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Why AI Projects Fail in Finance: The Top 7 Drivers

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  Published in Business and Finance on Tuesday, December 9th 2025 at 7:30 GMT by Forbes

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Why AI Projects Fail in Finance (and How to Build Ones That Succeed)
Summary of Forbes Technology Council Insights (Published December 9, 2025)

Introduction

Artificial intelligence is the new frontier for banks, insurers, and fintechs, promising everything from fraud detection to automated wealth management. Yet, despite the hype, a surprisingly high proportion of finance‑centric AI initiatives falter—often within the first year. A recent Forbes Technology Council article titled “Why AI Projects Fail in Finance and How to Build Ones That Succeed” dives into the root causes of these failures and offers a pragmatic roadmap for turning AI aspirations into operational realities.

The piece is authored by industry veterans who have piloted and scaled multiple AI programs across the financial services spectrum. Their take‑away? Success hinges less on technology and more on strategy, governance, and human factors. Below we distill the article’s key arguments and actionable recommendations.

Why AI Projects Fail in Finance

The Forbes article outlines seven core failure drivers that are particularly pronounced in regulated, data‑rich environments like banking and insurance:

These failure modes are not unique to finance, but the industry’s regulatory complexity and high customer trust stakes amplify their impact. The article underscores that even when the AI model performs well on test data, it may still fail because the governance pipeline—from data lineage to model monitoring—is missing.

The Success Blueprint: A 5‑Phase Framework

To counter these pitfalls, the Forbes article proposes a structured framework that integrates business strategy, data management, risk controls, and continuous learning. The phases are:

1. Problem Definition & Stakeholder Alignment
   Translate business goals into measurable outcomes.
   - Conduct workshops with product, risk, compliance, and customer‑experience teams.
   - Use OKRs (Objectives & Key Results) to tie the AI initiative to financial performance metrics.

2. Data Strategy & Governance
   Build a robust data ecosystem that meets regulatory and ethical standards.
   - Implement a unified data catalog with metadata, lineage, and quality scores.
   - Use automated data profiling tools to detect bias, missingness, and outliers.
   - Establish a Data Governance Board comprising risk, legal, and IT leaders.

3. Model Development with Bias & Explainability Controls
   Adopt a “model‑as‑a‑service” approach that includes bias mitigation.
   - Start with interpretable models (e.g., logistic regression, decision trees) before moving to complex neural nets.
   - Use SHAP or LIME to explain predictions to stakeholders.
   - Embed fairness constraints to satisfy regulatory requirements on discrimination.

4. Operationalization & Continuous Monitoring
   Deploy models into production with a focus on observability.
   - Use a MLOps platform that automates CI/CD pipelines for models and data.
   - Set up real‑time dashboards for performance drift, data drift, and risk indicators.
   - Define clear “model kill‑switch” protocols for rapid rollback.

5. Governance, Audit, & Learning Loops
   Ensure ongoing compliance and incorporate lessons learned.
   - Conduct quarterly “Model Risk Reviews” that include auditors, risk officers, and business leads.
   - Document all model decisions, data sources, and changes in a central repository.
   - Create a “post‑mortem” process for every model failure to capture insights.

The article stresses that these phases should be iterative—the insights from operational monitoring often necessitate a revisit to data strategy or even to the original problem definition.

Implementation Checklist (From the Forbes Article)

The article notes that adopting even a subset of this checklist can double the success rate of AI initiatives.

Real‑World Examples & Lessons

The Forbes piece cites two anonymized case studies to illustrate the framework in action:

• Retail Banking Fraud Detection – A mid‑size bank replaced a legacy rule‑based system with a supervised learning model. By first involving compliance and risk teams in the data‑curation phase, they avoided regulatory fines and increased fraud detection rates by 18% within six months.

• Insurance Underwriting Automation – An insurer used an interpretive model for auto‑policy pricing. After embedding a bias‑scan routine, they discovered and corrected a gender‑based bias that would have violated new EU regulations, saving the company €2 million in potential penalties.

These examples reinforce that stakeholder engagement and governance are the linchpins of successful AI projects.

Additional Resources Mentioned

While summarizing, the article references several external resources that deepen the reader’s understanding:

• Forbes Technology Council: A peer‑reviewed community that publishes finance‑tech research.
• “Model Risk Management in the Era of AI” – A whitepaper by the Basel Committee.
• AI Fairness 360 Toolkit – An open‑source library for bias detection.
• MLOps Best Practices Guide – Published by the Open Data Institute.

Finance leaders can explore these links for detailed frameworks, toolkits, and policy guidance.

Conclusion

The Forbes article delivers a clear message: AI projects in finance succeed not because of the algorithms, but because of the processes that surround them. By starting with a well‑defined business problem, building a data and governance foundation, and continuously monitoring outcomes, organizations can avoid the pitfalls that plague many AI initiatives. The 5‑phase framework and accompanying checklist provide a practical playbook that aligns technology with regulatory, risk, and strategic imperatives.

For finance executives, the takeaway is simple yet profound: Treat AI as a strategic, governed capability—not a technological novelty. Those who adopt this mindset will not only avoid costly failures but also unlock significant value in risk mitigation, customer experience, and competitive differentiation.