# AI Lifecycle Models Need To Be Revised. An Exploratory Study in Fintech

arXiv: https://arxiv.org/abs/2010.02716 · HTML: https://mlsanity.com/paper/2010.02716/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2020.

## Built on by (most cited first, 30 of 33)

- [A Systematic Review of Green AI](https://mlsanity.com/paper/2301.11047/) (2023)
- [Collaboration Challenges in Building ML-Enabled Systems: Communication, Documentation, Engineering, and Process](https://mlsanity.com/paper/2110.10234/) (2021)
- [Data-Centric Green AI: An Exploratory Empirical Study](https://mlsanity.com/paper/2204.02766/) (2022)
- [A Meta-Summary of Challenges in Building Products with ML Components -- Collecting Experiences from 4758+ Practitioners](https://mlsanity.com/paper/2304.00078/) (2023)
- [The Prevalence of Code Smells in Machine Learning projects](https://mlsanity.com/paper/2103.04146/) (2021)
- [Aspirations and Practice of Model Documentation: Moving the Needle with Nudging and Traceability](https://mlsanity.com/paper/2204.06425/) (2022)
- [The Pipeline for the Continuous Development of Artificial Intelligence Models -- Current State of Research and Practice](https://mlsanity.com/paper/2301.09001/) (2023)
- [Establishing and Evaluating Trustworthy AI: Overview and Research Challenges](https://mlsanity.com/paper/2411.09973/) (2024)
- [Code Smells for Machine Learning Applications](https://mlsanity.com/paper/2203.13746/) (2022)
- [Machine Learning with Requirements: a Manifesto](https://mlsanity.com/paper/2304.03674/) (2023)
- [Data Smells in Public Datasets](https://mlsanity.com/paper/2203.08007/) (2022)
- [Maintainability Challenges in ML: A Systematic Literature Review](https://mlsanity.com/paper/2408.09196/) (2024)
- [Improving Label Error Detection and Elimination with Uncertainty Quantification](https://mlsanity.com/paper/2405.09602/) (2024)
- [Monitoring Machine Learning Models: Online Detection of Relevant Deviations](https://mlsanity.com/paper/2309.15187/) (2023)
- [Innovating for Tomorrow: The Convergence of SE and Green AI](https://mlsanity.com/paper/2406.18142/) (2024)
- [Trustworthy and Responsible AI for Human-Centric Autonomous Decision-Making Systems](https://mlsanity.com/paper/2408.15550/) (2024)
- [Systematic Mapping Study on the Machine Learning Lifecycle](https://mlsanity.com/paper/2103.10248/) (2021)
- ["Project smells" -- Experiences in Analysing the Software Quality of ML Projects with mllint](https://mlsanity.com/paper/2201.08246/) (2022)
- [Analysing and Organising Human Communications for AI Fairness-Related Decisions: Use Cases from the Public Sector](https://mlsanity.com/paper/2404.00022/) (2024)
- [Towards Green AI: Current status and future research](https://mlsanity.com/paper/2407.10237/) (2024)
- [Co-Producing AI: Toward an Augmented, Participatory Lifecycle](https://mlsanity.com/paper/2508.00138/) (2025)
- [Data and AI governance: Promoting equity, ethics, and fairness in large language models](https://mlsanity.com/paper/2508.03970/) (2025)
- [MLSmellHound: A Context-Aware Code Analysis Tool](https://mlsanity.com/paper/2205.03790/) (2022)
- [Lost in Translation: Reimagining the Machine Learning Life Cycle in Education](https://mlsanity.com/paper/2209.03929/) (2022)
- [McUDI: Model-Centric Unsupervised Degradation Indicator for Failure Prediction AIOps Solutions](https://mlsanity.com/paper/2401.14093/) (2024)
- [A Framework to Model ML Engineering Processes](https://mlsanity.com/paper/2404.18531/) (2024)
- [Agile Management for Machine Learning: A Systematic Mapping Study](https://mlsanity.com/paper/2506.20759/) (2025)
- [CausalOps -- Towards an Industrial Lifecycle for Causal Probabilistic Graphical Models](https://mlsanity.com/paper/2308.01375/) (2023)
- [Experimentation, deployment and monitoring Machine Learning models: Approaches for applying MLOps](https://mlsanity.com/paper/2408.11112/) (2024)
- [Scalability and Maintainability Challenges and Solutions in Machine Learning: Systematic Literature Review](https://mlsanity.com/paper/2504.11079/) (2025)

## Abstract (arXiv)

Tech-leading organizations are embracing the forthcoming artificial intelligence revolution. Intelligent systems are replacing and cooperating with traditional software components. Thus, the same development processes and standards in software engineering ought to be complied in artificial intelligence systems. This study aims to understand the processes by which artificial intelligence-based systems are developed and how state-of-the-art lifecycle models fit the current needs of the industry. We conducted an exploratory case study at ING, a global bank with a strong European base. We interviewed 17 people with different roles and from different departments within the organization. We have found that the following stages have been overlooked by previous lifecycle models: data collection, feasibility study, documentation, model monitoring, and model risk assessment. Our work shows that the real challenges of applying Machine Learning go much beyond sophisticated learning algorithms - more focus is needed on the entire lifecycle. In particular, regardless of the existing development tools for Machine Learning, we observe that they are still not meeting the particularities of this field.