Explanations of Machine Learning predictions: a mandatory step for its
application to Operational Processes
- URL: http://arxiv.org/abs/2012.15103v1
- Date: Wed, 30 Dec 2020 10:27:59 GMT
- Title: Explanations of Machine Learning predictions: a mandatory step for its
application to Operational Processes
- Authors: Giorgio Visani, Federico Chesani, Enrico Bagli, Davide Capuzzo and
Alessandro Poluzzi
- Abstract summary: Credit Risk Modelling plays a paramount role.
Recent machine and deep learning techniques have been applied to the task.
We suggest to use LIME technique to tackle the explainability problem in this field.
- Score: 61.20223338508952
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In the global economy, credit companies play a central role in economic
development, through their activity as money lenders. This important task comes
with some drawbacks, mainly the risk of the debtors not being able to repay the
provided credit. Therefore, Credit Risk Modelling (CRM), namely the evaluation
of the probability that a debtor will not repay the due amount, plays a
paramount role. Statistical approaches have been successfully exploited since
long, becoming the most used methods for CRM. Recently, also machine and deep
learning techniques have been applied to the CRM task, showing an important
increase in prediction quality and performances. However, such techniques
usually do not provide reliable explanations for the scores they come up with.
As a consequence, many machine and deep learning techniques fail to comply with
western countries' regulations such as, for example, GDPR. In this paper we
suggest to use LIME (Local Interpretable Model-agnostic Explanations) technique
to tackle the explainability problem in this field, we show its employment on a
real credit-risk dataset and eventually discuss its soundness and the necessary
improvements to guarantee its adoption and compliance with the task.
Related papers
- Large Language Models Must Be Taught to Know What They Don't Know [97.90008709512921]
We show that fine-tuning on a small dataset of correct and incorrect answers can create an uncertainty estimate with good generalization and small computational overhead.
We also investigate the mechanisms that enable reliable uncertainty estimation, finding that many models can be used as general-purpose uncertainty estimators.
arXiv Detail & Related papers (2024-06-12T16:41:31Z) - A machine learning workflow to address credit default prediction [0.44943951389724796]
Credit default prediction (CDP) plays a crucial role in assessing the creditworthiness of individuals and businesses.
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.
arXiv Detail & Related papers (2024-03-06T15:30:41Z) - Empowering Many, Biasing a Few: Generalist Credit Scoring through Large
Language Models [53.620827459684094]
Large Language Models (LLMs) have great potential for credit scoring tasks, with strong generalization ability across multiple tasks.
We propose the first open-source comprehensive framework for exploring LLMs for credit scoring.
We then propose the first Credit and Risk Assessment Large Language Model (CALM) by instruction tuning, tailored to the nuanced demands of various financial risk assessment tasks.
arXiv Detail & Related papers (2023-10-01T03:50:34Z) - Benchmarking Automated Machine Learning Methods for Price Forecasting
Applications [58.720142291102135]
We show the possibility of substituting manually created ML pipelines with automated machine learning (AutoML) solutions.
Based on the CRISP-DM process, we split the manual ML pipeline into a machine learning and non-machine learning part.
We show in a case study for the industrial use case of price forecasting, that domain knowledge combined with AutoML can weaken the dependence on ML experts.
arXiv Detail & Related papers (2023-04-28T10:27:38Z) - Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement
Learning Approach [130.9259586568977]
We propose novel learning algorithms to recover the dynamic Vickrey-Clarke-Grove (VCG) mechanism over multiple rounds of interaction.
A key contribution of our approach is incorporating reward-free online Reinforcement Learning (RL) to aid exploration over a rich policy space.
arXiv Detail & Related papers (2022-02-25T16:17:23Z) - Explaining Credit Risk Scoring through Feature Contribution Alignment
with Expert Risk Analysts [1.7778609937758323]
We focus on companies credit scoring and we benchmark different machine learning models.
The aim is to build a model to predict whether a company will experience financial problems in a given time horizon.
We bring light by providing an expert-aligned feature relevance score highlighting the disagreement between a credit risk expert and a model feature attribution explanation.
arXiv Detail & Related papers (2021-03-15T12:59:15Z) - Sequential Deep Learning for Credit Risk Monitoring with Tabular
Financial Data [0.901219858596044]
We present our attempts to create a novel approach to assessing credit risk using deep learning.
We propose a new credit card transaction sampling technique to use with deep recurrent and causal convolution-based neural networks.
We show that our sequential deep learning approach using a temporal convolutional network outperformed the benchmark non-sequential tree-based model.
arXiv Detail & Related papers (2020-12-30T21:29:48Z) - Explainable AI for Interpretable Credit Scoring [0.8379286663107844]
Credit scoring helps financial experts make better decisions regarding whether or not to accept a loan application.
Regulations have added the need for model interpretability to ensure that algorithmic decisions are understandable coherent.
We present a credit scoring model that is both accurate and interpretable.
arXiv Detail & Related papers (2020-12-03T18:44:03Z) - The value of text for small business default prediction: A deep learning
approach [9.023847175654602]
It is standard policy for a loan officer to provide a textual loan assessment to mitigate limited data availability.
We exploit recent advances from the field of Deep Learning and Natural Language Processing to extract information from 60 000 textual assessments provided by a lender.
We find that the text alone is surprisingly effective for predicting default, but when combined with traditional data, it yields no additional predictive capability.
Our proposed deep learning model does, however, appear to be robust to the quality of the text and therefore suitable for partly automating the mSME lending process.
arXiv Detail & Related papers (2020-03-19T18:15:05Z) - Towards CRISP-ML(Q): A Machine Learning Process Model with Quality
Assurance Methodology [53.063411515511056]
We propose a process model for the development of machine learning applications.
The first phase combines business and data understanding as data availability oftentimes affects the feasibility of the project.
The sixth phase covers state-of-the-art approaches for monitoring and maintenance of a machine learning applications.
arXiv Detail & Related papers (2020-03-11T08:25:49Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.