leewayhertz.com-AI use cases and applications in private equity principal inv...KristiLBurns
Private equity investors traditionally relied on personal networks for deal flow, acting more as farmers than hunters. However, technological advancements, particularly in Artificial Intelligence (AI), enable investors to hunt for new opportunities proactively. Amid increasing competition for quality assets, record levels of dry powder, and soaring valuations, the best investors are becoming the best hunters.
1) The document discusses the application of artificial intelligence in finance fraud detection. It outlines key points such as different AI applications in finance, the impact of AI, and methodology.
2) AI systems use machine learning algorithms to analyze financial data and identify patterns that indicate fraudulent activity in real-time. This helps reduce fraud and financial losses.
3) The future of AI in finance fraud detection is promising, with potential applications including advanced machine learning, natural language processing, biometric authentication, and more automated risk management and investment processes.
This document discusses applications of data science in the financial sector. It begins by explaining how financial institutions generate vast amounts of data and how data science can extract valuable insights from this data to inform decision making. Some key applications discussed include using data science models to assess risk in lending, investing and insurance; detecting fraudulent transactions; predicting market trends; performing customer segmentation; and automating trading decisions. The document also outlines some potential risks of using data science like data privacy and security issues, algorithmic bias, model risk, and regulatory compliance concerns. In conclusion, it predicts that data science will continue growing in finance through predictive analytics, automation, personalization, its role in blockchain, and ensuring ethical data use.
AI for investment analysis utilizes advanced algorithms and data analytics to assess market trends, evaluate risks, and optimize investment strategies, enhancing decision-making processes for investors and financial institutions.
Benefits of AI in private equity amp principal investment.pdfStephenAmell4
AI’s role in the growth of private equity & principal investment is rapidly evolving, and its potential impact is becoming increasingly apparent. While the industry has been relatively slow to adopt AI, recent developments indicate it is gaining momentum. AI automates investment screening in private equity, conducts comprehensive due diligence, and monitors portfolio companies.
DutchMLSchool 2022 - Multi Perspective AnomaliesBigML, Inc
Multi Perspective Anomalies, by Jan W Veldsink, Master in the art of AI at Nyenrode, Rabobank, and Grio.
*Machine Learning School in The Netherlands 2022.
leewayhertz.com-AI use cases and applications in private equity principal inv...KristiLBurns
Private equity investors traditionally relied on personal networks for deal flow, acting more as farmers than hunters. However, technological advancements, particularly in Artificial Intelligence (AI), enable investors to hunt for new opportunities proactively. Amid increasing competition for quality assets, record levels of dry powder, and soaring valuations, the best investors are becoming the best hunters.
1) The document discusses the application of artificial intelligence in finance fraud detection. It outlines key points such as different AI applications in finance, the impact of AI, and methodology.
2) AI systems use machine learning algorithms to analyze financial data and identify patterns that indicate fraudulent activity in real-time. This helps reduce fraud and financial losses.
3) The future of AI in finance fraud detection is promising, with potential applications including advanced machine learning, natural language processing, biometric authentication, and more automated risk management and investment processes.
This document discusses applications of data science in the financial sector. It begins by explaining how financial institutions generate vast amounts of data and how data science can extract valuable insights from this data to inform decision making. Some key applications discussed include using data science models to assess risk in lending, investing and insurance; detecting fraudulent transactions; predicting market trends; performing customer segmentation; and automating trading decisions. The document also outlines some potential risks of using data science like data privacy and security issues, algorithmic bias, model risk, and regulatory compliance concerns. In conclusion, it predicts that data science will continue growing in finance through predictive analytics, automation, personalization, its role in blockchain, and ensuring ethical data use.
AI for investment analysis utilizes advanced algorithms and data analytics to assess market trends, evaluate risks, and optimize investment strategies, enhancing decision-making processes for investors and financial institutions.
Benefits of AI in private equity amp principal investment.pdfStephenAmell4
AI’s role in the growth of private equity & principal investment is rapidly evolving, and its potential impact is becoming increasingly apparent. While the industry has been relatively slow to adopt AI, recent developments indicate it is gaining momentum. AI automates investment screening in private equity, conducts comprehensive due diligence, and monitors portfolio companies.
DutchMLSchool 2022 - Multi Perspective AnomaliesBigML, Inc
Multi Perspective Anomalies, by Jan W Veldsink, Master in the art of AI at Nyenrode, Rabobank, and Grio.
*Machine Learning School in The Netherlands 2022.
The document discusses the use of artificial intelligence in finance fraud detection. It begins with an introduction on AI and its increasing use in the finance industry. It then discusses different applications of AI in finance fraud detection such as real-time transaction monitoring, pattern recognition, and machine learning. The document also covers the impact of AI on fraud detection through improved accuracy, efficiency and effectiveness. Finally, it discusses future scopes of AI including advanced machine learning algorithms and natural language processing.
This document summarizes the key findings of a survey conducted by Finextra and AdviceRobo on the rise of credit robos:
1. The majority of lenders agreed that credit consumption will continue to grow but that lenders should also play a role in protecting customers from overconsumption. There was disagreement on the primary causes of current credit problems.
2. Respondents agreed that unstructured data can improve risk profiles, especially for those with thin credit files, but most lenders still rely primarily on structured data for credit scoring. Half agreed advice robos can help define credit risk more effectively.
3. Lenders face cost pressures forcing infrastructure rethinking. Speed of loan decisions and fulfillment
This document provides an overview of predictive analytics. It discusses what predictive analytics is and how it is used by organizations to make smarter decisions about customers. Predictive analytics uses historical data and statistical techniques to predict future outcomes and automate decisions. Examples are given of how predictive analytics has helped industries like financial services, insurance, telecommunications, retail, and healthcare improve customer decisions and outcomes.
How Insurers Can Harness Artificial IntelligenceCognizant
Once science fiction, artificial intelligence now holds vast potential for insurers interested in reinventing their business models and transforming customer experience.
Unlocking Generative AIs Power in Asset Management.pdfcelinedion89121
Generative AI has the potential to revolutionize asset management by analyzing vast amounts of data to identify patterns and trends, enabling more accurate predictions, risk assessments, and investment decisions. It can optimize portfolios, generate personalized investment strategies, and streamline processes like regulatory compliance. Major asset managers are implementing generative AI to augment analyst research, power robo-advisors, and blend machine learning with human expertise for improved decision-making. The use of generative AI in asset management is expected to grow, with benefits including more customized portfolios, advanced risk management capabilities, and integrated ESG investing.
solulab.com-Unlocking Generative AIs Power in Asset Management.pdfSoluLab1231
Generative AI, or GenAI, has the power to revolutionize the asset management sector.
Think of GenAI as a creative machine. Its underlying models soak in vast amounts of information, grasp context and meaning, answer abstract questions, and even generate new information, such as text and images.
These models learn rapidly. When deployed on a large scale, GenAI is in a prime position to improve asset management—a knowledge-based industry where information is consumed, processed, and created, and where trillions of dollars in client assets are managed.
This article delves into the various advantages of Generative AI. It demonstrates how GenAI empowers asset managers and firms in asset servicing to foster strategic growth, improve decision-making, and provide unparalleled client experiences.
Generative Artificial Intelligence (AI) is a creative force that enables the generation of fresh content through text descriptions, existing images, video, or audio. It employs sophisticated algorithms to discern underlying patterns in the source material. By blending these identified patterns with their interpretations, Generative AI produces unique and representative artworks. The sources for this creativity can be explicitly provided assets or inferred from a text description, functioning as a specification or brief.
Unlocking Generative AIs Power in Asset Management.pdfmatthew09cyrus
Generative AI, or GenAI, has the power to revolutionize the asset management sector.
Think of GenAI as a creative machine. Its underlying models soak in vast amounts of information, grasp context and meaning, answer abstract questions, and even generate new information, such as text and images.
These models learn rapidly. When deployed on a large scale, GenAI is in a prime position to improve asset management—a knowledge-based industry where information is consumed, processed, and created, and where trillions of dollars in client assets are managed.
This article delves into the various advantages of Generative AI. It demonstrates how GenAI empowers asset managers and firms in asset servicing to foster strategic growth, improve decision-making, and provide unparalleled client experiences.
Unlocking Generative AIs Power in Asset Management.pdfSoluLab1231
Generative AI, or GenAI, has the power to revolutionize the asset management sector.
Think of GenAI as a creative machine. Its underlying models soak in vast amounts of information, grasp context and meaning, answer abstract questions, and even generate new information, such as text and images.
These models learn rapidly. When deployed on a large scale, GenAI is in a prime position to improve asset management—a knowledge-based industry where information is consumed, processed, and created, and where trillions of dollars in client assets are managed.
This article delves into the various advantages of Generative AI. It demonstrates how GenAI empowers asset managers and firms in asset servicing to foster strategic growth, improve decision-making, and provide unparalleled client experiences.
Generative Artificial Intelligence (AI) is a creative force that enables the generation of fresh content through text descriptions, existing images, video, or audio. It employs sophisticated algorithms to discern underlying patterns in the source material. By blending these identified patterns with their interpretations, Generative AI produces unique and representative artworks. The sources for this creativity can be explicitly provided assets or inferred from a text description, functioning as a specification or brief.
For example: Adobe Firefly generates images, showcasing the potential of Generative AI.
6 use cases of machine learning in Finance Swathi Young
The use of Artificial Intelligence and Machine learning is increasingly adopted in multiple industries. Question is, does a regulated industry like Finance adopt AI/ML? the answer is a huge YES! Here we take a look at 6 different use cases:
* Chatbots
* RoboAdvisors
* Risk scoring
* Fraud Detection
* Insurance claims
* Underwriting
* regulatory compliance
EMBRACING THE REVOLUTION: GENERATIVE AI AND SYNTHETIC DATA’S IMPACT ON FINANCEShaheen Kumar
Modern finance is characterized by rapid decision-making and data reliance.
Technological advancements, particularly Generative AI, drive this innovation.
Synthetic data emerges as a pivotal tool in transforming the financial landscape.
Artificial Intelligence for Banking Fraud PreventionJérôme Kehrli
Artificial Intelligence at NetGuardians:
"From skepticism to large scale adoption towards fraud prevention"
Slides of my speech at the EPFL / EMBA Innovation Leader 2018 event.
This document discusses how artificial intelligence is transforming the financial services industry. It begins by describing how AI technologies like chatbots, robotic process automation, and augmented intelligence are automating tasks and creating hybrid digital-human workforces. This reduces costs and processing times. The document also discusses how fintech partnerships are bringing new digitally-based processes and helping traditional financial institutions innovate. Finally, it explains that while AI provides opportunities, financial institutions must invest in integrating technologies and developing new operating models to fully realize the benefits of AI.
Revolutionizing your Business with AI (AUC VLabs).pdfOmar Maher
"Revolutionizing your Business with AI" is a comprehensive yet digestible overview of Artificial Intelligence and Machine Learning. This presentation elucidates their fundamental concepts, showcases real-world applications, and equips attendees with strategic tools like the AI Idea Canvas and Evaluation Template. Whether you're a business leader or an intrigued learner, this presentation simplifies AI, aiding you in confidently navigating its transformative landscape.
Responsible AI: An Example AI Development Process with Focus on Risks and Con...Patrick Van Renterghem
Organisations need to make sure that they use AI in an appropriate way. Martijn and Hugo explain how to ensure that the developments are ethically sound and comply with regulations, how to have end-to-end governance, and how to address bias and fairness, interpretability and explainability, and robustness and security.
During the conference, we looked at an example AI development process with focussing on the risks to be managed and the controls that can be established.
Bank offered rate based on Artificial IntelligenceIJAEMSJORNAL
The rise of event streaming in financial services is growing like crazy. Continuous real-time data integration and AI processing are mandatory for many use cases. Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision.
5 AI Solutions Every Chief Risk Officer NeedsAlisa Karybina
For the risk manager, AI means greater efficiency, lower costs, and less risk. There are many potential applications of AI when it comes to managing risk in banking, but this report will focus on five key solutions with huge potential ROI that every chief risk officer (CRO) can begin building immediately. Representing foundational capabilities for risk management, these five solutions have the potential to substantially impact a bank’s financial results, and an automated machine learning platform represents the most efficient and effective method of delivering on the promise of these AI use cases.
The insurance industry – from product development to underwriting to claims – is being fundamentally transformed by AI technologies. Although some companies are investing aggressively in AI to slash costs while also enhancing the customer experience, most insurers will need to accelerate their efforts or risk discovering that it has become too late to catch up.
The document discusses the use of artificial intelligence in finance fraud detection. It begins with an introduction on AI and its increasing use in the finance industry. It then discusses different applications of AI in finance fraud detection such as real-time transaction monitoring, pattern recognition, and machine learning. The document also covers the impact of AI on fraud detection through improved accuracy, efficiency and effectiveness. Finally, it discusses future scopes of AI including advanced machine learning algorithms and natural language processing.
This document summarizes the key findings of a survey conducted by Finextra and AdviceRobo on the rise of credit robos:
1. The majority of lenders agreed that credit consumption will continue to grow but that lenders should also play a role in protecting customers from overconsumption. There was disagreement on the primary causes of current credit problems.
2. Respondents agreed that unstructured data can improve risk profiles, especially for those with thin credit files, but most lenders still rely primarily on structured data for credit scoring. Half agreed advice robos can help define credit risk more effectively.
3. Lenders face cost pressures forcing infrastructure rethinking. Speed of loan decisions and fulfillment
This document provides an overview of predictive analytics. It discusses what predictive analytics is and how it is used by organizations to make smarter decisions about customers. Predictive analytics uses historical data and statistical techniques to predict future outcomes and automate decisions. Examples are given of how predictive analytics has helped industries like financial services, insurance, telecommunications, retail, and healthcare improve customer decisions and outcomes.
How Insurers Can Harness Artificial IntelligenceCognizant
Once science fiction, artificial intelligence now holds vast potential for insurers interested in reinventing their business models and transforming customer experience.
Unlocking Generative AIs Power in Asset Management.pdfcelinedion89121
Generative AI has the potential to revolutionize asset management by analyzing vast amounts of data to identify patterns and trends, enabling more accurate predictions, risk assessments, and investment decisions. It can optimize portfolios, generate personalized investment strategies, and streamline processes like regulatory compliance. Major asset managers are implementing generative AI to augment analyst research, power robo-advisors, and blend machine learning with human expertise for improved decision-making. The use of generative AI in asset management is expected to grow, with benefits including more customized portfolios, advanced risk management capabilities, and integrated ESG investing.
solulab.com-Unlocking Generative AIs Power in Asset Management.pdfSoluLab1231
Generative AI, or GenAI, has the power to revolutionize the asset management sector.
Think of GenAI as a creative machine. Its underlying models soak in vast amounts of information, grasp context and meaning, answer abstract questions, and even generate new information, such as text and images.
These models learn rapidly. When deployed on a large scale, GenAI is in a prime position to improve asset management—a knowledge-based industry where information is consumed, processed, and created, and where trillions of dollars in client assets are managed.
This article delves into the various advantages of Generative AI. It demonstrates how GenAI empowers asset managers and firms in asset servicing to foster strategic growth, improve decision-making, and provide unparalleled client experiences.
Generative Artificial Intelligence (AI) is a creative force that enables the generation of fresh content through text descriptions, existing images, video, or audio. It employs sophisticated algorithms to discern underlying patterns in the source material. By blending these identified patterns with their interpretations, Generative AI produces unique and representative artworks. The sources for this creativity can be explicitly provided assets or inferred from a text description, functioning as a specification or brief.
Unlocking Generative AIs Power in Asset Management.pdfmatthew09cyrus
Generative AI, or GenAI, has the power to revolutionize the asset management sector.
Think of GenAI as a creative machine. Its underlying models soak in vast amounts of information, grasp context and meaning, answer abstract questions, and even generate new information, such as text and images.
These models learn rapidly. When deployed on a large scale, GenAI is in a prime position to improve asset management—a knowledge-based industry where information is consumed, processed, and created, and where trillions of dollars in client assets are managed.
This article delves into the various advantages of Generative AI. It demonstrates how GenAI empowers asset managers and firms in asset servicing to foster strategic growth, improve decision-making, and provide unparalleled client experiences.
Unlocking Generative AIs Power in Asset Management.pdfSoluLab1231
Generative AI, or GenAI, has the power to revolutionize the asset management sector.
Think of GenAI as a creative machine. Its underlying models soak in vast amounts of information, grasp context and meaning, answer abstract questions, and even generate new information, such as text and images.
These models learn rapidly. When deployed on a large scale, GenAI is in a prime position to improve asset management—a knowledge-based industry where information is consumed, processed, and created, and where trillions of dollars in client assets are managed.
This article delves into the various advantages of Generative AI. It demonstrates how GenAI empowers asset managers and firms in asset servicing to foster strategic growth, improve decision-making, and provide unparalleled client experiences.
Generative Artificial Intelligence (AI) is a creative force that enables the generation of fresh content through text descriptions, existing images, video, or audio. It employs sophisticated algorithms to discern underlying patterns in the source material. By blending these identified patterns with their interpretations, Generative AI produces unique and representative artworks. The sources for this creativity can be explicitly provided assets or inferred from a text description, functioning as a specification or brief.
For example: Adobe Firefly generates images, showcasing the potential of Generative AI.
6 use cases of machine learning in Finance Swathi Young
The use of Artificial Intelligence and Machine learning is increasingly adopted in multiple industries. Question is, does a regulated industry like Finance adopt AI/ML? the answer is a huge YES! Here we take a look at 6 different use cases:
* Chatbots
* RoboAdvisors
* Risk scoring
* Fraud Detection
* Insurance claims
* Underwriting
* regulatory compliance
EMBRACING THE REVOLUTION: GENERATIVE AI AND SYNTHETIC DATA’S IMPACT ON FINANCEShaheen Kumar
Modern finance is characterized by rapid decision-making and data reliance.
Technological advancements, particularly Generative AI, drive this innovation.
Synthetic data emerges as a pivotal tool in transforming the financial landscape.
Artificial Intelligence for Banking Fraud PreventionJérôme Kehrli
Artificial Intelligence at NetGuardians:
"From skepticism to large scale adoption towards fraud prevention"
Slides of my speech at the EPFL / EMBA Innovation Leader 2018 event.
This document discusses how artificial intelligence is transforming the financial services industry. It begins by describing how AI technologies like chatbots, robotic process automation, and augmented intelligence are automating tasks and creating hybrid digital-human workforces. This reduces costs and processing times. The document also discusses how fintech partnerships are bringing new digitally-based processes and helping traditional financial institutions innovate. Finally, it explains that while AI provides opportunities, financial institutions must invest in integrating technologies and developing new operating models to fully realize the benefits of AI.
Revolutionizing your Business with AI (AUC VLabs).pdfOmar Maher
"Revolutionizing your Business with AI" is a comprehensive yet digestible overview of Artificial Intelligence and Machine Learning. This presentation elucidates their fundamental concepts, showcases real-world applications, and equips attendees with strategic tools like the AI Idea Canvas and Evaluation Template. Whether you're a business leader or an intrigued learner, this presentation simplifies AI, aiding you in confidently navigating its transformative landscape.
Responsible AI: An Example AI Development Process with Focus on Risks and Con...Patrick Van Renterghem
Organisations need to make sure that they use AI in an appropriate way. Martijn and Hugo explain how to ensure that the developments are ethically sound and comply with regulations, how to have end-to-end governance, and how to address bias and fairness, interpretability and explainability, and robustness and security.
During the conference, we looked at an example AI development process with focussing on the risks to be managed and the controls that can be established.
Bank offered rate based on Artificial IntelligenceIJAEMSJORNAL
The rise of event streaming in financial services is growing like crazy. Continuous real-time data integration and AI processing are mandatory for many use cases. Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision.
5 AI Solutions Every Chief Risk Officer NeedsAlisa Karybina
For the risk manager, AI means greater efficiency, lower costs, and less risk. There are many potential applications of AI when it comes to managing risk in banking, but this report will focus on five key solutions with huge potential ROI that every chief risk officer (CRO) can begin building immediately. Representing foundational capabilities for risk management, these five solutions have the potential to substantially impact a bank’s financial results, and an automated machine learning platform represents the most efficient and effective method of delivering on the promise of these AI use cases.
The insurance industry – from product development to underwriting to claims – is being fundamentally transformed by AI technologies. Although some companies are investing aggressively in AI to slash costs while also enhancing the customer experience, most insurers will need to accelerate their efforts or risk discovering that it has become too late to catch up.
Similar to AI Barometer by Nicolas Vincent from Sailpeak (20)
The musiconn services for musicologists and music librariansJürgen Diet
These slides have been presented in a presentation by Jürgen Diet at the IAML-congress 2024 in Stellenbosch ("International Association of Music Libraries, Archives and Documentation Centers"). Jürgen Diet is the deputy head of the music department in the Bavarian State Library.
➏➌➐➋➎➌➐➑➐➒ Kalyan chart satta matka guessing resultsanammadhu484
MATKASATTABOSS.COM IS INDIA'S MOST TRUSTED NO.1 WEBSITE. WE PROVIDE YOU EXACT GUESSING OF THE MATKA RESULT BY OUR TOP GUESSER, MATKASATTABOSS.COM ALWAYS PROVIDES EXACT AND FAST MATKA RESULTS. PLAY SATTA MATKA AND BECOME SATTA KING BY THE HELP OF MATKASATTABOSS.COM. INDIA'S TOP SATTA MATKA MARKET AND THEIR FAST MATKA RESULTS. GET ALL THE RESULTS AND WIN MONEY BY PERFECT KALYAN MATKA TIPS , MATKA GUESSING BY OUR TOP GUESSER AND KALYAN RAJSHREE RAJYOG SWASTIK NATRAAJ BANGLORE BIRLA RAJDHANI MILAN TIME BAZAAR MATKA CHART .
2. 17 institutions contributed to the
questionnaire
10 in-depth interviews
5 case studies
Together, they hold a significant share of the market*
*70% of Retail Banking, 70% of Insurance and 60% of Wealth Management & Private Banking 2
3. 3
Predictive AI Generative AI
Key objectives
Identify patterns, anomalies, correlations to
predict outcomes
Create new content based on learned
patterns and rules
Use cases
Forecast customer behavior, manage risk,
detect fraud, market trends
Generate personalised customer
interactions, financial reports, marketing
copy, synthetic data
Algorithms used
Regression, time series analysis, anomaly
detection, decision trees
Large language models, GPT models,
variational autoencoders
Training data
Historical transactions, market data,
customer profiles
Financial news, regulatory documents,
customer feedback, product descriptions
Limitations
Requires high-quality data, model
interpretability can be challenging
Risk of generating inaccurate or misleading
information, potential for misuse
12. Centralised
Centralised with
CoE
Coordinated Decentralised
Resources
All AI expertise concentrated
within the central AI team.
AI specialists report to both the
CoE team and business unit
leaders.
Business units have their own AI
teams or contract external
expertise.
Business units fully responsible
for resourcing their AI projects.
Governance
Strict central control over data,
algorithms, model selection, and
deployment.
CoE sets overall AI standards and
guidelines.
Central AI team provides tools,
platforms, and best practices
guidelines.
Minimal central guidelines, focus
on data privacy and basic ethical
standards.
Project Initiation
Business units submit project
proposals, central team prioritizes
and executes.
Collaboration between business
units and CoE to define use cases.
Business units drive their own AI
initiatives, a central "steering
committee" ensures alignment
and coordination.
Business units independently
identify and execute their AI
initiatives.
12
Centralised Decentralised
CXO
Business Units
AI team
Direct reporting
Functional reporting
2.
RESOURCES
13. 13
70% of employees rarely, if ever, use AI
applications in their daily tasks.*
*2024 survey - 1300 respondents
14. 14
Figure 15: AI Act’s Risk Based Approach
High risk
Ex: Credit scoring, algorithmic
trading
2
Limited risk
Ex: Chatbots, robo-advisors
3
Minimal risk
Ex: Budgeting tool, fraud
detection, etc.
4
Unacceptable risk
Ex: Social scoring
1
Risk level Obligation
Prohibited
Conformity assessment
Transparency
obligation
No obligation
3.
REGULATION
Source: European Commission, Sailpeak
15. 15
June 2023
Agreement on AI Act by
European Parliament
March 2024
AI Act adopted by European
Parliament
June 2024
Publication of the AI Act
End of 2024
Bans on certain AI practices
like social scoring will be
enforced
Mid 2025
High-risk AI systems (e.g.
credit scoring) need to
comply
Mid 2026
All remaining regulations of
the AI Act come into effect.
Time to prepare systems, processes, conformity assessment,
documentation, etc.
Figure 16: AI Act Timeline
Source: European Commission, Sailpeak
3.
REGULATION
雅虎竞拍
煤炉代买
paypay商城
乐天二手
日本亚马逊
乐天新品
ZOZOTOWN
