Money Walks: Implicit Mobility Behavior and Financial Well-Being
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{"title"=>"Money walks: Implicit mobility behavior and financial well-being", "type"=>"journal", "authors"=>[{"first_name"=>"Vivek Kumar", "last_name"=>"Singh", "scopus_author_id"=>"7404651152"}, {"first_name"=>"Burcin", "last_name"=>"Bozkaya", "scopus_author_id"=>"6507076223"}, {"first_name"=>"Alex", "last_name"=>"Pentland", "scopus_author_id"=>"7102755925"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"606226449", "doi"=>"10.1371/journal.pone.0136628", "isbn"=>"1932-6203", "pmid"=>"26317339", "issn"=>"19326203", "scopus"=>"2-s2.0-84943311692", "sgr"=>"84943311692"}, "id"=>"376ade8b-2667-3dde-b3bc-79f2d9eb881a", "abstract"=>"Traditional financial decision systems (e.g. credit) had to rely on explicit individual traits like age, gender, job type, and marital status, while being oblivious to spatio-temporal mobility or the habits of the individual involved. Emerging trends in geo-aware and mobile payment systems, and the resulting \"big data,\" present an opportunity to study human consumption patterns across space and time. Taking inspiration from animal behavior studies that have reported significant interconnections between animal spatio-temporal \"foraging\" behavior and their life outcomes, we analyzed a corpus of hundreds of thousands of human economic transactions and found that financial outcomes for individuals are intricately linked with their spatio-temporal traits like exploration, engagement, and elasticity. Such features yield models that are 30% to 49% better at predicting future financial difficulties than the comparable demographic models.", "link"=>"http://www.mendeley.com/research/money-walks-implicit-mobility-behavior-financial-wellbeing", "reader_count"=>35, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>3, "Student > Doctoral Student"=>5, "Researcher"=>4, "Student > Ph. D. Student"=>9, "Student > Master"=>6, "Other"=>2, "Student > Bachelor"=>3, "Lecturer"=>1, "Professor"=>1, "Unspecified"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>3, "Student > Doctoral Student"=>5, "Researcher"=>4, "Student > Ph. D. Student"=>9, "Student > Master"=>6, "Other"=>2, "Student > Bachelor"=>3, "Lecturer"=>1, "Professor"=>1, "Unspecified"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>4, "Engineering"=>2, "Design"=>4, "Business, Management and Accounting"=>5, "Pharmacology, Toxicology and Pharmaceutical Science"=>1, "Physics and Astronomy"=>1, "Psychology"=>3, "Social Sciences"=>1, "Computer Science"=>12, "Earth and Planetary Sciences"=>1, "Economics, Econometrics and Finance"=>1}, "reader_count_by_subdiscipline"=>{"Design"=>{"Design"=>4}, "Engineering"=>{"Engineering"=>2}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>3}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Computer Science"=>{"Computer Science"=>12}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>5}, "Unspecified"=>{"Unspecified"=>4}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}}, "reader_count_by_country"=>{"United States"=>2, "Spain"=>1}, "group_count"=>4}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2233787", "https://ndownloader.figshare.com/files/2233788", "https://ndownloader.figshare.com/files/2233789", "https://ndownloader.figshare.com/files/2233790"], "description"=>"<div><p>Traditional financial decision systems (e.g. credit) had to rely on explicit individual traits like age, gender, job type, and marital status, while being oblivious to spatio-temporal mobility or the habits of the individual involved. Emerging trends in geo-aware and mobile payment systems, and the resulting “big data,” present an opportunity to study human consumption patterns across space and time. Taking inspiration from animal behavior studies that have reported significant interconnections between animal spatio-temporal “foraging” behavior and their life outcomes, we analyzed a corpus of hundreds of thousands of human economic transactions and found that financial outcomes for individuals are intricately linked with their spatio-temporal traits like <i>exploration</i>, <i>engagement</i>, and <i>elasticity</i>. Such features yield models that are 30% to 49% better at predicting future financial difficulties than the comparable demographic models.</p></div>", "links"=>[], "tags"=>["engagement", "mobility", "trend", "elasticity", "job type", "foraging", "Implicit Mobility Behavior", "opportunity", "habit", "future", "corpus", "hundred", "consumption patterns", "status", "trait", "thousand", "interconnections", "payment systems", "difficulty", "Such features", "decision systems", "data", "inspiration", "individual", "financial", "money Walks", "e.g", "exploration", "model", "life outcomes", "animal behavior studies"], "article_id"=>1527952, "categories"=>["Uncategorised"], "users"=>["Vivek Kumar Singh", "Burcin Bozkaya", "Alex Pentland"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0136628.s001", "https://dx.doi.org/10.1371/journal.pone.0136628.s002", "https://dx.doi.org/10.1371/journal.pone.0136628.s003", "https://dx.doi.org/10.1371/journal.pone.0136628.s004"], "stats"=>{"downloads"=>19, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Money_Walks_Implicit_Mobility_Behavior_and_Financial_Well_Being_/1527952", "title"=>"Money Walks: Implicit Mobility Behavior and Financial Well-Being", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-08-28 04:45:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/2233774"], "description"=>"<p>A) All three curves have high median scores, thus indicating a strong affinity for all three traits in human shopping behavior; B) cdf for <i>diversity</i>, computed over space and time, shows that the customers were a lot more diverse in terms of their times of shopping than the locations visited; C) cdf for <i>loyalty</i>, computed over space and time, shows that the customers’ three most preferred locations account for a very large proportion (~90%) of all their shopping; and D) cdf for <i>regularity</i>, computed over space and time shows that most users exhibit very similar behavioral patterns over time. However, about 10% to 25% of the users exhibit low regularity, which could be useful for identifying anomalous behavior as well as rarer financial outcomes like delinquency.</p>", "links"=>[], "tags"=>["engagement", "mobility", "trend", "elasticity", "job type", "foraging", "Implicit Mobility Behavior", "opportunity", "habit", "future", "corpus", "hundred", "consumption patterns", "status", "trait", "thousand", "interconnections", "payment systems", "difficulty", "Such features", "decision systems", "data", "inspiration", "individual", "financial", "money Walks", "e.g", "exploration", "model", "life outcomes", "animal behavior studies"], "article_id"=>1527942, "categories"=>["Uncategorised"], "users"=>["Vivek Kumar Singh", "Burcin Bozkaya", "Alex Pentland"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0136628.g001", "stats"=>{"downloads"=>1, "page_views"=>20, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Cumulative_density_functions_cdf_of_for_diversity_loyalty_and_regularity_exhibited_by_the_customers_/1527942", "title"=>"Cumulative density functions (cdf) of for diversity, loyalty, and regularity exhibited by the customers.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-08-28 04:45:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/2233777"], "description"=>"<p>(A) Significant coefficients observed based on logistic regression between the financial outcomes and the demographic and behavioral features. Individuals with lower levels of education (High School, Middle School, or Primary School) are more likely to be late for their payments and get into financial trouble. Users with higher age were marginally less likely to overspend, miss payments, or get into financial trouble. Last, male customers and married customers were less likely to miss their payments. The behavioral mobility features <i>(diversity</i>, <i>loyalty</i>, and <i>regularity</i> computed based on hours as bins) were found to be significantly associated with the considered financial outcomes. Individuals who are “regular” were more likely to pay their bills on time. Users with a high degree of diversity or loyalty were less likely to overspend, yet more likely to miss payments or get into financial trouble. In relative terms, the behavioral features were found to be more significantly associated (in terms of p-values) and contain higher predictive power (in terms of odds ratios being further away from 1.0 in either direction) as compared to the demographic features.</p>", "links"=>[], "tags"=>["engagement", "mobility", "trend", "elasticity", "job type", "foraging", "Implicit Mobility Behavior", "opportunity", "habit", "future", "corpus", "hundred", "consumption patterns", "status", "trait", "thousand", "interconnections", "payment systems", "difficulty", "Such features", "decision systems", "data", "inspiration", "individual", "financial", "money Walks", "e.g", "exploration", "model", "life outcomes", "animal behavior studies"], "article_id"=>1527945, "categories"=>["Uncategorised"], "users"=>["Vivek Kumar Singh", "Burcin Bozkaya", "Alex Pentland"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0136628.g002", "stats"=>{"downloads"=>1, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Money_Walks_Implicit_Mobility_Behavior_and_Financial_Well_Being_Fig_2_/1527945", "title"=>"Money Walks: Implicit Mobility Behavior and Financial Well-Being - Fig 2", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-08-28 04:45:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/2233778"], "description"=>"<p>Customers with lower spatial loyalty are more likely to overspend across different demographic “bands” of age and gender, thus indicating a general pattern connecting overspending and spatial loyalty. There is one outlier point in each of the two yfigures corresponding to a bin (spatial loyalty 0.20–0.29), in which there was only one customer.</p>", "links"=>[], "tags"=>["engagement", "mobility", "trend", "elasticity", "job type", "foraging", "Implicit Mobility Behavior", "opportunity", "habit", "future", "corpus", "hundred", "consumption patterns", "status", "trait", "thousand", "interconnections", "payment systems", "difficulty", "Such features", "decision systems", "data", "inspiration", "individual", "financial", "money Walks", "e.g", "exploration", "model", "life outcomes", "animal behavior studies"], "article_id"=>1527946, "categories"=>["Uncategorised"], "users"=>["Vivek Kumar Singh", "Burcin Bozkaya", "Alex Pentland"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0136628.g003", "stats"=>{"downloads"=>4, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_relationship_between_loyalty_and_overspending_as_demonstrated_across_individuals_belonging_to_A_different_age_groups_and_B_gender_/1527946", "title"=>"The relationship between loyalty and overspending, as demonstrated across individuals belonging to A) different age groups and B) gender.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-08-28 04:45:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/2233779"], "description"=>"<p>The behavioral models perform 31%, 49%, and 30% better than the corresponding demography models for predicting “financial trouble\", \"overspending\", and \"late payment\", respectively.</p>", "links"=>[], "tags"=>["engagement", "mobility", "trend", "elasticity", "job type", "foraging", "Implicit Mobility Behavior", "opportunity", "habit", "future", "corpus", "hundred", "consumption patterns", "status", "trait", "thousand", "interconnections", "payment systems", "difficulty", "Such features", "decision systems", "data", "inspiration", "individual", "financial", "money Walks", "e.g", "exploration", "model", "life outcomes", "animal behavior studies"], "article_id"=>1527947, "categories"=>["Uncategorised"], "users"=>["Vivek Kumar Singh", "Burcin Bozkaya", "Alex Pentland"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0136628.g004", "stats"=>{"downloads"=>4, "page_views"=>47, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Prediction_performance_for_different_financial_outcomes_using_a_baseline_demography_based_and_behavior_based_model_/1527947", "title"=>"Prediction performance for different financial outcomes using a baseline, demography-based, and behavior-based model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-08-28 04:45:37"}
  • {"files"=>["https://ndownloader.figshare.com/files/2233780"], "description"=>"<p>A summary of various behavioral and financial outcome features considered in this work and their descriptive statistics.</p>", "links"=>[], "tags"=>["engagement", "mobility", "trend", "elasticity", "job type", "foraging", "Implicit Mobility Behavior", "opportunity", "habit", "future", "corpus", "hundred", "consumption patterns", "status", "trait", "thousand", "interconnections", "payment systems", "difficulty", "Such features", "decision systems", "data", "inspiration", "individual", "financial", "money Walks", "e.g", "exploration", "model", "life outcomes", "animal behavior studies"], "article_id"=>1527948, "categories"=>["Uncategorised"], "users"=>["Vivek Kumar Singh", "Burcin Bozkaya", "Alex Pentland"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0136628.t001", "stats"=>{"downloads"=>2, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_summary_of_various_behavioral_and_financial_outcome_features_considered_in_this_work_and_their_descriptive_statistics_/1527948", "title"=>"A summary of various behavioral and financial outcome features considered in this work and their descriptive statistics.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-08-28 04:45:37"}
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{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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