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Author: Majid Bazarbash
Publisher: International Monetary Fund
ISBN: 1498314422
Category : Business & Economics
Languages : en
Pages : 34
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Book Description
Recent advances in digital technology and big data have allowed FinTech (financial technology) lending to emerge as a potentially promising solution to reduce the cost of credit and increase financial inclusion. However, machine learning (ML) methods that lie at the heart of FinTech credit have remained largely a black box for the nontechnical audience. This paper contributes to the literature by discussing potential strengths and weaknesses of ML-based credit assessment through (1) presenting core ideas and the most common techniques in ML for the nontechnical audience; and (2) discussing the fundamental challenges in credit risk analysis. FinTech credit has the potential to enhance financial inclusion and outperform traditional credit scoring by (1) leveraging nontraditional data sources to improve the assessment of the borrower’s track record; (2) appraising collateral value; (3) forecasting income prospects; and (4) predicting changes in general conditions. However, because of the central role of data in ML-based analysis, data relevance should be ensured, especially in situations when a deep structural change occurs, when borrowers could counterfeit certain indicators, and when agency problems arising from information asymmetry could not be resolved. To avoid digital financial exclusion and redlining, variables that trigger discrimination should not be used to assess credit rating.
Author: Majid Bazarbash
Publisher: International Monetary Fund
ISBN: 1498314422
Category : Business & Economics
Languages : en
Pages : 34
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Book Description
Recent advances in digital technology and big data have allowed FinTech (financial technology) lending to emerge as a potentially promising solution to reduce the cost of credit and increase financial inclusion. However, machine learning (ML) methods that lie at the heart of FinTech credit have remained largely a black box for the nontechnical audience. This paper contributes to the literature by discussing potential strengths and weaknesses of ML-based credit assessment through (1) presenting core ideas and the most common techniques in ML for the nontechnical audience; and (2) discussing the fundamental challenges in credit risk analysis. FinTech credit has the potential to enhance financial inclusion and outperform traditional credit scoring by (1) leveraging nontraditional data sources to improve the assessment of the borrower’s track record; (2) appraising collateral value; (3) forecasting income prospects; and (4) predicting changes in general conditions. However, because of the central role of data in ML-based analysis, data relevance should be ensured, especially in situations when a deep structural change occurs, when borrowers could counterfeit certain indicators, and when agency problems arising from information asymmetry could not be resolved. To avoid digital financial exclusion and redlining, variables that trigger discrimination should not be used to assess credit rating.
Author: Majid Bazarbash
Publisher: International Monetary Fund
ISBN: 1498316034
Category : Business & Economics
Languages : en
Pages : 34
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Book Description
Recent advances in digital technology and big data have allowed FinTech (financial technology) lending to emerge as a potentially promising solution to reduce the cost of credit and increase financial inclusion. However, machine learning (ML) methods that lie at the heart of FinTech credit have remained largely a black box for the nontechnical audience. This paper contributes to the literature by discussing potential strengths and weaknesses of ML-based credit assessment through (1) presenting core ideas and the most common techniques in ML for the nontechnical audience; and (2) discussing the fundamental challenges in credit risk analysis. FinTech credit has the potential to enhance financial inclusion and outperform traditional credit scoring by (1) leveraging nontraditional data sources to improve the assessment of the borrower’s track record; (2) appraising collateral value; (3) forecasting income prospects; and (4) predicting changes in general conditions. However, because of the central role of data in ML-based analysis, data relevance should be ensured, especially in situations when a deep structural change occurs, when borrowers could counterfeit certain indicators, and when agency problems arising from information asymmetry could not be resolved. To avoid digital financial exclusion and redlining, variables that trigger discrimination should not be used to assess credit rating.
Author: Ms.Ratna Sahay
Publisher: International Monetary Fund
ISBN: 1513512242
Category : Business & Economics
Languages : en
Pages : 83
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Book Description
Technology is changing the landscape of the financial sector, increasing access to financial services in profound ways. These changes have been in motion for several years, affecting nearly all countries in the world. During the COVID-19 pandemic, technology has created new opportunities for digital financial services to accelerate and enhance financial inclusion, amid social distancing and containment measures. At the same time, the risks emerging prior to COVID-19, as digital financial services developed, are becoming even more relevant.
Author: El Bachir Boukherouaa
Publisher: International Monetary Fund
ISBN: 1589063953
Category : Business & Economics
Languages : en
Pages : 35
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Book Description
This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.
Author: Mr. Dmitry Gershenson
Publisher: International Monetary Fund
ISBN: 1513592238
Category : Business & Economics
Languages : en
Pages : 77
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Book Description
Despite some improvement since 2011, Latin America and the Caribbean continue to lag behind other regions in terms of financial inclusion. There is no clear evidence that fintech developments have supported greater financial inclusion in LAC, contrary to what has been observed elsewhere in the world. Case studies by national policy experts suggest that barriers to entry in the financial sector, along with a constraining regulatory environment, may have hindered a faster adoption of fintech. However, fintech development seems to have accelerated in the wake of the COVID-19 pandemic and with the support of recent policy initiatives.
Author: Mr.Amadou N Sy
Publisher: International Monetary Fund
ISBN: 1484385667
Category : Business & Economics
Languages : en
Pages : 61
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Book Description
FinTech is a major force shaping the structure of the financial industry in sub-Saharan Africa. New technologies are being developed and implemented in sub-Saharan Africa with the potential to change the competitive landscape in the financial industry. While it raises concerns on the emergence of vulnerabilities, FinTech challenges traditional structures and creates efficiency gains by opening up the financial services value chain. Today, FinTech is emerging as a technological enabler in the region, improving financial inclusion and serving as a catalyst for the emergence of innovations in other sectors, such as agriculture and infrastructure.
Author: Yiping Huang
Publisher:
ISBN: 9781513557618
Category :
Languages : en
Pages : 42
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Book Description
Promoting credit services to small and medium-size enterprises (SMEs) has been a perennial challenge for policy makers globally due to high information costs. Recent fintech developments may be able to mitigate this problem. By leveraging big data or digital footprints on existing platforms, some big technology (BigTech) firms have extended short-term loans to millions of small firms. By analyzing 1.8 million loan transactions of a leading Chinese online bank, this paper compares the fintech approach to assessing credit risk using big data and machine learning models with the bank approach using traditional financial data and scorecard models. The study shows that the fintech approach yields better prediction of loan defaults during normal times and periods of large exogenous shocks, reflecting information and modeling advantages. BigTech's proprietary information can complement or, where necessary, substitute credit history in risk assessment, allowing unbanked firms to borrow. Furthermore, the fintech approach benefits SMEs that are smaller and in smaller cities, hence complementing the role of banks by reaching underserved customers. With more effective and balanced policy support, BigTech lenders could help promote financial inclusion worldwide.
Author: Rajat Gera
Publisher: CRC Press
ISBN: 1003804624
Category : Technology & Engineering
Languages : en
Pages : 179
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Book Description
This book covers big data, machine learning, and artificial intelligence-related technologies and how these technologies can enable the design, development, and delivery of customer-focused financial services to both corporate and retail customers, as well as how to extend the benefits to the financially excluded sections of society. Artificial Intelligence, Fintech, and Financial Inclusion describes the applications of big data and its tools such as artificial intelligence and machine learning in products and services, marketing, risk management, and business operations. It also discusses the nature, sources, forms, and tools of big data and its potential applications in many industries for competitive advantage. The primary audience for the book includes practitioners, researchers, experts, graduate students, engineers, business leaders, and analysts researching contemporary issues in the area.
Author: Roberto Moro-Visconti
Publisher: Springer Nature
ISBN: 3030803945
Category : Business & Economics
Languages : en
Pages : 281
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Book Description
Microfinance is a renowned albeit controversial solution for giving financial access to the unbanked, even if micro-transactions increase costs, limiting outreach potential. The economic and financial sustainability of Microfinance Institutions (MFIs) is a prerequisite for widening a potentially unlimited client base. Automation decreases costs, expanding the outreach potential, and improving transparency and efficiency. Technological solutions range from branchless mobile banking to geo-localization of customers, digital/social networking for group lending, blockchain validation, big data, and artificial intelligence, up to “MicroFinTech” - FinTech applications adapted to microfinance. Of interest to both scholars, students, and professors of financial technology and microfinance, this book examines these trendy solutions comprehensively, going beyond the existing literature and showing potential applications to the traditional sustainability versus outreach trade-off.
Author: Mr. Ashraf Khan
Publisher: International Monetary Fund
ISBN: 1513582348
Category : Business & Economics
Languages : en
Pages : 75
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Book Description
Based on technical assistance to central banks by the IMF’s Monetary and Capital Markets Department and Information Technology Department, this paper examines fintech and the related area of cybersecurity from the perspective of central bank risk management. The paper draws on findings from the IMF Article IV Database, selected FSAP and country cases, and gives examples of central bank risks related to fintech and cybersecurity. The paper highlights that fintech- and cybersecurity-related risks for central banks should be addressed by operationalizing sound internal risk management by establishing and strengthening an integrated risk management approach throughout the organization, including a dedicated risk management unit, ongoing sensitizing and training of Board members and staff, clear reporting lines, assessing cyber resilience and security posture, and tying risk management into strategic planning.. Given the fast-evolving nature of such risks, central banks could make use of timely and regular inputs from external experts.
