Skip to content

The Twin Pillars of Financial Stability: Navigating Convergence, Disruption, And Sustainability in Banking and Insurance

Aug 2026 · Zenodo (CERN European Organization for Nuclear Research)

Abstract

Abstract The global financial services sector is in the middle of a fundamental transformation. Banking and insurance—long seen as the twin pillars holding up financial stability—are no longer operating in separate silos. Instead, they are converging into a tightly integrated, technology-driven ecosystem where the boundaries between lending, risk transfer, and customer experience are rapidly dissolving. This paper explores how these two sectors are evolving together across four critical fronts: structural convergence through bancassurance and embedded finance; technological disruption driven by artificial intelligence, the Internet of Things, and blockchain; escalating systemic vulnerabilities including cyber threats and macroeconomic sensitivities; and the urgent integration of environmental, social, and governance criteria into core financial functions. Drawing on a systematic synthesis of peer-reviewed literature, regulatory publications, and industry reports, the paper offers a unified conceptual framework for understanding how financial intermediaries are rethinking their business models. The central argument is straightforward: future financial stability will not come from keeping banking and insurance apart, but from enabling them to work together—supported by agile technology, grounded in sustainable practices, and shielded by integrated risk management. The paper concludes with a candid assessment of limitations and a roadmap for future empirical research

View source

Similar papers

#artificial intelligence Open access May 2023

Evaluating the Performance of Large Language Models on GAOKAO Benchmark

GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations of such models.

Xiaotian Zhang, Chun-yan Li, Yi Zong et al. · 216 citations · ⚡17
#artificial intelligence Open access Jul 2024

Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval

This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.

Kyra Wilson, Aylin Caliskan · 131 citations · ⚡8

PRISM: Self-Pruning Intrinsic Selection Method for Training-Free Multimodal Data Selection

Empirically, PRISM reduces the end-to-end time for data selection and model tuning to just 30% of conventional pipelines, and achieves this efficiency while simultaneously enhancing performance, surpassing models fine-tuned on the full dataset across eight multimodal and three language understanding benchmarks.

Jinhe Bi, Yifan Wang, Danqi Yan et al. · 73 citations · ⚡4
#artificial intelligence Conference Open access Apr 2020

ECCOLA - a Method for Implementing Ethically Aligned AI Systems

The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.

Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson · 64 citations · ⚡6

Let the Flows Tell: Solving Graph Combinatorial Optimization Problems with GFlowNets

This paper designs Markov decision processes (MDPs) for different combinatorial problems and proposes to train conditional GFlowNets to sample from the solution space and demonstrates that GFlowNet policies can efficiently find high-quality solutions.

Dinghuai Zhang, H. Dai, Esmeralda S. Whitammer et al. · 59 citations · ⚡8

Ethically Aligned Design of Autonomous Systems: Industry viewpoint and an empirical study

An empirical study on the current state of practice in artificial intelligence ethics is conducted by means of a multiple case study of five case companies, which indicates a gap between research and practice in the area.

Ville Vakkuri, Kai-Kristian Kemell, Joni Kultanen et al. · 56 citations · ⚡6

Related blog posts

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.