Fiddler for Financial Services

Minimize AI risks and provide customers with fair and equitable outcomes
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Financial services institutions (banks, credit card and related organizations, fintech) are charging ahead to implement generative AI and LLM applications to enhance existing and launch new products and services, improve productivity, and reduce operational costs, while meeting robust AI governance and regulatory compliance standards.

Fiddler is a pioneer in AI Observability — the foundation to ensure the performance, behavior, and safety of predictive and generative AI models and applications. With Fiddler, financial services institutions (FSIs) will be equipped with a comprehensive AI Observability platform to operationalize predictive and generative AI models responsibly and at scale. 

The Fiddler AI Observability platform aligns developers, platform engineering, data science, and business teams through the lifecycle to monitor, analyze, explain, and improve models and applications. 

A diagram of Fiddler's full stack AI Observability platform for generative AI and predictive models.

Why FSI Leaders Choose Fiddler

  • Expert AI Team for Responsible AI: Fiddler’s expert AI team is dedicated to helping enterprises succeed in their ML and LLM deployments, ensuring the achievement of responsible AI.
  • Unified Environment for ML and LLMOps: Provides a common language, centralized controls, and actionable insights to operationalize predictive and generative AI with trust.
  • 360° View Into AI Behavior: Integrates best-in-class XAI and analytics for a complete understanding of why and how predictions are made.
  • Built for the Enterprise: Enterprise-scale security and support without the hassle of building and maintaining in-house LLM and MLOps monitoring systems.

AI Observability is a Must to Scale Safe and Trustworthy AI

For enterprises to fully leverage AI capabilities, it is crucial to standardize their ML and LLMOps using the MOOD stack — a comprehensive framework that includes Modeling, AI Observability, Orchestration, and Data layers. Each layer plays an integral role in the overall functionality, with AI Observability being the key component that orchestrates and enhances the other layers by providing governance, interpretability, and ML and LLM monitoring to improve operational performance and mitigate risks.

This holistic approach not only enhances the reliability and accuracy of AI but also ensures compliance and reduces the likelihood of issues, thereby supporting the enterprise’s overall strategic goals.

MOOD - The 4 Layers of the LLMOps Stack

Fiddler Supports Advanced and Complex Use Cases for FSIs

Generative AI Use Cases

  • AI Chatbots:
    • Boost investor value and confidence with accurate financial advice and recommendations from AI chatbots. 
    • Ensure the accuracy, safety, privacy, and correctness of NLP and LLM-based chatbot conversations. Monitor key metrics such as hallucination rates, toxicity levels, and user feedback to maintain high performance in chatbot interactions.
  • Internal Copilot Applications:
    • Enhance employee productivity and boost their confidence in decision-making.
    • Boost your employees’ confidence and trust in information generated by LLM applications for business-critical projects. Fiddler monitors a comprehensive range of LLM metrics (hallucination, safety, PII) to ensure that these applications provide correct and accurate information for financial planning and investment analyses.
  • Risk Management
    • Detect adversarial attacks and data leakage.
    • Detect jailbreaks and prompt injection attacks that risk LLM applications from exposing sensitive enterprise and customer information. Visualize patterns in a 3D UMAP and identify all prompt injection attacks using Fiddler’s Slice and Explain.
  • Content Summarization
    • Deliver highly accurate summaries for your users.
    • Enhance the accuracy of LLM summarization by monitoring hallucination metrics such as faithfulness, answer/context relevance, coherence, and consistency using Fiddler Trust Models. Quickly detect signs of hallucination, diagnose and address their root causes before inaccurate LLM summarizations can impact the enterprise and its users.
  • LLM Cost Management
    • Increase LLM operational efficiency gains.
    • Track the costs of using LLM applications by obtaining a complete view of your LLM costs, latency, and session length. Use Fiddler’s custom metrics to measure efficiency gains, helping streamline processes and enhance productivity. 

Predictive Use Cases

  • Customer Risk Assessment
    • Improve the accuracy in credit, lending, and underwriting assessments.
    • Gain actionable insights into the performance of your credit, lending and underwriting models. Conduct root cause analysis to identify the features contributing to drift, and leverage Fiddler’s ‘Slice and Explain’ capability to further analyze dataset segments, including feature impact, correlation, and distribution. 
  • Fraud Detection
    • Safeguard the company and customers from fraudulent activities, and reduce the risk of financial loss.
    • Improve the detection of fraudulent transactions by closely monitoring subtle shifts in data caused by models processing highly imbalanced data inputs, and get real-time alerts as soon as anomalies come up.
  • Personalized Recommendations
    • Delight customers with personalized recommendations.  
    • Use Fiddler to uncover why segments of customers are offered specific products and services, and which factors contribute to those recommendations. Understand which attributes from segments of customers contribute to product recommendations.
  • Governance, Risk Management, and Compliance
    • Safeguard predictive and generative AI applications, and minimize risks and adverse consequences. 
    • Streamline Model Risk Management (MRM) and Governance, Risk, and Compliance (GRC) workflows with Fiddler. Enhance oversight of all your ML models and LLMs through a centralized platform. Utilize the Fiddler Report Generator to create fully customizable reports for periodic audits and MRM and GRC reviews, ensuring models remain compliant, safe, and secure while minimizing adverse risks, consequences, and preventing potential scandals.

Key Capabilities


Monitor predictive and generative AI models across pre-production and post-production. Track ML and LLM metrics at scale in a unified dashboard. Use the Fiddler Trust Service to quickly, safely, and cost-effectively measure LLM metrics with accuracy.

From model monitoring alerts to detailed root cause analysis, quickly identify and address model issues to minimize business impact.

  • Metrics Monitoring: Detect changes in model behavior by monitoring ML and LLM metrics
  • Segment Monitoring: Monitor segments to drill down on underperforming cohorts
  • 3D UMAP Visualizer: Gain contextual insights by identifying patterns and outliers in high dimensional spaces
  • Alerts: Configure and receive real-time model monitoring alerts to identify and troubleshoot high-priority issues
Fiddler chart for bank churn showing revenue impact by state


Gain actionable insights to power data-driven decisions. Analyze slices of data to surface the root cause of issues. Understand the 'why' behind all issues for quick issue resolution and model improvement.

  • Dashboards: Increase business alignment and confidence in decision-making by connecting ML and LLM metrics to business KPIs in a unified view
  • Charts: Build customizable reports with the reports with the insights you need to gain deep understanding of your models and their impact on business outcomes
  • Root Cause Analysis: Pinpoint problematic areas causing models to underperform
  • Slice and Explain: Drill down on slices of data to perform exploratory or targeted analysis
Fiddler root cause analysis for Hawaii bank churn customers

Explainable AI (XAI)

Fiddler provides best in class explainable AI technology to provide complete context and visibility into tabular and image model behaviors and predictions, from training to production. Implement powerful XAI techniques at scale to build trusted AI solutions.

  • Explainability methods for your use case: Increase your model’s transparency and interpretability using SHAP values, Fiddler SHAP, Integrated Gradients, or custom explanations
  • Image Explainability: Explain predictions in image models to understand their ensure they are high-performing and accurate
  • ‘What-If’ Analysis: Gain a better understanding of your model’s predictions by changing any value and studying the impact on scenario outcomes
  • Global and Local Explanations: Understand how each feature you select contributes to the model’s predictions (global) and uncover the root cause of an individual issue (local)
Fiddler explainable AI for bank churn
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