⚡ Quick Summary
Published by Kodex AI and co-authored with a Deutsche Bank contributor, this white paper examines how generative AI (GenAI) can be adopted in banking and the wider financial services industry. Its stated aim is to raise awareness of the factors involved in implementing GenAI systems and to give guidance on approaches, success factors, risks and regulatory considerations, with a focus on GenAI text-to-text systems.
The paper argues for a structured, incremental roadmap rather than one-off deployments. It proposes a three-stage composable portfolio of use cases — core text and language analysis, chat-to-agent, and chat-to-execution using agentic AI — and sets out quality determinants covering data, system components and cybersecurity, including Retrieval-Augmented Generation (RAG), Parameter Efficient Fine Tuning (PEFT), Low Rank Adaptation (LoRA/qLoRA) and Self-Play Fine Tuning (SPIN).
It catalogues general benchmarks (GLUE, SQuAD, MMLU, BIG-bench, THOR) and financial ones (FinanceBench, FinQA, FNS), then pairs data, model, dependency, cybersecurity and sustainability risks with mitigants. Its recommendations include industry test data, fair usage policies, AI sandboxes, reskilling and streamlined existing regulation.
🧩 What’s Covered
The paper runs from a foreword through six chapters to a conclusion and a reference list.
- Foreword and introduction (Sections 1.1–1.3): positions GenAI as a human-computer interface, covers its use in financial services, and contrasts it with robotic process automation, traditional AI and agentic AI (Figure 2), alongside model types from foundation model to deployed model with prompt engineering (Figure 1).
- Aligning a problem statement with GenAI (Section 1.4): six criteria — problem variability, data sufficiency, results materiality, extent of human decisions, cost of solution and jobs — plus a Project Aggie example that cut regulatory summarisation from 120 minutes to minutes under maker-checker governance.
- Building a portfolio of GenAI use cases (Section 2): a three-stage composable approach, from core text and language capabilities to chat-to-agent, illustrated by Project MILA's seven-step analysis of a public test set with 31 features and about 520,000 entries, and on to chat-to-execution with agentic qualities such as decision-making autonomy and self-adaptation.
- Identifying excellence (Section 3): foundation-model benchmarks (GLUE, SuperGLUE, LAMBADA, SQuAD, MMLU, BIG-bench, DNLI, MultiWOZ, THOR, ALFRED), domain benchmarks (FinanceBench, FinQA, FNS), Figure 9 on specialised versus generalised performance, and architecture factors including data, Knowledge-GraphRAG, PEFT, LoRA/qLoRA, chunking, content filters and lock-in avoidance.
- Factors that compromise quality (Section 4): data risks and mitigants, synthetic data, model drift, hallucination, feedback loop degradation, human-in-the-loop evaluation, cross-validation, performance metrics, scenario testing, dependency risks, LLM-specific attacks in Figure 10, and sustainability risks.
- Implementing GenAI and recommendations (Section 5): a non-exhaustive inventory of regulatory topics (Figure 12), explainability and transparency techniques, data and hallucination, synthetic data, IP and copyrights, open standards and fair data practice, cross-border scalability, non-English benchmarks, reskilling, and productivity metrics for ROI.
- Conclusion and references: asks whether GenAI can thrive in the regulated financial industry and calls for public-industry engagement; references include ASIFMA, the OECD, GFMA, Singapore's Model AI Governance Framework for Generative AI, Andrew Ng and VentureBeat.
💡 Why it matters?
For teams deploying GenAI inside a regulated firm, the paper provides a screening method before procurement: the six alignment criteria and Figure 3 help decide whether a problem suits generative AI at all, or better fits traditional AI or robotic process automation. Its risk catalogue — data, drift, hallucination, feedback loops, dependency, LLM-specific attacks and sustainability — maps each risk to named mitigants such as RAG, content filters, human-in-the-loop review and controlled retraining cycles. The benchmark lists give assessors concrete starting points for comparing models, and the regulatory chapter frames the compliance areas, from localisation and third-party risk to model evaluation, that GenAI deployments typically trigger.
❓ What’s Missing
Several areas are left open. The regulatory section is deliberately non-exhaustive and stays at topic level, without naming or analysing specific instruments or obligations. No implementation templates, thresholds, evaluation protocols or cost figures are given, and the productivity examples (Project Aggie, Project MILA) provide single measurements without describing the test conditions. Figure 9 shows benchmark scores that the surrounding text does not attribute on the extracted pages. Publication metadata is thin: no explicit publication date, version number or ISBN is printed. Non-English markets are identified as a gap rather than resolved.
👥 Best For
GenAI programme leads and product owners in banks and other regulated firms scoping a use-case portfolio; risk, compliance and model-governance teams building checklists of GenAI risks and mitigants; and technology or data architects choosing benchmarks, fine-tuning methods and RAG architectures for financial deployments.
📄 Source Details
Adopting generative AI in banking, a white paper published by Kodex AI, whose site www.kodex-ai.com is printed in the page footer. Authors as printed: Thomas Kaiser (Kodex AI), Boon-Hiong Chan (Deutsche Bank) and Delane Zahoruiko (Kodex AI). No publication date or version is printed; the closing notice reads "Copyright © 2024 Kodex AI". 43 pages, English, with a table of contents and a reference list. The input was a text extraction of all 43 pages.
