Measuring what matters: GenAI KPIs for banking success beyond accuracy

Step 2: Establish Baselines & Measurement Mechanisms

In recent years, Generative AI (GenAI) has moved well beyond the realm of novelty emerging as a strategic imperative for global financial institutions. As banks and fintechs integrate GenAI into everyday operations, they are forced to ask: How do we measure success beyond model accuracy? Traditional performance metrics, like precision, recall, or F1 scores, only tell us if a model is technically correct. But enterprise value lies in outcomes operational, human, and financial.  

Empirical research confirms GenAI’s transformative potential. A study of customer support teams found that access to AI assistance raised productivity by 14%, enabling agents to resolve more issues per hour, with especially significant gains for less-experienced staff ^{[1] [2]}.  

Similarly, controlled experiments using GitHub Copilot showed developers completing tasks 55.8% faster with AI assistance ^[3]. On a macro scale, workers using GenAI report saving an average of 5.4% of work hours per week, translating to about 2.2 hours saved weekly per user ^{[4] [5]}. Economists anticipate that GenAI could boost global labour productivity by 0.1 to 0.6 percentage points annually through 2040, with automation driving even higher gains ^{[6] [7]}. 

Yet, many AI projects fall short of delivering tangible business returns. Studies show that 95% of organizations report no measurable ROI on their GenAI initiatives often because pilots lack integration into workflows, use cases remain vague, and success metrics are misaligned ^[8]. 

Therefore, banking leaders must embrace a holistic KPI framework, one that measures adoption, productivity, satisfaction, compliance, and economic outcomes. Only then can GenAI transition from an experimental toolkit into a consistent driver of operational excellence, resilience, and competitive differentiation. 

2. Why expand KPIs beyond model accuracy? 

While traditional AI evaluation emphasizes technical metrics such as accuracy, precision, recall, and F1 scores, these measures alone fail to capture whether GenAI is delivering value to the enterprise. A growing body of research underscores the limitations of model-centric evaluation and emphasizes the importance of human-centric, productivity-driven, and compliance-oriented KPIs. 

2.1. Model accuracy ≠ Business impact 

• The "accuracy paradox" illustrates how highly accurate models can fail in practice when integrated into misaligned workflows or when user trust is lacking. AI often highlights inconsistencies within existing processes, making accurate models appear less useful in real-world operations ^{[9] [10]} . 

2.2. Productivity gains over marginal accuracy 

• Evidence shows that GenAI’s economic benefits stem more from enhancing productivity than from incremental accuracy improvements: 
• In a landmark field experiment, customer support agents using GenAI assistants resolved 14% more issues per hour, with the largest gains (up to 35%) seen among less experienced agents  ^{[11] [12] [13]}. 
  Separate research found software developers completed tasks 55.8% faster with AI pair-programming tools—without any compromise in correctness ^[14] . 
• The accuracy-focused mindset underestimates the value of time saved, worker enablement, and task acceleration—core benefits that expand enterprise value. 
  2.3. Adoption & Trust Drive ROI 
• High-performing AI models often fail to yield ROI when end-users don’t trust or adopt them. Research shows that interpretability alone doesn't build trust; instead, outcome transparency (feedback on impact) is more effective at enhancing adoption ^[15]. 
• Tools that offer clear, intuitive outputs see higher adoption a necessity if AI output is to benefit real-world operations. 

2.4. Governance, Compliance & Ethical risks require their own KPIs 

As GenAI becomes more pervasive, it's not enough to measure only performance. Robust KPIs must include: 

• Hallucination frequency (incorrect AI-generated content). 
• Bias and fairness metrics (e.g., demographic parity). 
• Compliance violations (e.g., privacy breaches, misinformation). 

Regulatory frameworks such as the NIST AI Risk Management Framework advocate measuring trust and safety—not just technical accuracy  ^[16][17] . 

3. KPI categories for banking: Capturing GenAI's true value 

Financial institutions must move past model-centric metrics and adopt a multi-dimensional KPI framework to fully realize the value of GenAI. Below is a refined, banking-focused breakdown of the core KPI categories each supported by global insights and industry data. 

 

a)Model quality

• What to Measure: Traditional metrics precision, recall, F1-score are essential for tracking baseline performance. 
• Beyond the Basics: Benchmarks like LLM self-assessment (e.g., pairwise preference scoring) can augment evaluation rigor. 
• Key Insight: Strong accuracy alone doesn’t guarantee real-world gains without adoption and workflow alignment.  

(b) Adoption & utilization

• What to Measure: % of staff using GenAI tools (e.g., RMs, compliance); production rollout rates. 
• Why It Matters: Even accurate models fail if adoption is low. Bain reports ~20% average productivity gains in financial institutions where GenAI is actively used. 
• Banking Insight: By mid-2024, 75% of banks were deploying “at least one” GenAI application, with 40% in production. 
  World Wide Technology 

(c) Productivity & efficiency

• What to Measure: Time saved per task (e.g., KYC processing cycle time); tasks/issue resolution per hour. 
• Global Data: GenAI users saved an average of 5.4% of weekly work hours about 2.2 hours per week. 
• Banking Example: Indian banks could boost operational efficiency by up to 46% via GenAI. 
• EY Insight: GenAI in banking is enabling automation of sales activities (66%) and accelerating innovation (54%). 

(d) Business impact & innovation

• What to Measure: Revenue uplift tied to GenAI (e.g., personalization cross-sell), cost savings, and innovation speed (time-to-market). 
• Global Scale: McKinsey estimates GenAI could contribute $2.6–4.4 trillion annually, with banking among the top sectors. 
• Accenture Insight: Early adopters report 20–30% productivity improvements, up to 6% revenue growth, and notably higher Return on Equity. 

(e) Employee & customer satisfaction

• What to Measure: NPS post-GenAI interaction, internal satisfaction scores, perceived workload reduction. 
• Global View: Employees using GenAI report lower cognitive load and higher job satisfaction. 
• Banking Behavior: JPMorgan’s internal GenAI tools increased advisory productivity over threefold and reduced servicing costs by nearly 30%. 

(f) Governance, compliance & risk

• What to Measure: Frequency of hallucinations; bias evaluations; policy or regulatory breaches. 
• Why It Matters: Foundation models bring risks from hallucinations to fairness concerns. Rigorous KPIs ensure GenAI remains trusted.
• (g) Strategic & ESG-aligned metrics 
• What to measure: Carbon footprint per model inference; sustainability contributor scores; ethical incident reductions. 
• Global sustainability insights: 
  Efficiency of AI operations is emerging as a governance and ESG metric but detailed financial-sector data is still developing.

Integrated KPI dashboard for GenAI in banking