International Conference on Financial Applications of Machine Learning

ICFAML

25th Feb – 26th Feb 2027 Kyoto, Japan

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Registration Summary

Conference ICFAML

Date 25th Feb – 26th Feb 2027

Location Kyoto, Japan

Participation Type Listener

Organizer Science Leagues

Registration Mode Standard / Physical

Registration Fee USD 0.00

Bank Convenience Charges (5.8%)i USD 0.00

Calculated based on 5.8% of the registration fee

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Conference Session Tracks

Aligned with UN Sustainable Development Goals

The conference's session tracks effectively support the following SDGs.

01 Predictive Analytics in Finance +

This track focuses on the application of predictive analytics techniques in financial contexts. It aims to explore innovative methodologies for forecasting financial trends and behaviors.

SDG 8 SDG 9 SDG 10

02 Fraud Detection Techniques Using Machine Learning +

This session will delve into the various machine learning algorithms employed for detecting fraudulent activities in financial transactions. Participants will discuss the effectiveness and challenges of these techniques in real-world applications.

SDG 16

03 Risk Modeling and Management +

This track examines the role of machine learning in enhancing risk modeling and management practices within financial institutions. It will cover advanced methodologies for assessing and mitigating financial risks.

SDG 8 SDG 9 SDG 11

04 Algorithmic Trading Strategies +

This session will explore the integration of machine learning algorithms in developing sophisticated algorithmic trading strategies. Discussions will include performance analysis and optimization of trading models.

SDG 8 SDG 9

05 Portfolio Optimization Techniques +

This track focuses on the application of machine learning methods for portfolio optimization. It will highlight innovative approaches to asset allocation and risk-return trade-offs.

SDG 8 SDG 9 SDG 10

06 Advancements in Credit Scoring Models +

This session will investigate the latest advancements in credit scoring methodologies using machine learning. Participants will discuss the implications of these models on lending practices and financial inclusion.

SDG 1 SDG 8 SDG 10

07 Financial Forecasting with Machine Learning +

This track will address the use of machine learning techniques for financial forecasting across various sectors. It aims to present case studies and empirical results demonstrating the effectiveness of these approaches.

SDG 8 SDG 9 SDG 10

08 Anomaly Detection in Financial Data +

This session will explore machine learning approaches for anomaly detection in financial datasets. It will focus on identifying unusual patterns that may indicate fraud or operational inefficiencies.

SDG 16

09 Regression Models in Financial Analysis +

This track will discuss the application of regression models in analyzing financial data. Participants will explore both traditional and machine learning-based regression techniques.

SDG 8 SDG 9

10 Classification Models for Financial Decision Making +

This session will focus on the development and application of classification models in financial decision-making processes. It will cover various techniques and their implications for financial outcomes.

SDG 8 SDG 9

11 Deep Learning Applications in Finance +

This track will investigate the transformative impact of deep learning technologies on financial applications. Discussions will include case studies showcasing deep learning's effectiveness in various financial domains.

SDG 9 SDG 10 SDG 11