<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>2683-2690</journal-id>
<journal-title><![CDATA[The Anáhuac journal]]></journal-title>
<abbrev-journal-title><![CDATA[The Anáhuac j.]]></abbrev-journal-title>
<issn>2683-2690</issn>
<publisher>
<publisher-name><![CDATA[Universidad Anáhuac del Sur S.C., Facultad de Economía y Negocios]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2683-26902024000200208</article-id>
<article-id pub-id-type="doi">10.36105/theanahuacjour.2024v24n2.2516</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[A simple credit rating prediction model for FinTech companies using SMOTE and MRMR techniques]]></article-title>
<article-title xml:lang="es"><![CDATA[Modelo sencillo para la predicción de la calificación crediticia para empresas fintech aplicando técnicas SMOTE y MRMR]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gopar Sánchez]]></surname>
<given-names><![CDATA[Jesús]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Tecnológico de Monterrey EGADE Business School ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2024</year>
</pub-date>
<volume>24</volume>
<numero>2</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2683-26902024000200208&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2683-26902024000200208&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2683-26902024000200208&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: FinTech companies have made the financial industry more efficient and have increased financial inclusion. However, it has also brought new risks to the financial system. Regulators, investors, and researchers are concerned that their financial difficulties could affect the financial system. Our study aims to delve deeper into the effectiveness of machine learning techniques in identifying early warnings of FinTech companies&#8217; credit risk impairment. Using commonly employed accounting and market measures in the literature, we created various classifiers to predict FinTech credit ratings. Classification algorithms face a challenge when the number of observations between classes is not equivalent, affecting their performance. Due to the limited size of publicly traded FinTech stocks with an issuer-level credit rating, our database has few observations and is highly imbalanced. The results of our study show that the SMOTE oversampling technique improves the predictive power of machine learning algorithms and that feature selection algorithms such as MRMR allow the generation of less complex and easierto-understand models. Our results suggest that the KNN classification algorithm has higher accuracy in predicting FinTech&#8217;s credit ratings.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen: Las empresas fintech han mejorado la eficiencia de la industria financiera y han aumentado la inclusión financiera. Sin embargo, también han incorporado nuevos riesgos al sistema financiero. Los reguladores, los inversionistas y los investigadores están preocupados de que sus dificultades financieras puedan afectar a todo el sistema financiero. Nuestro estudio tiene como objetivo profundizar en la eficacia de las técnicas de machine learning (aprendizaje automático) para identificar alertas tempranas de deterioro del riesgo crediticio de las fintech. Valiéndonos de medidas contables y de mercado comúnmente empleadas en la literatura, creamos varios clasificadores para predecir las calificaciones crediticias de las fintech. Los algoritmos de clasificación enfrentan un desafío cuando el número de observaciones entre clases no es equivalente, lo que afecta su desempeño. Debido al tamaño limitado de las fintech que cotizan en la bolsa y que tienen una calificación crediticia a nivel de emisor, nuestra base de datos incluye pocas observaciones y está muy desequilibrada. Los resultados de nuestro estudio muestran que la técnica de sobremuestreo SMOTE mejora el poder predictivo de los algoritmos de aprendizaje automático y que los algoritmos de selección de características como MRMR permiten la generación de modelos más sencillos y fáciles de entender. Nuestros resultados sugieren que los algoritmos de clasificación basados en KNN tienen mayor precisión para predecir las calificaciones crediticias de las fintech.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[FinTech]]></kwd>
<kwd lng="en"><![CDATA[Credit Rating]]></kwd>
<kwd lng="en"><![CDATA[Machine Learning]]></kwd>
<kwd lng="en"><![CDATA[SMOTE]]></kwd>
<kwd lng="en"><![CDATA[MRMR]]></kwd>
<kwd lng="es"><![CDATA[fintech]]></kwd>
<kwd lng="es"><![CDATA[calificación crediticia]]></kwd>
<kwd lng="es"><![CDATA[aprendizaje automático]]></kwd>
<kwd lng="es"><![CDATA[SMOTE]]></kwd>
<kwd lng="es"><![CDATA[MRMR]]></kwd>
</kwd-group>
</article-meta>
</front><back>
<ref-list>
<ref id="B1">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Agarwal]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[FinTech, Lending and Payment Innovation: A Review]]></article-title>
<source><![CDATA[Asia-Pacific Journal of Financial Studies]]></source>
<year>2020</year>
<volume>49</volume>
<numero>3</numero>
<issue>3</issue>
<page-range>353-67</page-range></nlm-citation>
</ref>
<ref id="B2">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Al-Shari]]></surname>
<given-names><![CDATA[H. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Lokhande]]></surname>
<given-names><![CDATA[M. A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[The Relationship Between the Risks of Adopting FinTech in Banks and their Impact on the Performance]]></article-title>
<source><![CDATA[Cogent Business and Management]]></source>
<year>2023</year>
<volume>10</volume>
<numero>1</numero>
<issue>1</issue>
</nlm-citation>
</ref>
<ref id="B3">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Altman]]></surname>
<given-names><![CDATA[E. I.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy]]></article-title>
<source><![CDATA[The Journal of Finance]]></source>
<year>1968</year>
<volume>23</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>589-609</page-range></nlm-citation>
</ref>
<ref id="B4">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Anagnostopoulos]]></surname>
<given-names><![CDATA[I.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Fintech and Regtech: Impact on Regulators and Banks]]></article-title>
<source><![CDATA[Journal of Economics and Business]]></source>
<year>2018</year>
<volume>100</volume>
<page-range>7-25</page-range></nlm-citation>
</ref>
<ref id="B5">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Bellotti]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Crook]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Support vector machines for credit scoring and discovery of significant features]]></article-title>
<source><![CDATA[Expert Systems with Applications]]></source>
<year>2009</year>
<volume>36</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>3302-8</page-range></nlm-citation>
</ref>
<ref id="B6">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Beneish]]></surname>
<given-names><![CDATA[M. D.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[The Detection of Earnings Manipulation]]></article-title>
<source><![CDATA[Financial Analysts Journal]]></source>
<year>1999</year>
<volume>55</volume>
<numero>5</numero>
<issue>5</issue>
<page-range>24-36</page-range></nlm-citation>
</ref>
<ref id="B7">
<nlm-citation citation-type="book">
<collab>Bloomberg</collab>
<source><![CDATA[Country Risk Premium]]></source>
<year>2024</year>
<publisher-name><![CDATA[Bloomberg Professional (database)]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B8">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Brownlee]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
</person-group>
<source><![CDATA[Imbalanced Classification with Python: Better Metrics, Balance Skewed Classes, and Apply Cost-Sensitive Learning]]></source>
<year>2021</year>
<publisher-name><![CDATA[Self-published]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B9">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Cevik]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[The Dark Side of the Moon? Fintech and Financial Stability]]></article-title>
<source><![CDATA[International Review of Economics]]></source>
<year>2024</year>
<volume>71</volume>
<page-range>421-33</page-range></nlm-citation>
</ref>
<ref id="B10">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Chaudhry]]></surname>
<given-names><![CDATA[S. M.]]></given-names>
</name>
<name>
<surname><![CDATA[Ahmed]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Huynh]]></surname>
<given-names><![CDATA[T. L. D.]]></given-names>
</name>
<name>
<surname><![CDATA[Benjasak]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Tail Risk and Systemic Risk of Finance and Technology (FinTech) Firms]]></article-title>
<source><![CDATA[Technological Forecasting and Social Change]]></source>
<year>2022</year>
<volume>174</volume>
</nlm-citation>
</ref>
<ref id="B11">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Chawla]]></surname>
<given-names><![CDATA[N. v]]></given-names>
</name>
<name>
<surname><![CDATA[Bowyer]]></surname>
<given-names><![CDATA[K. W.]]></given-names>
</name>
<name>
<surname><![CDATA[Hall]]></surname>
<given-names><![CDATA[L. O.]]></given-names>
</name>
<name>
<surname><![CDATA[Kegelmeyer]]></surname>
<given-names><![CDATA[W. P.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[SMOTE: Synthetic Minority Over-Sampling Technique]]></article-title>
<source><![CDATA[Journal of Artificial Intelligence Research]]></source>
<year>2002</year>
<volume>16</volume>
<page-range>321-57</page-range></nlm-citation>
</ref>
<ref id="B12">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Dastile]]></surname>
<given-names><![CDATA[X.]]></given-names>
</name>
<name>
<surname><![CDATA[Celik]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Potsane]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Statistical and Machine Learning Models in Credit Scoring: A Systematic Literature Survey]]></article-title>
<source><![CDATA[Applied Soft Computing Journal]]></source>
<year>2020</year>
<volume>91</volume>
</nlm-citation>
</ref>
<ref id="B13">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Demirgüç-Kunt]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Klapper]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Singer]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Ansar]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
</person-group>
<source><![CDATA[The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19]]></source>
<year>2022</year>
<publisher-name><![CDATA[World Bank]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B14">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ding]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Peng]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Minimum Redundancy Feature Selection from Microarray Gene Expression Data]]></article-title>
<source><![CDATA[Journal of Bioinformatics and Computational Biology]]></source>
<year>2005</year>
<volume>3</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>185-205</page-range></nlm-citation>
</ref>
<ref id="B15">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Doumpos]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Niklis]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Zopounidis]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Andriosopoulos]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Combining Accounting Data and a Structural Model for Predicting Credit Ratings: Empirical Evidence from European Listed Firms]]></article-title>
<source><![CDATA[Journal of Banking and Finance]]></source>
<year>2015</year>
<volume>50</volume>
<page-range>599-607</page-range></nlm-citation>
</ref>
<ref id="B16">
<nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Durand]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Risk Elements in Consumer Installment Financing]]></article-title>
<source><![CDATA[National Bureau of Economy Research (NBER)]]></source>
<year>1941</year>
</nlm-citation>
</ref>
<ref id="B17">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Friedman]]></surname>
<given-names><![CDATA[J. H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Multivariate Adaptive Regression Splines]]></article-title>
<source><![CDATA[The Annals of Statistics]]></source>
<year>1991</year>
<volume>19</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>1-67</page-range></nlm-citation>
</ref>
<ref id="B18">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Galil]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
<name>
<surname><![CDATA[Hauptman]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Rosenboim]]></surname>
<given-names><![CDATA[R. L.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Prediction of Corporate Credit Ratings with Machine Learning: Simple Interpretative Models]]></article-title>
<source><![CDATA[Finance Research Letters]]></source>
<year>2023</year>
<volume>58</volume>
</nlm-citation>
</ref>
<ref id="B19">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Golbayani]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Florescu]]></surname>
<given-names><![CDATA[I.]]></given-names>
</name>
<name>
<surname><![CDATA[Chatterjee]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[A Comparative Study of Forecasting Corporate Credit Ratings Using Neural Networks, Support Vector Machines, and Decision Trees]]></article-title>
<source><![CDATA[North American Journal of Economics and Finance]]></source>
<year>2020</year>
<volume>54</volume>
</nlm-citation>
</ref>
<ref id="B20">
<nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Gupton]]></surname>
<given-names><![CDATA[G. M.]]></given-names>
</name>
<name>
<surname><![CDATA[Finger]]></surname>
<given-names><![CDATA[C.C.]]></given-names>
</name>
<name>
<surname><![CDATA[Bhatia]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Morgan]]></surname>
<given-names><![CDATA[J.P.]]></given-names>
</name>
</person-group>
<collab>Company Incorporated</collab>
<source><![CDATA[CreditMetrics&#8482;: Technical Document]]></source>
<year>1997</year>
</nlm-citation>
</ref>
<ref id="B21">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Hajek]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Michalak]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Feature Selection In Corporate Credit Rating Prediction]]></article-title>
<source><![CDATA[Knowledge-Based Systems]]></source>
<year>2013</year>
<volume>51</volume>
<page-range>72-84</page-range></nlm-citation>
</ref>
<ref id="B22">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Henrique]]></surname>
<given-names><![CDATA[B. M.]]></given-names>
</name>
<name>
<surname><![CDATA[Sobreiro]]></surname>
<given-names><![CDATA[V. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Kimura]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Literature Review: Machine Learning Techniques Applied to Financial Market Prediction]]></article-title>
<source><![CDATA[Expert Systems with Applications]]></source>
<year>2019</year>
<volume>124</volume>
<page-range>226-51</page-range></nlm-citation>
</ref>
<ref id="B23">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[J. P.]]></given-names>
</name>
<name>
<surname><![CDATA[Mirza]]></surname>
<given-names><![CDATA[N.]]></given-names>
</name>
<name>
<surname><![CDATA[Rahat]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
<name>
<surname><![CDATA[Xiong]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Machine Learning and Credit Ratings Prediction in the Age of Fourth Industrial Revolution]]></article-title>
<source><![CDATA[Technological Forecasting and Social Change]]></source>
<year>2020</year>
<volume>161</volume>
</nlm-citation>
</ref>
<ref id="B24">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Jiang]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Credit Ratings, Financial Ratios, and Equity Risk: A Decomposition Analysis Based on Moody&#8217;s, Standard &amp; Poor&#8217;s and Fitch&#8217;s ratings]]></article-title>
<source><![CDATA[Finance Research Letters]]></source>
<year>2022</year>
<volume>46</volume>
</nlm-citation>
</ref>
<ref id="B25">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Junarsin]]></surname>
<given-names><![CDATA[E.]]></given-names>
</name>
<name>
<surname><![CDATA[Pelawi]]></surname>
<given-names><![CDATA[R. Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Kristanto]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Marcelin]]></surname>
<given-names><![CDATA[I.]]></given-names>
</name>
<name>
<surname><![CDATA[Pelawi]]></surname>
<given-names><![CDATA[J. B.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Does Fintech Lending Expansion Disturb Financial System Stability? Evidence from Indonesia]]></article-title>
<source><![CDATA[Heliyon]]></source>
<year>2023</year>
<volume>9</volume>
<numero>9</numero>
<issue>9</issue>
</nlm-citation>
</ref>
<ref id="B26">
<nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Kealhofer]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[McQuown]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Vasicek]]></surname>
<given-names><![CDATA[O.]]></given-names>
</name>
</person-group>
<source><![CDATA[The KMV Model for Credit Portfolio Management]]></source>
<year>1997</year>
<publisher-name><![CDATA[KMV Corporation]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B27">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Kiff]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Kisser]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Schumacher]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Rating Through-the-Cycle: What Does the Concept Imply for Rating Stability and Accuracy?]]></article-title>
<source><![CDATA[IMF Working Paper]]></source>
<year>2013</year>
<volume>13</volume>
<numero>64</numero>
<issue>64</issue>
</nlm-citation>
</ref>
<ref id="B28">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Merton]]></surname>
<given-names><![CDATA[R.C.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[On The Pricing Of Corporate Debt: The Risk Structure Of Interest Rates]]></article-title>
<source><![CDATA[The Journal of Finance]]></source>
<year>1974</year>
<volume>29</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>449-70</page-range></nlm-citation>
</ref>
<ref id="B29">
<nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Metz]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Cantor]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
</person-group>
<source><![CDATA[The Distribution of Common Financial Ratios by Rating and Industry for North American Non-Financial Corporations: July 2006, Moody&#8217;s Special Comment]]></source>
<year>2006</year>
</nlm-citation>
</ref>
<ref id="B30">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Milian]]></surname>
<given-names><![CDATA[E. Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Spinola]]></surname>
<given-names><![CDATA[M. de M.]]></given-names>
</name>
<name>
<surname><![CDATA[Carvalho]]></surname>
<given-names><![CDATA[M. M. de]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Fintechs: A Literature Review and Research Agenda]]></article-title>
<source><![CDATA[Electronic Commerce Research and Applications]]></source>
<year>2019</year>
<volume>34</volume>
</nlm-citation>
</ref>
<ref id="B31">
<nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Siriseriwan]]></surname>
<given-names><![CDATA[W.]]></given-names>
</name>
</person-group>
<source><![CDATA[Smotefamily: A Collection of Oversampling Techniques for Class Imbalance Problem Based on SMOTE]]></source>
<year>2021</year>
</nlm-citation>
</ref>
<ref id="B32">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Snoek]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Larochelle]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Adams]]></surname>
<given-names><![CDATA[R P.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Practical Bayesian Optimization of Machine Learning Algorithms]]></article-title>
<source><![CDATA[arXiv]]></source>
<year>2012</year>
</nlm-citation>
</ref>
<ref id="B33">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Sundar]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Punniyamoorthy]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Performance Enhanced Boosted SVM for Imbalanced Datasets]]></article-title>
<source><![CDATA[Applied Soft Computing Journal]]></source>
<year>2019</year>
<volume>83</volume>
</nlm-citation>
</ref>
<ref id="B34">
<nlm-citation citation-type="journal">
<collab>Statista</collab>
<article-title xml:lang=""><![CDATA[Transaction Value of Fintech Industry Worldwide from 2018 to 2023, with Forecasts from 2024 to 2028, by Segment (in Trillion U.S. dollars)]]></article-title>
<source><![CDATA[Statista Digital Market Insights]]></source>
<year>2023</year>
</nlm-citation>
</ref>
<ref id="B35">
<nlm-citation citation-type="journal">
<collab>Statista</collab>
<article-title xml:lang=""><![CDATA[Number of Fintech Users Worldwide from 2023 to 2023, with Forecasts from 2024 to 2028, by Segment (in Billions)]]></article-title>
<source><![CDATA[Statista Digital Market Insights]]></source>
<year>2024</year>
</nlm-citation>
</ref>
<ref id="B36">
<nlm-citation citation-type="journal">
<collab>Statista</collab>
<article-title xml:lang=""><![CDATA[Number of Fintechs Worldwide from 2018 to 2024, by Regio]]></article-title>
<source><![CDATA[Statista]]></source>
<year>2024</year>
</nlm-citation>
</ref>
<ref id="B37">
<nlm-citation citation-type="">
<collab>S&amp;P Global</collab>
<source><![CDATA[Understanding Credit Ratings]]></source>
<year>2024</year>
</nlm-citation>
</ref>
<ref id="B38">
<nlm-citation citation-type="">
<collab>S&amp;P Capital IQ</collab>
<source><![CDATA[Payment Processors; Payment Service Providers and Gateways; Mobile Wallets; Payments Fraud Management; Money Transfer and Remittance; Payments Infrastructure: Public company profile]]></source>
<year>2024</year>
</nlm-citation>
</ref>
<ref id="B39">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Tello-Gamarra]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Campos-Teixeira]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Longaray]]></surname>
<given-names><![CDATA[A. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Reis]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Hernani-Merino]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Fintechs and Institutions: A Systematic Literature Review and Future Research Agenda]]></article-title>
<source><![CDATA[Journal of Theoretical and Applied Electronic Commerce Research]]></source>
<year>2022</year>
<volume>17</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>722-50</page-range></nlm-citation>
</ref>
<ref id="B40">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Treu]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Elss]]></surname>
<given-names><![CDATA[V. I.]]></given-names>
</name>
<name>
<surname><![CDATA[Buono]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
<name>
<surname><![CDATA[Winkler]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[The Rising of Fintech-How the Tech Revolution in Financial Services Represents a Paradigm Shift]]></article-title>
<source><![CDATA[Journal of International Business and Management]]></source>
<year>2021</year>
<volume>4</volume>
<numero>5</numero>
<issue>5</issue>
<page-range>1-8</page-range></nlm-citation>
</ref>
<ref id="B41">
<nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wilson]]></surname>
<given-names><![CDATA[T. C.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[Portfolio Credit Risk]]></article-title>
<source><![CDATA[Economic Policy Review]]></source>
<year>1998</year>
<volume>4</volume>
<numero>3</numero>
<issue>3</issue>
</nlm-citation>
</ref>
</ref-list>
</back>
</article>
