The detection of money laundering is a very important problem, especially in the financial sector. We propose a mathematical specification of the problem in terms of a classification tree model that ”automates” expert based manual decisions. We operationally validate the model on a concrete application that originates from a large Italian bank. The application of the model to the data shows a good predictive accuracy and, even more importantly, the reduction of false positives, with respect to the ”manual” expert based activity. From an interpretational viewpoint, while some drivers of suspicious laundering activity are in line with the daily business practices of the bank’s anti money laundering operations, some others are new discoveries.
{"title":"Money laundering transaction detection with classification tree models","authors":"Paolo Giudici, Giulia Marini","doi":"10.47473/2020rmm0096","DOIUrl":"https://doi.org/10.47473/2020rmm0096","url":null,"abstract":"The detection of money laundering is a very important problem, especially in the financial sector. We propose a mathematical specification of the problem in terms of a classification tree model that ”automates” expert based manual decisions. We operationally validate the model on a concrete application that originates from a large Italian bank. The application of the model to the data shows a good predictive accuracy and, even more importantly, the reduction of false positives, with respect to the ”manual” expert based activity. From an interpretational viewpoint, while some drivers of suspicious laundering activity are in line with the daily business practices of the bank’s anti money laundering operations, some others are new discoveries.","PeriodicalId":296057,"journal":{"name":"Risk Management Magazine","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124629614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Quantitative risk management techniques should prove their efficacy when financially turbulent periods are about to occur. Along the common saying “who needs an umbrella on a sunny day?”, a theoretical model is really helpful when it carries the right suggestion at the proper time, that is when markets start behaving hecticly. The beginning of the third decade of the 21st century carried along a turmoil that severely affected worldwide economy and changed it, probably for good. A consequent and plausible research question could be this: which financial quantitative approaches can still be considered reliable? This article tries to partially answer this question by testing if the mean-variance selection model (Markowitz [16], [17]) and some of his refinements can provide some useful hints in terms of portfolio management.
{"title":"A remark on some extensions of the mean-variance portfolio selection models","authors":"Enrico Moretto","doi":"10.47473/2020rmm0097","DOIUrl":"https://doi.org/10.47473/2020rmm0097","url":null,"abstract":"Quantitative risk management techniques should prove their efficacy when financially turbulent periods are about to occur. Along the common saying “who needs an umbrella on a sunny day?”, a theoretical model is really helpful when it carries the right suggestion at the proper time, that is when markets start behaving hecticly. The beginning of the third decade of the 21st century carried along a turmoil that severely affected worldwide economy and changed it, probably for good. A consequent and plausible research question could be this: which financial quantitative approaches can still be considered reliable? This article tries to partially answer this question by testing if the mean-variance selection model (Markowitz [16], [17]) and some of his refinements can provide some useful hints in terms of portfolio management.","PeriodicalId":296057,"journal":{"name":"Risk Management Magazine","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134190161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The acceleration in the issuance of government debt since the global financial crisis has led central bankers to engineer interest rates that are historically low in nominal terms and consistently lower than inflation rates. Although the ostensible aim of this policy is to stimulate economic growth, maintaining negative real rates also goes a long way so that government debt is manageable and will decline in the long run, relative to the size of the economy. Financial institutions hold the great majority of government debt, and their books of retail and corporate loans are expanding briskly at a time when ultra-low interest rates make borrowing especially attractive. Rates paid on deposits are low, in advanced economies, even negative in the euro zone in nominal terms. That helps to offset the reduction in income that banks earn on their lending. Even so, the extreme and unique conditions resulting from persistent negative real interest rates mean that banks must take particular care to manage their interest-rate risk in the context of other risk types and the banks’ profit-and-loss analysis.
{"title":"Managing the Risks of Negative Interest Rates","authors":"I. Akkizidis","doi":"10.47473/2020rmm0094","DOIUrl":"https://doi.org/10.47473/2020rmm0094","url":null,"abstract":"The acceleration in the issuance of government debt since the global financial crisis has led central bankers to engineer interest rates that are historically low in nominal terms and consistently lower than inflation rates. Although the ostensible aim of this policy is to stimulate economic growth, maintaining negative real rates also goes a long way so that government debt is manageable and will decline in the long run, relative to the size of the economy. Financial institutions hold the great majority of government debt, and their books of retail and corporate loans are expanding briskly at a time when ultra-low interest rates make borrowing especially attractive. Rates paid on deposits are low, in advanced economies, even negative in the euro zone in nominal terms. That helps to offset the reduction in income that banks earn on their lending. Even so, the extreme and unique conditions resulting from persistent negative real interest rates mean that banks must take particular care to manage their interest-rate risk in the context of other risk types and the banks’ profit-and-loss analysis.","PeriodicalId":296057,"journal":{"name":"Risk Management Magazine","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115962665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Our study highlights a literature map on Fintech and the risks associated with this technological innovation in the financial sector. Considering all the studies published from 2014 to 2021 in "Scopus", we resort to econometric techniques to create our map. Our results show the recent attention of academics and researchers, mainly belonging to the technological and IT areas, towards Fintech. In particular, the studies focus on the issue of emerging technologies applied to investment and credit processes linked to the assessment of customer insolvency risk. For this reason, the existing analyzes adopt a mainly technical approach with very limited attention to strategic, organizational and managerial aspects typical of financial intermediation. Future studies could investigate the issue of Fintech behavior and relations with incumbent banks, as well as the risks that the applications of emerging digital technologies have on the sound and prudent management of these operators. In addition, further analysis can capture the risks of Fintech for clients, taking into account financial education. These are important aspects for the growth of Fintechs themselves, for the sustainability of the incumbent banks, with which they increasingly collaborate, and obviously for the banking supervisory authorities, attentive to the stability, efficiency and competitiveness of the financial sector as a whole.
{"title":"Fintech & Risks. A Bibliometric Analysis","authors":"V. Boscia, Valeria Stefanelli, M. Trinchera","doi":"10.47473/2020rmm0091","DOIUrl":"https://doi.org/10.47473/2020rmm0091","url":null,"abstract":"Our study highlights a literature map on Fintech and the risks associated with this technological innovation in the financial sector. Considering all the studies published from 2014 to 2021 in \"Scopus\", we resort to econometric techniques to create our map. Our results show the recent attention of academics and researchers, mainly belonging to the technological and IT areas, towards Fintech. In particular, the studies focus on the issue of emerging technologies applied to investment and credit processes linked to the assessment of customer insolvency risk. For this reason, the existing analyzes adopt a mainly technical approach with very limited attention to strategic, organizational and managerial aspects typical of financial intermediation. Future studies could investigate the issue of Fintech behavior and relations with incumbent banks, as well as the risks that the applications of emerging digital technologies have on the sound and prudent management of these operators. In addition, further analysis can capture the risks of Fintech for clients, taking into account financial education. These are important aspects for the growth of Fintechs themselves, for the sustainability of the incumbent banks, with which they increasingly collaborate, and obviously for the banking supervisory authorities, attentive to the stability, efficiency and competitiveness of the financial sector as a whole.","PeriodicalId":296057,"journal":{"name":"Risk Management Magazine","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117134560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Antonio Lugoboni, Banco Bpm, Nicola Picchiotti, A. Spuntarelli
The topic of risk aggregation arises from the need to incorporate in a single measure the overall exposure to the different risk types. In general, the methodologies adopted for the purposes of risk integration are based on the principle that the overall economic capital is lower than the simple algebraic sum of economic capital measures related to individual risks. This phenomenon, due to the existence of an imperfect correlation between the risks, determines, in line with portfolio theory, a "diversification benefit". The issue of risk allocation subsequently arises when the risk value of the diversified aggregated loss needs to be reassigned to the different risk classes. A similar issue has been solved in the framework of cooperative Game Theory, where the Shapley value provides a player-specific contribution of the total surplus generated by the coalition. The paper proposes a novel application of the Shapley formula in the ICAAP context (Pillar II - economic view). In particular, we show that the Shapley value is the unique solution to the allocation problem of an overall risk value, granting the fundamental requested properties, including the efficiency one. An exemplificative model application is reported, as well as a comparison with a benchmark methodology. The experimental part shows the advantages of the novel approach in terms of precision and reliability of the estimates. Finally, it is important to mention that the presented framework can be applied also in other contexts such as, for instance, in the risk class attribution of the operational risk.
{"title":"Risk allocation with Shapley value in the risk aggregation framework","authors":"Antonio Lugoboni, Banco Bpm, Nicola Picchiotti, A. Spuntarelli","doi":"10.47473/2020rmm0086","DOIUrl":"https://doi.org/10.47473/2020rmm0086","url":null,"abstract":"The topic of risk aggregation arises from the need to incorporate in a single measure the overall exposure to the different risk types. In general, the methodologies adopted for the purposes of risk integration are based on the principle that the overall economic capital is lower than the simple algebraic sum of economic capital measures related to individual risks. This phenomenon, due to the existence of an imperfect correlation between the risks, determines, in line with portfolio theory, a \"diversification benefit\". The issue of risk allocation subsequently arises when the risk value of the diversified aggregated loss needs to be reassigned to the different risk classes. A similar issue has been solved in the framework of cooperative Game Theory, where the Shapley value provides a player-specific contribution of the total surplus generated by the coalition. The paper proposes a novel application of the Shapley formula in the ICAAP context (Pillar II - economic view). In particular, we show that the Shapley value is the unique solution to the allocation problem of an overall risk value, granting the fundamental requested properties, including the efficiency one. An exemplificative model application is reported, as well as a comparison with a benchmark methodology. The experimental part shows the advantages of the novel approach in terms of precision and reliability of the estimates. Finally, it is important to mention that the presented framework can be applied also in other contexts such as, for instance, in the risk class attribution of the operational risk.","PeriodicalId":296057,"journal":{"name":"Risk Management Magazine","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130627938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Giorgio Ciaponi, Federico Dalbon, P. Fabris, Chiara Frigerio, Emilio Maria Longobardi, Romano Lucernati, Ivan Scarcipino Pattarello, Elena Repetto, Francesca Terrizzano, Reply
The reputation of an institution refers to its public image in terms of competence, integrity and trustworthiness, which results from the awareness of its stakeholders. The related risk, i.e. “Reputational Risk”, is defined as the current or prospective risk of a decline in profits or capital resulting from a negative perception of the financial institution image by clients, counterparties, shareholders, investors or supervisory authorities. In this scenario, the reputation and the assessment of the associated risk component represent a decisive factor for ensuring long-lasting profitability. In recent years, the importance of managing and monitoring Reputational Risk is growing in importance with supervisory authorities, but nevertheless, there are no specific guidelines yet that the institutions can follow. The lack of a precise orientation means that the risk component is still considered discretionary, subjective and highly prone to interpretation. Considering that in the economic literature there is not a universally accepted approach, the aim of the paper is to provide a quantitative and objective methodology, a Quantitative Model, to assess the Reputational Risk in order to overcome the limits of a qualitative approach, by using exclusively numerical and objective analysis drivers, and to meet the increasing attention of the supervisory authorities on the issue. The Quantitative Model structure allows firms to study and to monitor the phenomenon from a managerial point of view. This approach provides financial institutions, in particular the Risk Management Department, a model to evaluate the reputational risk arising from economic magnitudes that characterise the business model of the financial institution. This means that the quantitative Model enables financial institutions to steering possible negative situations and promptly intervening with any corrective measures or actions deemed appropriate.
{"title":"Reputational Risk for financial institutions: a proposal of quantitative approach","authors":"Giorgio Ciaponi, Federico Dalbon, P. Fabris, Chiara Frigerio, Emilio Maria Longobardi, Romano Lucernati, Ivan Scarcipino Pattarello, Elena Repetto, Francesca Terrizzano, Reply","doi":"10.47473/2020rmm0090","DOIUrl":"https://doi.org/10.47473/2020rmm0090","url":null,"abstract":"The reputation of an institution refers to its public image in terms of competence, integrity and trustworthiness, which results from the awareness of its stakeholders. The related risk, i.e. “Reputational Risk”, is defined as the current or prospective risk of a decline in profits or capital resulting from a negative perception of the financial institution image by clients, counterparties, shareholders, investors or supervisory authorities. In this scenario, the reputation and the assessment of the associated risk component represent a decisive factor for ensuring long-lasting profitability. In recent years, the importance of managing and monitoring Reputational Risk is growing in importance with supervisory authorities, but nevertheless, there are no specific guidelines yet that the institutions can follow. The lack of a precise orientation means that the risk component is still considered discretionary, subjective and highly prone to interpretation. Considering that in the economic literature there is not a universally accepted approach, the aim of the paper is to provide a quantitative and objective methodology, a Quantitative Model, to assess the Reputational Risk in order to overcome the limits of a qualitative approach, by using exclusively numerical and objective analysis drivers, and to meet the increasing attention of the supervisory authorities on the issue. The Quantitative Model structure allows firms to study and to monitor the phenomenon from a managerial point of view. This approach provides financial institutions, in particular the Risk Management Department, a model to evaluate the reputational risk arising from economic magnitudes that characterise the business model of the financial institution. This means that the quantitative Model enables financial institutions to steering possible negative situations and promptly intervening with any corrective measures or actions deemed appropriate.","PeriodicalId":296057,"journal":{"name":"Risk Management Magazine","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114421711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nicoletta Figurelli, Carlo Frazzei, Alessandro Garufi, T. Giordani, Luca Miraldi, M. Peron, Andrea Rodonò, Edoardo Siccardi, Gaetano Stellacci, Pietro Tenuta, Banca Sella, Banco Bpm, Cassa Centrale Banca
Following the publication of the regulatory framework for the Fundamental Review of the Trading Book (FRTB) by both the Basel Committee (BCBS) and the EU Regulator, the Financial Institutions have started the mandatory actions to comply with the new regulatory requirements. This article aims to provide an overview of the key challenges that banks have had to face in recent years, with a particular focus on the most significant methodological key points and the main impacts on business from the technicalities of the new regulatory framework, in order to provide guidelines and best practices on Standardized Approach (SA) topics shared between Risk Management and Front Office
{"title":"Fundamental review of the trading book: state of art on implementation of Standardised Approach","authors":"Nicoletta Figurelli, Carlo Frazzei, Alessandro Garufi, T. Giordani, Luca Miraldi, M. Peron, Andrea Rodonò, Edoardo Siccardi, Gaetano Stellacci, Pietro Tenuta, Banca Sella, Banco Bpm, Cassa Centrale Banca","doi":"10.47473/2020rmm0087","DOIUrl":"https://doi.org/10.47473/2020rmm0087","url":null,"abstract":"Following the publication of the regulatory framework for the Fundamental Review of the Trading Book (FRTB) by both the Basel Committee (BCBS) and the EU Regulator, the Financial Institutions have started the mandatory actions to comply with the new regulatory requirements. This article aims to provide an overview of the key challenges that banks have had to face in recent years, with a particular focus on the most significant methodological key points and the main impacts on business from the technicalities of the new regulatory framework, in order to provide guidelines and best practices on Standardized Approach (SA) topics shared between Risk Management and Front Office","PeriodicalId":296057,"journal":{"name":"Risk Management Magazine","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132432230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
G. Macchia, Ciia® Independent Investment Risk Analyst
It is clear the action of policy makers aimed at supporting the economic recovery, holding up consumption in the short term as well as public investments in the long terms. Furthermore, policy makers exploit a favorable monetary policy as long as inflation allows it. This effect can be surely considered a current and future issue that impacts on the levels of government debt, the sustainability and the new Fed overshooting strategy for inflation (AIT) which makes flexible the optimal 2% target. In terms of portfolio management, these effects are very negative considering both the exposure to government debt and the impact on the credit and equity assets. High levels of inflation are certainly useful in order to manage the debt in real terms, but it could turn to be a risk for portfolio management. This study aims to show how these risks linked with inflation can impact on the value of the different types of investment portfolios characterized by different levels of volatility, different asset classes and equity/corporate factor exposures. Through the application of a composite scenario on several variables, ad-hoc stress tests and scenarios, the article shows the key-role of an ex-ante risk management participation for a proper asset-allocation.
{"title":"Economic recovery and inflation risk: what is the “price” to manage debt?","authors":"G. Macchia, Ciia® Independent Investment Risk Analyst","doi":"10.47473/2020rmm0088","DOIUrl":"https://doi.org/10.47473/2020rmm0088","url":null,"abstract":"It is clear the action of policy makers aimed at supporting the economic recovery, holding up consumption in the short term as well as public investments in the long terms. Furthermore, policy makers exploit a favorable monetary policy as long as inflation allows it. This effect can be surely considered a current and future issue that impacts on the levels of government debt, the sustainability and the new Fed overshooting strategy for inflation (AIT) which makes flexible the optimal 2% target. In terms of portfolio management, these effects are very negative considering both the exposure to government debt and the impact on the credit and equity assets. High levels of inflation are certainly useful in order to manage the debt in real terms, but it could turn to be a risk for portfolio management. This study aims to show how these risks linked with inflation can impact on the value of the different types of investment portfolios characterized by different levels of volatility, different asset classes and equity/corporate factor exposures. Through the application of a composite scenario on several variables, ad-hoc stress tests and scenarios, the article shows the key-role of an ex-ante risk management participation for a proper asset-allocation.","PeriodicalId":296057,"journal":{"name":"Risk Management Magazine","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126734231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The study proposes an innovative application of Discrete Event Simulations (DES) and System Dynamics (SD) theory to the pricing of a certain kind of certificates very popular among private investors and, more generally, in the context of wealth management. The paper shows how numerical simulation software mainly used in traditional engineering, such as industrial and mechanical engineering, can be successfully adapted to the risk analysis of structured financial products. The article can be divided into three macro-sections: in the first part a synthetic overview of the most widespread option pricing models in the quantitative finance branch is given to the readers together with the fundamental technical-instrumental background of the implemented DES and SD simulator. After dealing with some of the most popular models adopted for Equity and Equity index options, which are the most common underlying assets for the certificates structuring, we move, in the second part, to describe how the mathematical models can be integrated into a general simulation environment able to provide both DES and SD extensively used in the engineering field. The core stochastic differential equation (SDE) will therefore be translated, together with all its input parameters, into a visual block model which allows an immediate quantitative analysis of how market parameters and the other model variables can change over time. The possibility for the structurer to observe how the variables evolve day-by-day gives a strong sensitivity to evaluate how the price and the associated risk measures can be directly affected. The third part of the study compares the results obtained from the simulator designed by the authors with the more traditional pricing approaches, which consist in programming Matlab® codes for the numerical integration of the core stochastic dynamics through a Euler-Maruyama scheme. The comparison includes a price check using the Bloomberg® DLIB pricing module and a check directly against the valuation provided by the counterparty. In this section, real market cases will therefore be examined with a complete quantitative analysis of two of the most widespread categories of certificates in wealth management: Multi-asset Barrier Reverse Convertible with Issuer Callability and Multi-asset Express Certificate with conditional memory fixed coupon.
{"title":"Certificate pricing using Discrete Event Simulations and System Dynamics theory","authors":"P. Giribone, R. Revetria, Banca Carige","doi":"10.47473/2020rmm0092","DOIUrl":"https://doi.org/10.47473/2020rmm0092","url":null,"abstract":"The study proposes an innovative application of Discrete Event Simulations (DES) and System Dynamics (SD) theory to the pricing of a certain kind of certificates very popular among private investors and, more generally, in the context of wealth management. The paper shows how numerical simulation software mainly used in traditional engineering, such as industrial and mechanical engineering, can be successfully adapted to the risk analysis of structured financial products. The article can be divided into three macro-sections: in the first part a synthetic overview of the most widespread option pricing models in the quantitative finance branch is given to the readers together with the fundamental technical-instrumental background of the implemented DES and SD simulator. After dealing with some of the most popular models adopted for Equity and Equity index options, which are the most common underlying assets for the certificates structuring, we move, in the second part, to describe how the mathematical models can be integrated into a general simulation environment able to provide both DES and SD extensively used in the engineering field. The core stochastic differential equation (SDE) will therefore be translated, together with all its input parameters, into a visual block model which allows an immediate quantitative analysis of how market parameters and the other model variables can change over time. The possibility for the structurer to observe how the variables evolve day-by-day gives a strong sensitivity to evaluate how the price and the associated risk measures can be directly affected. The third part of the study compares the results obtained from the simulator designed by the authors with the more traditional pricing approaches, which consist in programming Matlab® codes for the numerical integration of the core stochastic dynamics through a Euler-Maruyama scheme. The comparison includes a price check using the Bloomberg® DLIB pricing module and a check directly against the valuation provided by the counterparty. In this section, real market cases will therefore be examined with a complete quantitative analysis of two of the most widespread categories of certificates in wealth management: Multi-asset Barrier Reverse Convertible with Issuer Callability and Multi-asset Express Certificate with conditional memory fixed coupon.","PeriodicalId":296057,"journal":{"name":"Risk Management Magazine","volume":"180 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133434919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Peer-to-Peer (P2P) lending is an online lending process allowing individuals to obtain or concede loans without the interference of traditional financial intermediaries. It has grown quickly the last years, with some platforms reaching billions of dollars of loans in principal in a short amount of time. Since each loan is associated with the probability of loss due to a borrower's failure, this paper addresses the borrower's default prediction problem in the P2P financial ecosystem. The main assumption, which makes this study different from the available literature, is that borrowers sharing the same homeownership status display similar risk profile, thus a model per segment should be developed. We estimate the Probability of Default (PD) of a borrower by using Logistic Regression (LR) coupled with Weight of Evidence encoding. The features set is identified via the Sequential Feature Selection (SFS). We compare the forward against the backward SFS, in terms of the Area Under the Curve (AUC), and we choose the one that maximizes this statistic. Finally, we compare the results of the chosen LR approach against two other popular Machine Learning (ML) techniques: the k Nearest Neighbors (k-NN) and the Random Forest (RF).
{"title":"Why segmentation matters: a Machine Learning approach for predicting loan defaults in the Peer-to-Peer (P2P) Financial Ecosystem","authors":"Adamaria Perrotta, Georgios Bliatsios","doi":"10.47473/2020rmm0089","DOIUrl":"https://doi.org/10.47473/2020rmm0089","url":null,"abstract":"Peer-to-Peer (P2P) lending is an online lending process allowing individuals to obtain or concede loans without the interference of traditional financial intermediaries. It has grown quickly the last years, with some platforms reaching billions of dollars of loans in principal in a short amount of time. Since each loan is associated with the probability of loss due to a borrower's failure, this paper addresses the borrower's default prediction problem in the P2P financial ecosystem. The main assumption, which makes this study different from the available literature, is that borrowers sharing the same homeownership status display similar risk profile, thus a model per segment should be developed. We estimate the Probability of Default (PD) of a borrower by using Logistic Regression (LR) coupled with Weight of Evidence encoding. The features set is identified via the Sequential Feature Selection (SFS). We compare the forward against the backward SFS, in terms of the Area Under the Curve (AUC), and we choose the one that maximizes this statistic. Finally, we compare the results of the chosen LR approach against two other popular Machine Learning (ML) techniques: the k Nearest Neighbors (k-NN) and the Random Forest (RF).","PeriodicalId":296057,"journal":{"name":"Risk Management Magazine","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121410816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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