration costs outweigh expected gains.
- A leading European bank created an AI model to generate next best offer (NBO) recommendations. The model was tried on a limited customer base and the results showed that it was worth scaling to production. However, the business case was weak due to other priorities. Using the four-pillar framework, the bank uncovered key hidden KPIs around effort-to-scale, new client acquisition, and net promoter score (NPS). Uncovering this hidden link helped demonstrate value, ultimately driving the decision to implement the model.
As with human experts, AI too makes mistakes and users tend to accept it. However, AI errors are glaring and take too long to self-correct. For example, early large language models (LLMs) claimed that ‘strawberry’ had two ‘r’s and only conceded after extended prompting. In another case, a user tricked a chatbot into offering a Chevy Tahoe costing $75,000 for $1. These failures highlight the need to evaluate how human intelligence can be inserted into AI workflows. At the same time, balancing oversight and learning requires thoughtful design, demanding inputs from workflow experts to ensure AI systems evolve safely, efficiently, along with built in human support.
- Relay race versus individual race: Ensure seamless AI-human collaboration where both AI and humans leverage their unique strengths. Operationalizing this will require banks and insurers to determine if the chosen use cases allow AI and humans to pass the baton seamlessly or if AI must run the full course alone. In tightly coupled workflows, human intervention may disrupt timing. But in asynchronous settings, human involvement can catch critical errors without slowing the process.
- Explainability: Enable well-designed hand-offs for better collaboration and trust. Even when human oversight is possible, how the AI model hands over matters. One extreme: the expert starts over, ignoring AI’s output. The other: AI clearly explains its logic and narrows down the options, letting the human choose.
- Coachability: Enable learning by allowing user interactions to feed into the AI engine. Search engines do this effectively—user clicks on lower-ranked links inform future rankings. In intelligent document processing (IDP), when AI lacks confidence in a social security number (SSN), it can automatically pre-fill the field, flag it for human review, and learn from human action.
BFSI firms are seeing significant value by leveraging AI for statistically heavy use cases such as portfolio management and yield or swap curve constructions. However, given the transparency required to explain the construction process, AI-only use cases face business resistance and human-in-the-loop designs are encouraged—pillar three mandates precisely that: synergistic human-augmented AI output.
The first three pillars are centered on business RoI while keeping other variables constant. However, there are multiple ways of keeping the other variables constant and each affects the RoI differently. To arrive at a production-grade use case, it is important to determine the right mix of variables that need to be constant. Production-ready AI use cases are resilient, compliant, trusted, and designed for real-world impact. Thus, primetime-readiness translates into embedding the right safeguards while enabling confident, scalable deployment.
- Operational readiness: Ensure built-in auditability, clear governance, and mechanisms to detect and correct errors for mature use cases. Provide support through logging, fallback strategies, and clear escalation paths, making them reliable under pressure.
- Compliance and ethics: Incorporate privacy protection, bias mitigation, and ethical use at the outset during design. BFSI firms must build explainability and fairness into the model lifecycle by default and not as afterthoughts.
- Brand and business alignment: Align models with organizational values and customer trust, calibrating for reputational impact and operational scale.
Ultimately, a use case is primetime-ready when it can operate safely, transparently, and responsibly at enterprise scale, delivering business value without compromising control or trust during deployment. Before embarking on deployment in a production environment, BFSI firms must establish guardrails, simulate scenarios with a fail-fast approach, and lay down systems to monitor progress.
The four pillars form a practical framework for scaling AI with confidence. Each pillar addresses a critical success factor, ensuring that AI initiatives are not just technically sound but strategically aligned, operationally embedded, and enterprise-ready. To effectively measure progress, organizations must track RoI-aligned metrics that reflect real impact across people, process, technology, and business performance (see Table 1).
Metric |
Use case |
Dimension |
Business KPIs |
Improved fraud detection, claim accuracy, underwriting speed |
Domain |
Time to production |
Duration from model development to deployment |
Technology |
Model adoption rate |
% of users or business units using the model in real workflows |
People, domain |
Cost per decision |
Infrastructure and operational cost per prediction or automation |
Technology |
Revenue contribution |
Uptick in cross-sell, dynamic pricing, or customer retention |
Data, domain |
Data reuse rate |
% of new models using shared pipelines or feature stores |
Data |
Feedback-to-retrain cycle |
Time taken to update the model when a user raises an issue |
People, domain |
Training efficiency |
Time and cost to train or retrain models at scale |
Technology |
AI upskilling coverage |
% of the workforce trained in AI tools or concepts |
People |
Explainability compliance |
% of models meeting internal and regulatory explainability and audit standards |
Domain, technology |
Table 1: AI RoI-aligned metrics to quantify and monitor AI transformations
Shaping the AI future
Growing investments in AI indicate BFSI firms’ ambition to scale.
However, RoI gaps are real and need to be bridged. For successful, enterprise-wide AI transformation focused on AI RoI, CXOs at banks and insurance firms must create an environment conducive to maximizing the power of AI solutions. A fresher can evolve into a trusted lieutenant who can be assigned critical responsibilities if a favorable learning environment conducive to her professional growth is created. Likewise, today’s newly minted AI models can morph into mature and dependable digital partners if the right technological and procedural ecosystem is provided.
To successfully move the AI RoI needle, BFSI firms must prioritize the right use cases and build on top of existing architectures to quickly adapt to new technology and data needs, ultimately creating an enterprise AI architecture. This will help move AI initiatives beyond experimentation to adoption-at-scale. As firms navigate uncharted territory, knowing what will work and what will not from a practitioner’s perspective is paramount. And here’s where a structured approach that incorporates a tested framework with the capability to align new age AI advances with business RoI can be a godsend. The time for action is now, and banks and insurers that move quickly to adopt an approach relentlessly centered on business RoI will lead in the AI era.
