Agent-Based Mapping of Credit Risk for Sustainable Microfinance
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{"title"=>"Agent-based mapping of credit risk for sustainable microfinance", "type"=>"journal", "authors"=>[{"first_name"=>"Joung Hun", "last_name"=>"Lee", "scopus_author_id"=>"47962001600"}, {"first_name"=>"Marko", "last_name"=>"Jusup", "scopus_author_id"=>"15136099300"}, {"first_name"=>"Boris", "last_name"=>"Podobnik", "scopus_author_id"=>"7004220065"}, {"first_name"=>"Yoh", "last_name"=>"Iwasa", "scopus_author_id"=>"7202650289"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"25945790", "doi"=>"10.1371/journal.pone.0126447", "sgr"=>"84929119222", "arxiv"=>"1504.05737", "scopus"=>"2-s2.0-84929119222", "issn"=>"19326203", "pui"=>"604276363"}, "id"=>"006f50c9-c456-3d66-b342-46d256c7a4dc", "abstract"=>"Inspired by recent ideas on how the analysis of complex financial risks can benefit from analogies with independent research areas, we propose an unorthodox framework for mapping microfinance credit risk---a major obstacle to the sustainability of lenders outreaching to the poor. Specifically, using the elements of network theory, we constructed an agent-based model that obeys the stylised rules of microfinance industry. We found that in a deteriorating economic environment confounded with adverse selection, a form of latent moral hazard may cause a regime shift from a high to a low loan repayment probability. An after-the-fact recovery, when possible, required the economic environment to improve beyond that which led to the shift in the first place. These findings suggest a small set of measurable quantities for mapping microfinance credit risk and, consequently, for balancing the requirements to reasonably price loans and to operate on a fully self-financed basis. We illustrate how the proposed mapping works using a 10-year monthly data set from one of the best-known microfinance representatives, Grameen Bank in Bangladesh. Finally, we discuss an entirely new perspective for managing microfinance credit risk based on enticing spontaneous cooperation by building social capital.", "link"=>"http://www.mendeley.com/research/agentbased-mapping-credit-risk-sustainable-microfinance", "reader_count"=>25, "reader_count_by_academic_status"=>{"Student > Doctoral Student"=>1, "Researcher"=>1, "Student > Ph. D. Student"=>11, "Student > Postgraduate"=>2, "Student > Master"=>6, "Other"=>2, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1}, "reader_count_by_user_role"=>{"Student > Doctoral Student"=>1, "Researcher"=>1, "Student > Ph. D. Student"=>11, "Student > Postgraduate"=>2, "Student > Master"=>6, "Other"=>2, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Engineering"=>1, "Environmental Science"=>1, "Mathematics"=>2, "Arts and Humanities"=>1, "Business, Management and Accounting"=>4, "Physics and Astronomy"=>1, "Social Sciences"=>5, "Computer Science"=>1, "Economics, Econometrics and Finance"=>7}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Social Sciences"=>{"Social Sciences"=>5}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>7}, "Computer Science"=>{"Computer Science"=>1}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>4}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>2}, "Environmental Science"=>{"Environmental Science"=>1}, "Arts and Humanities"=>{"Arts and Humanities"=>1}}, "reader_count_by_country"=>{"Malaysia"=>1, "Estonia"=>1}, "group_count"=>0}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2056273"], "description"=>"<p>Blue dots represent agents. Black, orange, and red lines represent the connections within groups, units, and branches, respectively, whereas the gray lines represent the connections across branches. The parameter values are <i>n</i><sub>1</sub> = 6, <i>n</i><sub>2</sub> = 60, <i>n</i><sub>3</sub> = 420, and <i>n</i><sub>4</sub> = 2100, with the connectedness probabilities <i>q</i><sub>1</sub> = 1, <i>q</i><sub>2</sub> = 0.05, <i>q</i><sub>3</sub> = 0.001, and <i>q</i><sub>4</sub> = 10<sup>−4</sup>. Five distinct branches are recognizable at a glance, while a closer inspection reveals seven units per branch. Individual groups are more difficult to distinguish. For clarity, the displayed network is much smaller than the ones used in simulations.</p>", "links"=>[], "tags"=>["mapping works", "research areas", "microfinance representatives", "network theory", "Grameen Bank", "loan payment probability", "lenders outreaching", "Sustainable Microfinance", "price loans", "microfinance credit risk", "credit risk", "mapping microfinance credit risk", "regime shift", "microfinance industry"], "article_id"=>1405986, "categories"=>["Uncategorised"], "users"=>["Joung-Hun Lee", "Marko Jusup", "Boris Podobnik", "Yoh Iwasa"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0126447.g001", "stats"=>{"downloads"=>1, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Visualization_of_the_network_structure_/1405986", "title"=>"Visualization of the network structure.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-05-06 03:10:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/2056275"], "description"=>"<p>The probability of intrinsic failure, <i>p</i><sub><i>int</i></sub>, steadily increases (decreases) throughout the first (second) half of the simulation at a rate 3.854 × 10<sup>−6</sup> per time-step, symbolizing a deteriorating (recovering) economic environment. The network is seen switching between regimes with high and low fractions of active agents, <i>f</i>. Depending on the probability of extrinsic failure, <i>p</i><sub><i>ext</i></sub>, the network performance may lag behind that of the economy (red curve) or it may never return to the high-<i>f</i> regime (blue curve). Other parameter values are <i>t</i><sub><i>h</i></sub> = 0.2, <i>τ</i><sub>0</sub> = 7, and <i>σ</i> = 30. In all simulations, <i>n</i><sub>1</sub> = 6, <i>n</i><sub>2</sub> = 60, <i>n</i><sub>3</sub> = 420, with the total number of agents set to <i>n</i><sub>4</sub> ≈ 10<sup>4</sup> for computational reasons. The connectedness probabilities are <i>q</i><sub>1</sub> = 1, <i>q</i><sub>2</sub> = 0.7, and <i>q</i><sub>3</sub> = 0.05, and <i>q</i><sub>4</sub> is constrained so that the total average number of connections is 100 [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0126447#pone.0126447.ref031\" target=\"_blank\">31</a>]. The scale for <i>p</i><sub><i>int</i></sub> is reversed and magnified 32 times for easier visual comparison.</p>", "links"=>[], "tags"=>["mapping works", "research areas", "microfinance representatives", "network theory", "Grameen Bank", "loan payment probability", "lenders outreaching", "Sustainable Microfinance", "price loans", "microfinance credit risk", "credit risk", "mapping microfinance credit risk", "regime shift", "microfinance industry"], "article_id"=>1405988, "categories"=>["Uncategorised"], "users"=>["Joung-Hun Lee", "Marko Jusup", "Boris Podobnik", "Yoh Iwasa"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0126447.g002", "stats"=>{"downloads"=>2, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Time_evolution_of_the_dynamical_network_in_a_stylized_economic_downturn_/1405988", "title"=>"Time evolution of the dynamical network in a stylized economic downturn.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-05-06 03:10:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/2056278"], "description"=>"<p>(a) The performance of the dynamical network as measured by <i>f</i> converges either to a high-<i>f</i> or a low-<i>f</i> equilibrium, depending on the probability of intrinsic failure, <i>p</i><sub><i>int</i></sub>, and the initial state. The parameter <i>p</i><sub><i>int</i></sub> ranges from 0.001 (triangles) to 0.005 (stars) in increments of 0.001. The dependence on the initial state is exemplified by the dashed curves, which converge to different equilibria although both were generated with the same <i>p</i><sub><i>int</i></sub>. (b) The equilibrium states (<i>f</i>*) of the dynamical network form a hysteresis loop. Numerical results (black) compare favorably with the analytically derived high-<i>f</i> and low-<i>f</i> equilibria (red and blue curves, respectively; see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0126447#pone.0126447.e010\" target=\"_blank\">Eq (1)</a> and its limits in the text). A major consequence of hysteresis is that the recovery of the network lags that of the economy because the probability of intrinsic failure, <i>p</i><sub><i>int</i></sub>, causing a shift from a high-<i>f</i> to a low-<i>f</i> regime is larger than the corresponding probability permitting the opposite regime shift. Parameter values are <i>t</i><sub><i>h</i></sub> = 0.2, <i>p</i><sub><i>ext</i></sub> = 0.009, <i>τ</i><sub>0</sub> = 7, and <i>σ</i> = 30.</p>", "links"=>[], "tags"=>["mapping works", "research areas", "microfinance representatives", "network theory", "Grameen Bank", "loan payment probability", "lenders outreaching", "Sustainable Microfinance", "price loans", "microfinance credit risk", "credit risk", "mapping microfinance credit risk", "regime shift", "microfinance industry"], "article_id"=>1405991, "categories"=>["Uncategorised"], "users"=>["Joung-Hun Lee", "Marko Jusup", "Boris Podobnik", "Yoh Iwasa"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0126447.g003", "stats"=>{"downloads"=>1, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Two_types_of_equilibria_and_the_regime_shift_/1405991", "title"=>"Two types of equilibria and the regime shift.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-05-06 03:10:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/2056281"], "description"=>"<p>We are interested in where the real-world lender’s parameters are situated relative to the region of the phase space characterized by high instability. Our model depends on three parameters, but instead of a three-dimensional phase space, we plot risk maps in three separate two-dimensional phase planes. The model exhibits three distinct behaviors depending on the parameter values: a high-<i>f</i> regime (I), a low-<i>f</i> regime with recovery (II), and a low-<i>f</i> regime without recovery (III). (a) Phase diagram in the <i>t</i><sub><i>h</i></sub>-<i>p</i><sub><i>ext</i></sub> plane with constant </p><p></p><p></p><p><mi>τ</mi><mo>¯</mo></p><mo>=</mo><p><mi>τ</mi><mn>0</mn></p><mo>+</mo><mi>σ</mi><p></p><p></p>, where <i>τ</i><sub>0</sub> = 7 and <i>σ</i> = 30. (b) Phase diagram in the <i>t</i><sub><i>h</i></sub>-<p></p><p></p><p><mi>τ</mi><mo>¯</mo></p><p></p><p></p> plane with constant <i>p</i><sub><i>ext</i></sub> = 0.009. (c) Phase diagram in the <i>p</i><sub><i>ext</i></sub>-<p></p><p></p><p><mi>τ</mi><mo>¯</mo></p><p></p><p></p> plane with constant <i>t</i><sub><i>h</i></sub> = 0.2. The probability of intrinsic failure is <i>p</i><sub><i>int</i></sub> = 0.004. Shown are the numerical results and curves of best fit.<p></p>", "links"=>[], "tags"=>["mapping works", "research areas", "microfinance representatives", "network theory", "Grameen Bank", "loan payment probability", "lenders outreaching", "Sustainable Microfinance", "price loans", "microfinance credit risk", "credit risk", "mapping microfinance credit risk", "regime shift", "microfinance industry"], "article_id"=>1405994, "categories"=>["Uncategorised"], "users"=>["Joung-Hun Lee", "Marko Jusup", "Boris Podobnik", "Yoh Iwasa"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0126447.g004", "stats"=>{"downloads"=>5, "page_views"=>22, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Risk_maps_for_credit_risk_evaluation_/1405994", "title"=>"Risk maps for credit risk evaluation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-05-06 03:10:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/2056283"], "description"=>"<p>(a) Standard deviations of the fractions of active and extrinsically failed agents spike as the dynamical network undergoes a shift from regime I to regime II at <i>p</i><sub><i>ext</i></sub> = 0.006 (cf. Fig <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0126447#pone.0126447.g004\" target=\"_blank\">4a</a> and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0126447#pone.0126447.g004\" target=\"_blank\">4c</a>). (b) Auto-correlation at the network level due to agent failures exhibits a longer memory close to the regime shift at <i>p</i><sub><i>ext</i></sub> = 0.006 (cf. Fig <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0126447#pone.0126447.g004\" target=\"_blank\">4a</a> and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0126447#pone.0126447.g004\" target=\"_blank\">4c</a>). In detrended fluctuation analysis (DFA) the strength of the correlation is reflected in the scaling exponent, <i>α</i> (corresponding to the slope in the logarithmic plot). Lines with slopes <i>α</i> = 1.5 and <i>α</i> = 0.5 (no correlation) are plotted for easier visual comparison. Parameter values are <i>t</i><sub><i>h</i></sub> = 0.2, <i>τ</i><sub>0</sub> = 7, and <i>σ</i> = 30. The probability of intrinsic failure is <i>p</i><sub><i>int</i></sub> = 0.004.</p>", "links"=>[], "tags"=>["mapping works", "research areas", "microfinance representatives", "network theory", "Grameen Bank", "loan payment probability", "lenders outreaching", "Sustainable Microfinance", "price loans", "microfinance credit risk", "credit risk", "mapping microfinance credit risk", "regime shift", "microfinance industry"], "article_id"=>1405996, "categories"=>["Uncategorised"], "users"=>["Joung-Hun Lee", "Marko Jusup", "Boris Podobnik", "Yoh Iwasa"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0126447.g005", "stats"=>{"downloads"=>2, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Early_warning_indicators_of_a_regime_shift_/1405996", "title"=>"Early-warning indicators of a regime shift.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-05-06 03:10:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/2056284"], "description"=>"<p>(a) The data set contains monthly information on amounts due that were actually paid to Grameen bank between June 2002 and Jan 2013. A long-term trend observable in the data is extracted using a smoothing spline, <i>s</i>(<i>t</i>). The trend suggests that the model forcing is of the form </p><p></p><p></p><p><mi>p</mi></p><p><mi>i</mi><mi>n</mi><mi>t</mi></p><p></p><mo stretchy=\"false\">(</mo><mi>t</mi><mo stretchy=\"false\">)</mo><mo>=</mo><p></p><p><mi>p</mi><mo>¯</mo></p><p><mi>i</mi><mi>n</mi><mi>t</mi></p><p></p><mo>+</mo><mo>Δ</mo><p><mi>p</mi></p><p><mi>i</mi><mi>n</mi><mi>t</mi></p><p></p><mo stretchy=\"false\">(</mo><mi>t</mi><mo stretchy=\"false\">)</mo><p></p><p></p>, where <p></p><p></p><p></p><p><mi>p</mi><mo>¯</mo></p><p><mi>i</mi><mi>n</mi><mi>t</mi></p><p></p><p></p><p></p> is a constant and Δ<i>p</i><sub><i>int</i></sub>(<i>t</i>) a zero-mean smooth function of time. (b) Comparison of the data set with typical model runs reveals satisfactory agreement between the two. For the forcing term, we assume <p></p><p><mo>Δ</mo></p><p><mi>p</mi></p><p><mi>i</mi><mi>n</mi><mi>t</mi></p><p></p><mo stretchy=\"false\">(</mo><mi>t</mi><mo stretchy=\"false\">)</mo><mo>=</mo><mi>c</mi><mo stretchy=\"false\">[</mo><mi>s</mi><mo stretchy=\"false\">(</mo><mi>t</mi><mo stretchy=\"false\">)</mo><mo>−</mo><p><mi>s</mi><mo>¯</mo></p><mo stretchy=\"false\">]</mo><p></p><p></p>, where <p></p><p><mi>c</mi><mo>=</mo><mo>−</mo></p><p><mn>1</mn><mn>8</mn></p><p></p><p></p> and <p></p><p></p><p><mi>s</mi><mo>¯</mo></p><p></p><p></p> is the average of <i>s</i>(<i>t</i>). (c) Estimated model parameters place Grameen bank in regime I (x-mark) as could be expected from one of the most successful microfinance representatives. Even in the wake of 2007–2008 financial crisis, Grameen Bank continues to operate in the low risk regime (star) despite the more challenging economic environment.<p></p>", "links"=>[], "tags"=>["mapping works", "research areas", "microfinance representatives", "network theory", "Grameen Bank", "loan payment probability", "lenders outreaching", "Sustainable Microfinance", "price loans", "microfinance credit risk", "credit risk", "mapping microfinance credit risk", "regime shift", "microfinance industry"], "article_id"=>1405997, "categories"=>["Uncategorised"], "users"=>["Joung-Hun Lee", "Marko Jusup", "Boris Podobnik", "Yoh Iwasa"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0126447.g006", "stats"=>{"downloads"=>2, "page_views"=>22, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Data_set_comparison_with_model_runs_and_the_risk_map_for_Grameen_bank_/1405997", "title"=>"Data set, comparison with model runs, and the risk map for Grameen bank.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-05-06 03:10:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/2056285"], "description"=>"<p>List of mathematical symbols in alphabetical order.</p>", "links"=>[], "tags"=>["mapping works", "research areas", "microfinance representatives", "network theory", "Grameen Bank", "loan payment probability", "lenders outreaching", "Sustainable Microfinance", "price loans", "microfinance credit risk", "credit risk", "mapping microfinance credit risk", "regime shift", "microfinance industry"], "article_id"=>1405998, "categories"=>["Uncategorised"], "users"=>["Joung-Hun Lee", "Marko Jusup", "Boris Podobnik", "Yoh Iwasa"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0126447.t001", "stats"=>{"downloads"=>7, "page_views"=>64, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_List_of_mathematical_symbols_in_alphabetical_order_/1405998", "title"=>"List of mathematical symbols in alphabetical order.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-05-06 03:10:59"}

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Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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