Due to the recent increase in interest in Financial Technology (FinTech),
applications like credit default prediction (CDP) are gaining significant
industrial and academic attention. In this regard, CDP plays a crucial role in
assessing the creditworthiness of individuals and businesses, enabling lenders
to make informed decisions regarding loan approvals and risk management. In
this paper, we propose a workflow-based approach to improve CDP, which refers
to the task of assessing the probability that a borrower will default on his or
her credit obligations. The workflow consists of multiple steps, each designed
to leverage the strengths of different techniques featured in machine learning
pipelines and, thus best solve the CDP task. We employ a comprehensive and
systematic approach starting with data preprocessing using Weight of Evidence
encoding, a technique that ensures in a single-shot data scaling by removing
outliers, handling missing values, and making data uniform for models working
with different data types. Next, we train several families of learning models,
introducing ensemble techniques to build more robust models and hyperparameter
optimization via multi-objective genetic algorithms to consider both predictive
accuracy and financial aspects. Our research aims at contributing to the
FinTech industry in providing a tool to move toward more accurate and reliable
credit risk assessment, benefiting both lenders and borrowers.

我们提出了一种基于工作流的方法来改进信用违约预测，通过利用机器学习流程中不同技术的优势来解决信用违约预测任务，从数据预处理到训练多个学习模型，并引入集成技术和多目标遗传算法来获得更准确和可靠的信用风险评估。

应对信用违约预测的机器学习工作流程

A machine learning workflow to address credit default prediction

The rapid evolution of the Smart-everything movement and Artificial
Intelligence (AI) advancements have given rise to sophisticated cyber threats
that traditional methods cannot counteract. Cyber threats are extremely
critical in financial technology (FinTech) as a data-centric sector expected to
provide 24/7 services. This paper introduces a novel and refined taxonomy of
security threats in FinTech and conducts a comprehensive systematic review of
defensive strategies. Through PRISMA methodology applied to 74 selected studies
and topic modeling, we identified 11 central cyber threats, with 43 papers
detailing them, and pinpointed 9 corresponding defense strategies, as covered
in 31 papers. This in-depth analysis offers invaluable insights for
stakeholders ranging from banks and enterprises to global governmental bodies,
highlighting both the current challenges in FinTech and effective
countermeasures, as well as directions for future research.

该论文通过对 74 篇选择的研究进行 PRISMA 方法和主题建模的综合系统审查，引入了一种新颖的、精细的金融科技安全威胁分类体系，并确定了相应的 9 种防御策略，为银行、企业和全球政府机构等利益相关者提供了宝贵的洞察力，凸显了金融科技的当前挑战、有效的对策及未来研究方向。

金融科技中的网络安全威胁：系统综述

Cybersecurity threats in FinTech: A systematic review

The use of NLP in the realm of financial technology is broad and complex,
with applications ranging from sentiment analysis and named entity recognition
to question answering. Large Language Models (LLMs) have been shown to be
effective on a variety of tasks; however, no LLM specialized for the financial
domain has been reported in literature. In this work, we present BloombergGPT,
a 50 billion parameter language model that is trained on a wide range of
financial data. We construct a 363 billion token dataset based on Bloomberg's
extensive data sources, perhaps the largest domain-specific dataset yet,
augmented with 345 billion tokens from general purpose datasets. We validate
BloombergGPT on standard LLM benchmarks, open financial benchmarks, and a suite
of internal benchmarks that most accurately reflect our intended usage. Our
mixed dataset training leads to a model that outperforms existing models on
financial tasks by significant margins without sacrificing performance on
general LLM benchmarks. Additionally, we explain our modeling choices, training
process, and evaluation methodology. As a next step, we plan to release
training logs (Chronicles) detailing our experience in training BloombergGPT.

本文介绍了 BloombergGPT， 这是一个在大量金融数据上训练得到的具有 500 亿参数的语言模型。通过使用混合数据集进行训练，我们得到的模型不仅在金融任务上表现出色，还在普遍的 LLM 基准测试上得到了不错的表现，同时也解释了模型构建、训练过程和评估方法。