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#hiring *Fraud Analytics Model Validation Officer (Hybrid)*, Jacksonville, *United States*, fulltime #jobs #jobseekers #careers #Jacksonvillejobs #Floridajobs *Apply*: https://lnkd.in/dsNUkjE3 As part of the Fraud Analytics, Modeling & Intelligence organization, the Business Modeling and AI Validation Analyst (VP) manages the documentation and validation of the fraud analytics models supporting Citi's North American and global credit card and retail bank businesses. The Fraud Model Documentation & Data Governance team is responsible for documenting, reviewing, and assessing qualitative models as part of Citi's Model Risk Management framework. The main objectives are to ensure that qualitative models are used appropriately by the business and that model users are aware of the models' limitations and weaknesses that should be mitigated by compensating controls. A qualitative model is a model whose output is largely or entirely dependent upon key assumptions which are primarily qualitative in nature (but may have quantitative components, e.g., models that are partially based on expert judgment or other qualitative evidence). Qualitative models are held to the same high standard as all models at Citi.This role partners closely with Vendors, MRM, Fraud Policy, Operations and various partners to keep apprised of business and technology direction to determine potential and existing fraud impacts.Responsibilities:Develops, enhances, and publishes Model/AI Development Documentation to obtain governance approval enabling business use while managing inherit risk.Key partner managing Fraud Model Risk including managing stakeholder interaction with model developers, Vendors, and Model Risk Management Validators across the model lifecycle including validation, ongoing performance evaluation, and annual reviews.Distill diverse and complex information from various technical sources into a comprehensive, common language documentation intended for audiences without technical or Fraud knowledge.Conducts analysis and packages it into detailed technical documentation report for validation purposes sufficient to meet regulatory guidelines and exceed industry standards.Presents model validation findings to senior management, supervisory authorities, and regulatory agencies as required.Maintain a comprehensive library of technical terminology and reference materials.Identifies modeling opportunities that yield measurable business results.Contributes to strategic, cross-functional initiatives within the Fraud Analytics organization.Appropriately assess risk when business decisions are made, demonstrating particular consideration for the firm's reputation and safeguarding Citigroup, its clients and assets, by driving compliance with applicable laws, rules and regulations, adhering to Policy, applying sound ethical judgment regarding personal behavior, conduct and business practices, an

#hiring *Fraud Analytics Model Validation Officer (Hybrid)*, Tampa, *United States*, fulltime #jobs #jobseekers #careers #Tampajobs #Floridajobs *Apply*: https://lnkd.in/gNr9cFXn As part of the Fraud Analytics, Modeling & Intelligence organization, the Business Modeling and AI Validation Analyst (VP) manages the documentation and validation of the fraud analytics models supporting Citi's North American and global credit card and retail bank businesses. The Fraud Model Documentation & Data Governance team is responsible for documenting, reviewing, and assessing qualitative models as part of Citi's Model Risk Management framework. The main objectives are to ensure that qualitative models are used appropriately by the business and that model users are aware of the models' limitations and weaknesses that should be mitigated by compensating controls. A qualitative model is a model whose output is largely or entirely dependent upon key assumptions which are primarily qualitative in nature (but may have quantitative components, e.g., models that are partially based on expert judgment or other qualitative evidence). Qualitative models are held to the same high standard as all models at Citi.This role partners closely with Vendors, MRM, Fraud Policy, Operations and various partners to keep apprised of business and technology direction to determine potential and existing fraud impacts.Responsibilities:Develops, enhances, and publishes Model/AI Development Documentation to obtain governance approval enabling business use while managing inherit risk.Key partner managing Fraud Model Risk including managing stakeholder interaction with model developers, Vendors, and Model Risk Management Validators across the model lifecycle including validation, ongoing performance evaluation, and annual reviews.Distill diverse and complex information from various technical sources into a comprehensive, common language documentation intended for audiences without technical or Fraud knowledge.Conducts analysis and packages it into detailed technical documentation report for validation purposes sufficient to meet regulatory guidelines and exceed industry standards.Presents model validation findings to senior management, supervisory authorities, and regulatory agencies as required.Maintain a comprehensive library of technical terminology and reference materials.Identifies modeling opportunities that yield measurable business results.Contributes to strategic, cross-functional initiatives within the Fraud Analytics organization.Appropriately assess risk when business decisions are made, demonstrating particular consideration for the firm's reputation and safeguarding Citigroup, its clients and assets, by driving compliance with applicable laws, rules and regulations, adhering to Policy, applying sound ethical judgment regarding personal behavior, conduct and business practices, and escalating, managin

#hiring *Fraud Analytics Model Validation Officer (Hybrid)*, San Antonio, *United States*, fulltime #jobs #jobseekers #careers #SanAntoniojobs #Texasjobs *Apply*: https://lnkd.in/gURrqd8M As part of the Fraud Analytics, Modeling & Intelligence organization, the Business Modeling and AI Validation Analyst (VP) manages the documentation and validation of the fraud analytics models supporting Citi's North American and global credit card and retail bank businesses. The Fraud Model Documentation & Data Governance team is responsible for documenting, reviewing, and assessing qualitative models as part of Citi's Model Risk Management framework. The main objectives are to ensure that qualitative models are used appropriately by the business and that model users are aware of the models' limitations and weaknesses that should be mitigated by compensating controls. A qualitative model is a model whose output is largely or entirely dependent upon key assumptions which are primarily qualitative in nature (but may have quantitative components, e.g., models that are partially based on expert judgment or other qualitative evidence). Qualitative models are held to the same high standard as all models at Citi.This role partners closely with Vendors, MRM, Fraud Policy, Operations and various partners to keep apprised of business and technology direction to determine potential and existing fraud impacts.Responsibilities: Develops, enhances, and publishes Model/AI Development Documentation to obtain governance approval enabling business use while managing inherit risk. Key partner managing Fraud Model Risk including managing stakeholder interaction with model developers, Vendors, and Model Risk Management Validators across the model lifecycle including validation, ongoing performance evaluation, and annual reviews. Distill diverse and complex information from various technical sources into a comprehensive, common language documentation intended for audiences without technical or Fraud knowledge. Conducts analysis and packages it into detailed technical documentation report for validation purposes sufficient to meet regulatory guidelines and exceed industry standards. Presents model validation findings to senior management, supervisory authorities, and regulatory agencies as required. Maintain a comprehensive library of technical terminology and reference materials. Identifies modeling opportunities that yield measurable business results. Contributes to strategic, cross-functional initiatives within the Fraud Analytics organization. Appropriately assess risk when business decisions are made, demonstrating particular consideration for the firm's reputation and safeguarding Citigroup, its clients and assets, by driving compliance with applicable laws, rules and regulations, adhering to Policy, applying sound ethical judgment regarding personal behavior, conduct and business prac

#hiring *Fraud Analytics Model Validation Analyst, AVP (Hybrid)*, Jacksonville, *United States*, fulltime #jobs #jobseekers #careers #Jacksonvillejobs #Floridajobs *Apply*: https://lnkd.in/djeDYu_p As part of the Fraud Analytics, Modeling & Intelligence organization, the Business Modeling and AI Validation Analyst (VP) manages the documentation and validation of the fraud analytics models supporting Citi's North American and global credit card and retail bank businesses. The Fraud Model Documentation & Data Governance team is responsible for documenting, reviewing, and assessing qualitative models as part of Citi's Model Risk Management framework. The main objectives are to ensure that qualitative models are used appropriately by the business and that model users are aware of the models' limitations and weaknesses that should be mitigated by compensating controls. A qualitative model is a model whose output is largely or entirely dependent upon key assumptions which are primarily qualitative in nature (but may have quantitative components, e.g., models that are partially based on expert judgment or other qualitative evidence). Qualitative models are held to the same high standard as all models at Citi.This role partners closely with Vendors, MRM, Fraud Policy, Operations and various partners to keep apprised of business and technology direction to determine potential and existing fraud impacts.Responsibilities: Qualifications: Education:Develops, enhances, and publishes Model/AI Development Documentation to obtain governance approval enabling business use while managing inherit risk.Key partner managing Fraud Model Risk including managing stakeholder interaction with model developers, Vendors, and Model Risk Management Validators across the model lifecycle including validation, ongoing performance evaluation, and annual reviews.Serve as in-business POC for fielding Model Risk Management Validators effective challenges to model/AI assumptions, mathematical formulation, and implementation.Conduct analysis and packages it into detailed technical documentation report for validation purposes sufficient to meet regulatory guidelines and exceed industry standards.Manages and mitigates model risk identified via model/AI limitations and work with Model Risk Management Validators to identify/develop/execute compensating controls.Distill diverse and complex information from various technical sources into a comprehensive, common language documentation intended for audiences without technical or Fraud knowledge.Contribute/Administer a comprehensive library of technical terminology and reference materials.Contributes to strategic, cross-functional initiatives within the Fraud Analytics organization.Appropriately assess risk when business decisions are made, demonstrating particular consideration for the firm's reputation and safeguarding Citigroup, its clients and asset

#hiring *Fraud Analytics Model Validation Analyst, AVP (Hybrid)*, San Antonio, *United States*, fulltime #jobs #jobseekers #careers #SanAntoniojobs #Texasjobs *Apply*: https://lnkd.in/g55-ZP_s As part of the Fraud Analytics, Modeling & Intelligence organization, the Business Modeling and AI Validation Analyst (VP) manages the documentation and validation of the fraud analytics models supporting Citi's North American and global credit card and retail bank businesses. The Fraud Model Documentation & Data Governance team is responsible for documenting, reviewing, and assessing qualitative models as part of Citi's Model Risk Management framework. The main objectives are to ensure that qualitative models are used appropriately by the business and that model users are aware of the models' limitations and weaknesses that should be mitigated by compensating controls. A qualitative model is a model whose output is largely or entirely dependent upon key assumptions which are primarily qualitative in nature (but may have quantitative components, e.g., models that are partially based on expert judgment or other qualitative evidence). Qualitative models are held to the same high standard as all models at Citi.This role partners closely with Vendors, MRM, Fraud Policy, Operations and various partners to keep apprised of business and technology direction to determine potential and existing fraud impacts.Responsibilities: Qualifications: Education:Develops, enhances, and publishes Model/AI Development Documentation to obtain governance approval enabling business use while managing inherit risk.Key partner managing Fraud Model Risk including managing stakeholder interaction with model developers, Vendors, and Model Risk Management Validators across the model lifecycle including validation, ongoing performance evaluation, and annual reviews.Serve as in-business POC for fielding Model Risk Management Validators effective challenges to model/AI assumptions, mathematical formulation, and implementation.Conduct analysis and packages it into detailed technical documentation report for validation purposes sufficient to meet regulatory guidelines and exceed industry standards.Manages and mitigates model risk identified via model/AI limitations and work with Model Risk Management Validators to identify/develop/execute compensating controls.Distill diverse and complex information from various technical sources into a comprehensive, common language documentation intended for audiences without technical or Fraud knowledge.Contribute/Administer a comprehensive library of technical terminology and reference materials.Contributes to strategic, cross-functional initiatives within the Fraud Analytics organization.Appropriately assess risk when business decisions are made, demonstrating particular consideration for the firm's reputation and safeguarding Citigroup, its clients and assets, by dr

#hiring *Fraud Analytics Model Validation Analyst, AVP (Hybrid)*, Tampa, *United States*, fulltime #jobs #jobseekers #careers #Tampajobs #Floridajobs *Apply*: https://lnkd.in/gCWvRBJq As part of the Fraud Analytics, Modeling & Intelligence organization, the Business Modeling and AI Validation Analyst (VP) manages the documentation and validation of the fraud analytics models supporting Citi's North American and global credit card and retail bank businesses. The Fraud Model Documentation & Data Governance team is responsible for documenting, reviewing, and assessing qualitative models as part of Citi's Model Risk Management framework. The main objectives are to ensure that qualitative models are used appropriately by the business and that model users are aware of the models' limitations and weaknesses that should be mitigated by compensating controls. A qualitative model is a model whose output is largely or entirely dependent upon key assumptions which are primarily qualitative in nature (but may have quantitative components, e.g., models that are partially based on expert judgment or other qualitative evidence). Qualitative models are held to the same high standard as all models at Citi.This role partners closely with Vendors, MRM, Fraud Policy, Operations and various partners to keep apprised of business and technology direction to determine potential and existing fraud impacts.Responsibilities: Qualifications: Education: Develops, enhances, and publishes Model/AI Development Documentation to obtain governance approval enabling business use while managing inherit risk. Key partner managing Fraud Model Risk including managing stakeholder interaction with model developers, Vendors, and Model Risk Management Validators across the model lifecycle including validation, ongoing performance evaluation, and annual reviews. Serve as in-business POC for fielding Model Risk Management Validators effective challenges to model/AI assumptions, mathematical formulation, and implementation. Conduct analysis and packages it into detailed technical documentation report for validation purposes sufficient to meet regulatory guidelines and exceed industry standards. Manages and mitigates model risk identified via model/AI limitations and work with Model Risk Management Validators to identify/develop/executecompensating controls. Distill diverse and complex information from various technical sources into a comprehensive, common language documentation intended for audiences without technical or Fraud knowledge. Contribute/Administera comprehensive library of technical terminology and reference materials. Contributes to strategic, cross-functional initiatives within the Fraud Analytics organization. Appropriately assess risk when business decisions are made, demonstrating particular consideration for the firm's reputation and safeguarding Citigroup, its clients and assets, by

#hiring *Fraud Analytics Model Validation Officer (Hybrid)*, San Antonio, *United States*, fulltime #jobs #jobseekers #careers #SanAntoniojobs #Texasjobs *Apply*: https://lnkd.in/gURrqd8M As part of the Fraud Analytics, Modeling & Intelligence organization, the Business Modeling and AI Validation Analyst (VP) manages the documentation and validation of the fraud analytics models supporting Citi's North American and global credit card and retail bank businesses. The Fraud Model Documentation & Data Governance team is responsible for documenting, reviewing, and assessing qualitative models as part of Citi's Model Risk Management framework. The main objectives are to ensure that qualitative models are used appropriately by the business and that model users are aware of the models' limitations and weaknesses that should be mitigated by compensating controls. A qualitative model is a model whose output is largely or entirely dependent upon key assumptions which are primarily qualitative in nature (but may have quantitative components, e.g., models that are partially based on expert judgment or other qualitative evidence). Qualitative models are held to the same high standard as all models at Citi.This role partners closely with Vendors, MRM, Fraud Policy, Operations and various partners to keep apprised of business and technology direction to determine potential and existing fraud impacts.Responsibilities: Develops, enhances, and publishes Model/AI Development Documentation to obtain governance approval enabling business use while managing inherit risk. Key partner managing Fraud Model Risk including managing stakeholder interaction with model developers, Vendors, and Model Risk Management Validators across the model lifecycle including validation, ongoing performance evaluation, and annual reviews. Distill diverse and complex information from various technical sources into a comprehensive, common language documentation intended for audiences without technical or Fraud knowledge. Conducts analysis and packages it into detailed technical documentation report for validation purposes sufficient to meet regulatory guidelines and exceed industry standards. Presents model validation findings to senior management, supervisory authorities, and regulatory agencies as required. Maintain a comprehensive library of technical terminology and reference materials. Identifies modeling opportunities that yield measurable business results. Contributes to strategic, cross-functional initiatives within the Fraud Analytics organization. Appropriately assess risk when business decisions are made, demonstrating particular consideration for the firm's reputation and safeguarding Citigroup, its clients and assets, by driving compliance with applicable laws, rules and regulations, adhering to Policy, applying sound ethical judgment regarding personal behavior, conduct and business prac

Dear all, ICONIC's client is #urgent hiring an ANTI - FRAUD SPECIALIST (#BNPL, #Strategy, #Policy, #ArchitectureDesign) *Location: Dist 1, HCMC *Salary: 50 - 60mil Gross *The ideal candidate will have extensive experience in #frauddetection, #creditrisk management, and working with real-time #transaction monitoring systems. < Job Description > Key Responsibilities: - Fraud Strategy Development: Design and lead the implementation of anti-fraud strategies specific to BNPL services, balancing risk and customer experience. - Real-Time Monitoring: Manage and optimize real-time transaction monitoring systems to detect and prevent fraudulent activity. - Data-Driven Insights: Use advanced data analytics, machine learning models, and AI tools to identify suspicious patterns and anomalies. - Credit Risk Evaluation: Collaborate with the credit risk team to assess customers’ creditworthiness and optimize fraud prevention during the credit approval process. - Third-Party Integrations: Integrate external fraud detection tools, such as credit bureau data (#PCB, #CIC), blacklist checks (#Kalapa), and fraud scoring tools into BNPL platforms. - Collaboration: Work closely with engineering, product, and legal teams to ensure that fraud prevention measures comply with regulatory standards.  Reporting & Compliance: Develop reports and dashboards to track fraud incidents, analyze trends, and maintain compliance with local financial regulations. - Customer Communication: Design customer verification workflows and manage communication processes when fraud is detected (e.g., additional verification steps). - Adaptability to External Changes: Stay updated on shifts in social, economic, and legal environment and swiftly adapt fraud prevention strategies to address emerging risks. Proactively upgrade anti-fraud systems to ensure alignment with new regulations, industry trends, and fraud tactics in the local market. < Required Skills > - Experience: 5+ years in fraud prevention, risk management, or #financialcrime, preferably within #fintech or BNPL. - Technical Knowledge: Familiarity with fraud detection systems, machine learning models, credit scorecards, and #paymentgateways. - Analytical Skills: Strong ability to analyze large datasets, detect patterns, and provide actionable insights to mitigate risks. - Familiarity with Regulations: Knowledge of financial and data privacy regulations relevant to BNPL and credit risk. - Leadership: Proven experience in managing teams and cross-functional projects. - Local Market Knowledge: Understanding of VN’s social, economic, and #legal landscape, and experience in adjusting strategies based on these dynamics. - Good at communicate by English.

Fraudulent activities pose significant threats to businesses across industries, leading to financial losses, damaged reputations, and regulatory penalties. By implementing comprehensive fraud risk management solutions, businesses can safeguard their assets, protect customer trust, and maintain compliance with regulatory requirements. Building the best fraud risk management products involve - 1. Data Collection and Integration - Gather data from various sources including transactions, user activities, historical fraud cases, and external databases. 2. Data Preprocessing - Clean, preprocess, and normalize the collected data to ensure consistency and accuracy for analysis. 3. ML Models - Develop ML models like supervised learning for classification tasks (e.g., identifying fraudulent transactions), unsupervised learning for anomaly detection (e.g., detecting unusual patterns), and reinforcement learning for adaptive fraud prevention strategies. 4. Feature Selection and Engineering - Identify relevant features (attributes) that contribute to fraud detection and engineer new features if needed. Feature selection techniques like statistical tests, correlation analysis, and domain knowledge can help prioritize features. 5. Model Training and Validation - Train ML models using appropriate algorithms such as logistic regression, decision trees, random forests, support vector machines, or neural networks. Validate models using techniques like cross-validation, holdout validation, or time-based validation to assess performance and generalization ability. 6. Real-time Monitoring - Involve stream processing technologies and rule-based systems to flag suspicious transactions or behaviors in real-time. 7. Alerting and Reporting - Develop alerting mechanisms to notify stakeholders (e.g., fraud analysts, investigators, business owners) about detected fraud incidents. Create comprehensive reports and dashboards to visualize key metrics, trends, and patterns. 8. Integration with Business Processes - Integrate FRM products with existing business processes and systems (e.g., Core Banking System, PG’s, CRM systems) to automate decision-making and streamline workflows. 9. Continuous Improvement - Regularly monitor the performance of fraud detection models and update them as needed to adapt to changing fraud patterns and emerging threats. Employ techniques like model retraining, feedback loops, and A/B testing to continuously improve the effectiveness. 10. Compliance and Regulations - Ensure that fraud risk management products comply with relevant regulations and industry standards (e.g., PCI DSS, GDPR, AML regulations). #frmproducts #regulatorycompliance #dataanalysis #transactionmonitoring #payments

With soaring rates of identity theft, scams, and cybercrime, the anti-fraud industry is rapidly expanding. The fast-paced and sophisticated nature of fraud calls for expertise from a wide range of industries, including IT, criminology, finance, accounting, and psychology. It also offers a wide range of opportunities for highly analytical and curious actuaries. So what might a non-traditional actuarial career working in fraud look like? Fraud teams within insurance businesses typically consist of several roles, each requiring specific skill sets: Fraud/Claims Investigators conduct thorough investigations into suspected fraudulent claims, analysing data, interviewing relevant parties, and collaborating with law enforcement and legal counsel. They often have backgrounds in business, finance, accounting, criminology, psychology, or marketing. Fraud/Intelligence Analysts monitor claim data for fraud patterns, utilising detection tools and developing fraud detection models. They possess strong analytical skills and are familiar with SQL, Python, and predictive modeling, usually coming from IT, data science, psychology, finance, or accounting fields. Fraud Prevention Specialists implement strategies and controls to mitigate fraud risk, provide staff training on fraud awareness, and collaborate with data and technology teams to implement prevention technologies. Actuaries with a background in business, finance, accounting, criminology, IT, data science, or psychology are often well-suited for this role. Finally, Financial Crime Compliance Analysts ensure compliance with anti-money laundering (AML) and know-your-customer (KYC) regulations, conducting due diligence on high-risk claims. They typically have expertise in business, finance, accounting, criminology, IT, psychology, or law. As technology advances, we’re likely to see more and more opportunities for professionals with experience in data science, machine learning, and AI. These skills will become invaluable to fraud detection and prevention projects, opening up incredible opportunities for actuaries who want to be at the forefront of innovation and technology. What other roles do you think actuaries will eventually play in the fraud industry? Let me know in the comments! #FounderTips #ActuaryTips #CEO