Are you tired of falling for the hype and false promises of data-driven decision making? Introducing the Scoring Models in Machine Learning Trap – a comprehensive knowledge base that will equip you with the necessary tools to avoid the pitfalls of this industry.
Our dataset contains 1510 prioritized requirements, cutting-edge solutions, and proven benefits to help you make informed decisions by urgency and scope.
Our carefully curated information will guide you through the maze of data-driven decision making and provide you with tangible results.
But what sets our product apart from competitors and alternatives? Our Scoring Models in Machine Learning Trap is specifically designed for professionals in the field.
Whether you are a data scientist, analyst, or business professional, this knowledge base will give you a competitive advantage in your decision-making process.
With its user-friendly format, our product is easy to use and provides detailed specifications and case studies to showcase its effectiveness.
We understand that not everyone has access to expensive data analytics tools, which is why we offer an affordable and DIY alternative.
But it′s not just about cost – our Scoring Models in Machine Learning Trap gives you a deeper understanding of the ever-changing landscape of data-driven decision making.
Our researched-based approach ensures that you stay ahead of the curve and stay on top of the latest trends and advancements.
However, our product is not just for individuals – businesses can also benefit greatly from our knowledge base.
With its emphasis on urgency and scope, companies can make informed decisions that align with their goals and objectives.
We understand that there may be concerns about the cost and potential cons of using our product.
But let us assure you that the benefits far outweigh any negative aspects.
With our Scoring Models in Machine Learning Trap, you can make data-driven decisions with confidence and avoid wasting resources on flawed strategies.
In summary, our Scoring Models in Machine Learning Trap provides a detailed overview of the industry, helpful guidelines for professionals, and practical solutions for businesses.
Don′t fall for the hype – trust our knowledge base to guide you through the complexities of data-driven decision making.
Try it out today and see the difference for yourself.
Does your organization implement enhanced controls when using alternative data in models? Is your organization using other scoring models, including behavioural, small business, or collection? How should the data outputs of machine learning models be used, stored, and reused?
Scoring Models
Scoring models use alternative data to assess risk and make decisions, but organizations should have additional controls in place to ensure accuracy and ethical use.
1. Implementing stricter controls and standards when using alternative data in scoring models. This ensures that the data used is reliable and accurate, reducing the risk of biased or misleading results.
2. Conducting regular audits and reviews of scoring models to identify any potential issues or biases. This helps to maintain transparency and trust in the decision-making process.
3. Incorporating diverse perspectives and expertise in the development and evaluation of scoring models. This helps to mitigate the risk of groupthink and promotes a more balanced approach.
4. Ensuring that scoring models are regularly updated and tested to keep up with changing data and market trends. This helps to prevent outdated and inaccurate results.
5. Educating decision-makers on the limitations and potential biases of scoring models. This promotes a critical and skeptical mindset and encourages them to question and validate the results.
6. Utilizing multiple scoring models and comparing their results to gain a more comprehensive understanding of the data. This reduces the reliance on a single model and mitigates the risk of skewed results.
7. Incorporating ethical considerations and diverse data sources in scoring models to prevent discrimination and promote fairness. This increases the accuracy and reliability of the results.
8. Seeking external validation and review of scoring models from independent experts. This provides an unbiased evaluation of the model′s effectiveness and helps to identify any potential issues.
CONTROL QUESTION: Does the organization implement enhanced controls when using alternative data in models?
Big Hairy Audacious Goal (BHAG) for 10 years from now:
By 2031, Scoring Models will have successfully implemented enhanced controls for the use of alternative data in all models within our organization. These controls will be integrated into every stage of the modeling process, from data collection and validation to model development and testing.
Through these enhanced controls, we will ensure that all alternative data used in our models is ethically sourced, accurately represented, and objective. Our teams will be trained on the proper handling and utilization of alternative data to mitigate potential biases and discrimination.
Furthermore, we will have established a rigorous monitoring and auditing system to continuously assess the effectiveness and fairness of our models. Any issues identified will be addressed promptly to maintain transparency and accountability.
Our commitment to responsible and ethical use of alternative data in scoring models will not only benefit our organization but also contribute to creating a more just and equitable financial ecosystem for our customers and society as a whole. We envision Scoring Models to be a leader in this area, setting an industry standard for responsible and unbiased modeling practices.
"The documentation is clear and concise, making it easy for even beginners to understand and utilize the dataset."
Introduction
In the current era of big data, organizations are constantly looking for ways to improve their decision-making processes and gain a competitive edge. With the growth of alternative data sources such as social media, online transactions, and geolocation data, organizations have an immense amount of data at their disposal. To make sense of this vast amount of data, organizations often turn to scoring models, which use statistical algorithms and predictive analytics to evaluate risk and make informed decisions. However, in recent years, there has been a growing concern about the use of alternative data in scoring models and the potential risks associated with it.
This case study explores the implementation of enhanced controls when using alternative data in scoring models for a financial services organization, ABC Bank. The main objective of this project was to assess the effectiveness of the existing controls and develop recommendations for implementing enhanced controls to mitigate potential risks associated with the use of alternative data in scoring models.
Synopsis of Client Situation
ABC Bank is a global financial services organization with operations in several countries. The bank offers a range of financial products and services such as loans, credit cards, and investment products. The bank has a large and diverse customer base and deals with a significant amount of sensitive customer information. To evaluate the creditworthiness of customers and make informed lending decisions, the bank relies heavily on scoring models. However, with the rise of alternative data sources, the bank started utilizing this data in their scoring models to get a more comprehensive view of a customer′s creditworthiness. This raised concerns about the risks associated with the use of alternative data, including potential biases and privacy concerns. As a result, the bank sought the help of a consulting firm to assess their current practices and develop recommendations for implementing enhanced controls when using alternative data in scoring models.
Consulting Methodology
The consulting firm followed a structured methodology to assess the effectiveness of existing controls and develop recommendations for implementing enhanced controls. The methodology involved the following steps:
1. Understanding the current practices: The first step was to gain a thorough understanding of the bank′s current practices. This involved interviews with key stakeholders, reviewing relevant documents and policies, and assessing the existing controls in place.
2. Identifying potential risks: The consulting team then conducted a risk assessment to identify potential risks associated with the use of alternative data in scoring models. This involved a review of industry best practices, relevant regulations, and emerging trends in the market.
3. Evaluating the existing controls: The next step was to evaluate the effectiveness of the existing controls in mitigating the identified risks. This involved a detailed review of the processes, policies, and technology in place.
4. Developing recommendations: Based on the findings from the risk assessment and control evaluation, the consulting team developed a set of recommendations for implementing enhanced controls. These recommendations were tailored to the specific needs and requirements of ABC Bank.
5. Implementing the recommendations: The final step was to work with the bank to implement the recommendations. This involved developing an action plan, providing training to employees, and providing ongoing support to ensure smooth implementation.
Deliverables
The consulting firm delivered a comprehensive report that included the following:
1. Executive summary: A high-level overview of the project, including the objectives, approach, and key findings.
2. Current practices assessment: A detailed assessment of the bank′s current practices, including scoring models and the use of alternative data.
3. Risk assessment report: This report highlighted the potential risks associated with the use of alternative data in scoring models.
4. Control evaluation report: A detailed assessment of the existing controls, including strengths and weaknesses.
5. Recommendations: A set of practical and actionable recommendations for implementing enhanced controls when using alternative data in scoring models.
6. Implementation plan: A detailed plan outlining the steps for implementing the recommendations, including timelines and responsibilities.
Implementation Challenges
The main challenge faced during the implementation of this project was the lack of standardized guidelines or regulations around the use of alternative data in scoring models. This made it difficult to benchmark the bank′s practices against industry best practices. Another challenge was the limited awareness among employees about the potential risks associated with the use of alternative data in scoring models. Therefore, the consulting firm had to provide extensive training and awareness sessions to ensure buy-in and successful implementation of the recommendations.
KPIs
The key performance indicators (KPIs) used to measure the success of this project were:
1. Reduction in potential risks associated with the use of alternative data in scoring models.
2. Improvement in the existing controls and processes.
3. Adherence to industry best practices and regulatory requirements.
4. Training and awareness among employees about the potential risks and the need for enhanced controls when using alternative data in scoring models.
5. Reduction in customer complaints related to the use of alternative data in scoring models.
Management Considerations
The management team at ABC Bank should consider implementing the recommendations provided by the consulting firm to mitigate potential risks associated with the use of alternative data in scoring models. The management should also regularly review and update their policies and procedures relating to the use of alternative data to ensure compliance with regulations and best practices. Additionally, the bank should invest in ongoing training and awareness programs to ensure all employees are aware of the risks associated with the use of alternative data and follow the recommended controls.
Conclusion
In conclusion, the use of alternative data in scoring models has the potential to improve the decision-making process for organizations. However, it also comes with inherent risks that need to be mitigated through enhanced controls. This case study highlights the importance of implementing effective controls when using alternative data in scoring models. By following a structured approach and working closely with the consulting firm, ABC Bank was able to identify potential risks and develop recommendations for implementing enhanced controls. By implementing these recommendations, the bank can improve their practices and mitigate potential risks associated with the use of alternative data in scoring models.
Our dataset contains 1510 prioritized requirements, cutting-edge solutions, and proven benefits to help you make informed decisions by urgency and scope.
Our carefully curated information will guide you through the maze of data-driven decision making and provide you with tangible results.
But what sets our product apart from competitors and alternatives? Our Scoring Models in Machine Learning Trap is specifically designed for professionals in the field.
Whether you are a data scientist, analyst, or business professional, this knowledge base will give you a competitive advantage in your decision-making process.
With its user-friendly format, our product is easy to use and provides detailed specifications and case studies to showcase its effectiveness.
We understand that not everyone has access to expensive data analytics tools, which is why we offer an affordable and DIY alternative.
But it′s not just about cost – our Scoring Models in Machine Learning Trap gives you a deeper understanding of the ever-changing landscape of data-driven decision making.
Our researched-based approach ensures that you stay ahead of the curve and stay on top of the latest trends and advancements.
However, our product is not just for individuals – businesses can also benefit greatly from our knowledge base.
With its emphasis on urgency and scope, companies can make informed decisions that align with their goals and objectives.
We understand that there may be concerns about the cost and potential cons of using our product.
But let us assure you that the benefits far outweigh any negative aspects.
With our Scoring Models in Machine Learning Trap, you can make data-driven decisions with confidence and avoid wasting resources on flawed strategies.
In summary, our Scoring Models in Machine Learning Trap provides a detailed overview of the industry, helpful guidelines for professionals, and practical solutions for businesses.
Don′t fall for the hype – trust our knowledge base to guide you through the complexities of data-driven decision making.
Try it out today and see the difference for yourself.
Discover Insights, Make Informed Decisions, and Stay Ahead of the Curve:
Key Features:
Comprehensive set of 1510 prioritized Scoring Models requirements. - Extensive coverage of 196 Scoring Models topic scopes.
- In-depth analysis of 196 Scoring Models step-by-step solutions, benefits, BHAGs.
- Detailed examination of 196 Scoring Models case studies and use cases.
- Digital download upon purchase.
- Enjoy lifetime document updates included with your purchase.
- Benefit from a fully editable and customizable Excel format.
- Trusted and utilized by over 10,000 organizations.
- Covering: Behavior Analytics, Residual Networks, Model Selection, Data Impact, AI Accountability Measures, Regression Analysis, Density Based Clustering, Content Analysis, AI Bias Testing, AI Bias Assessment, Feature Extraction, AI Transparency Policies, Decision Trees, Brand Image Analysis, Transfer Learning Techniques, Feature Engineering, Predictive Insights, Recurrent Neural Networks, Image Recognition, Content Moderation, Video Content Analysis, Data Scaling, Data Imputation, Scoring Models, Sentiment Analysis, AI Responsibility Frameworks, AI Ethical Frameworks, Validation Techniques, Algorithm Fairness, Dark Web Monitoring, AI Bias Detection, Missing Data Handling, Learning To Learn, Investigative Analytics, Document Management, Evolutionary Algorithms, Data Quality Monitoring, Intention Recognition, Market Basket Analysis, AI Transparency, AI Governance, Online Reputation Management, Predictive Models, Predictive Maintenance, Social Listening Tools, AI Transparency Frameworks, AI Accountability, Event Detection, Exploratory Data Analysis, User Profiling, Convolutional Neural Networks, Survival Analysis, Data Governance, Forecast Combination, Sentiment Analysis Tool, Ethical Considerations, Machine Learning Platforms, Correlation Analysis, Media Monitoring, AI Ethics, Supervised Learning, Transfer Learning, Data Transformation, Model Deployment, AI Interpretability Guidelines, Customer Sentiment Analysis, Time Series Forecasting, Reputation Risk Assessment, Hypothesis Testing, Transparency Measures, AI Explainable Models, Spam Detection, Relevance Ranking, Fraud Detection Tools, Opinion Mining, Emotion Detection, AI Regulations, AI Ethics Impact Analysis, Network Analysis, Algorithmic Bias, Data Normalization, AI Transparency Governance, Advanced Predictive Analytics, Dimensionality Reduction, Trend Detection, Recommender Systems, AI Responsibility, Intelligent Automation, AI Fairness Metrics, Gradient Descent, Product Recommenders, AI Bias, Hyperparameter Tuning, Performance Metrics, Ontology Learning, Data Balancing, Reputation Management, Predictive Sales, Document Classification, Data Cleaning Tools, Association Rule Mining, Sentiment Classification, Data Preprocessing, Model Performance Monitoring, Classification Techniques, AI Transparency Tools, Cluster Analysis, Anomaly Detection, AI Fairness In Healthcare, Principal Component Analysis, Data Sampling, Click Fraud Detection, Time Series Analysis, Random Forests, Data Visualization Tools, Keyword Extraction, AI Explainable Decision Making, AI Interpretability, AI Bias Mitigation, Calibration Techniques, Social Media Analytics, AI Trustworthiness, Unsupervised Learning, Nearest Neighbors, Transfer Knowledge, Model Compression, Demand Forecasting, Boosting Algorithms, Model Deployment Platform, AI Reliability, AI Ethical Auditing, Quantum Computing, Log Analysis, Robustness Testing, Collaborative Filtering, Natural Language Processing, Computer Vision, AI Ethical Guidelines, Customer Segmentation, AI Compliance, Neural Networks, Bayesian Inference, AI Accountability Standards, AI Ethics Audit, AI Fairness Guidelines, Continuous Learning, Data Cleansing, AI Explainability, Bias In Algorithms, Outlier Detection, Predictive Decision Automation, Product Recommendations, AI Fairness, AI Responsibility Audits, Algorithmic Accountability, Clickstream Analysis, AI Explainability Standards, Anomaly Detection Tools, Predictive Modelling, Feature Selection, Generative Adversarial Networks, Event Driven Automation, Social Network Analysis, Social Media Monitoring, Asset Monitoring, Data Standardization, Data Visualization, Causal Inference, Hype And Reality, Optimization Techniques, AI Ethical Decision Support, In Stream Analytics, Privacy Concerns, Real Time Analytics, Recommendation System Performance, Data Encoding, Data Compression, Fraud Detection, User Segmentation, Data Quality Assurance, Identity Resolution, Hierarchical Clustering, Logistic Regression, Algorithm Interpretation, Data Integration, Big Data, AI Transparency Standards, Deep Learning, AI Explainability Frameworks, Speech Recognition, Neural Architecture Search, Image To Image Translation, Naive Bayes Classifier, Explainable AI, Predictive Analytics, Federated Learning
Scoring Models Assessment Dataset - Utilization, Solutions, Advantages, BHAG (Big Hairy Audacious Goal):
Scoring Models
Scoring models use alternative data to assess risk and make decisions, but organizations should have additional controls in place to ensure accuracy and ethical use.
1. Implementing stricter controls and standards when using alternative data in scoring models. This ensures that the data used is reliable and accurate, reducing the risk of biased or misleading results.
2. Conducting regular audits and reviews of scoring models to identify any potential issues or biases. This helps to maintain transparency and trust in the decision-making process.
3. Incorporating diverse perspectives and expertise in the development and evaluation of scoring models. This helps to mitigate the risk of groupthink and promotes a more balanced approach.
4. Ensuring that scoring models are regularly updated and tested to keep up with changing data and market trends. This helps to prevent outdated and inaccurate results.
5. Educating decision-makers on the limitations and potential biases of scoring models. This promotes a critical and skeptical mindset and encourages them to question and validate the results.
6. Utilizing multiple scoring models and comparing their results to gain a more comprehensive understanding of the data. This reduces the reliance on a single model and mitigates the risk of skewed results.
7. Incorporating ethical considerations and diverse data sources in scoring models to prevent discrimination and promote fairness. This increases the accuracy and reliability of the results.
8. Seeking external validation and review of scoring models from independent experts. This provides an unbiased evaluation of the model′s effectiveness and helps to identify any potential issues.
CONTROL QUESTION: Does the organization implement enhanced controls when using alternative data in models?
Big Hairy Audacious Goal (BHAG) for 10 years from now:
By 2031, Scoring Models will have successfully implemented enhanced controls for the use of alternative data in all models within our organization. These controls will be integrated into every stage of the modeling process, from data collection and validation to model development and testing.
Through these enhanced controls, we will ensure that all alternative data used in our models is ethically sourced, accurately represented, and objective. Our teams will be trained on the proper handling and utilization of alternative data to mitigate potential biases and discrimination.
Furthermore, we will have established a rigorous monitoring and auditing system to continuously assess the effectiveness and fairness of our models. Any issues identified will be addressed promptly to maintain transparency and accountability.
Our commitment to responsible and ethical use of alternative data in scoring models will not only benefit our organization but also contribute to creating a more just and equitable financial ecosystem for our customers and society as a whole. We envision Scoring Models to be a leader in this area, setting an industry standard for responsible and unbiased modeling practices.
Customer Testimonials:
"The documentation is clear and concise, making it easy for even beginners to understand and utilize the dataset."
"This downloadable dataset of prioritized recommendations is a game-changer! It`s incredibly well-organized and has saved me so much time in decision-making. Highly recommend!"
"I`ve tried several datasets before, but this one stands out. The prioritized recommendations are not only accurate but also easy to interpret. A fantastic resource for data-driven decision-makers!"
Scoring Models Case Study/Use Case example - How to use:
Introduction
In the current era of big data, organizations are constantly looking for ways to improve their decision-making processes and gain a competitive edge. With the growth of alternative data sources such as social media, online transactions, and geolocation data, organizations have an immense amount of data at their disposal. To make sense of this vast amount of data, organizations often turn to scoring models, which use statistical algorithms and predictive analytics to evaluate risk and make informed decisions. However, in recent years, there has been a growing concern about the use of alternative data in scoring models and the potential risks associated with it.
This case study explores the implementation of enhanced controls when using alternative data in scoring models for a financial services organization, ABC Bank. The main objective of this project was to assess the effectiveness of the existing controls and develop recommendations for implementing enhanced controls to mitigate potential risks associated with the use of alternative data in scoring models.
Synopsis of Client Situation
ABC Bank is a global financial services organization with operations in several countries. The bank offers a range of financial products and services such as loans, credit cards, and investment products. The bank has a large and diverse customer base and deals with a significant amount of sensitive customer information. To evaluate the creditworthiness of customers and make informed lending decisions, the bank relies heavily on scoring models. However, with the rise of alternative data sources, the bank started utilizing this data in their scoring models to get a more comprehensive view of a customer′s creditworthiness. This raised concerns about the risks associated with the use of alternative data, including potential biases and privacy concerns. As a result, the bank sought the help of a consulting firm to assess their current practices and develop recommendations for implementing enhanced controls when using alternative data in scoring models.
Consulting Methodology
The consulting firm followed a structured methodology to assess the effectiveness of existing controls and develop recommendations for implementing enhanced controls. The methodology involved the following steps:
1. Understanding the current practices: The first step was to gain a thorough understanding of the bank′s current practices. This involved interviews with key stakeholders, reviewing relevant documents and policies, and assessing the existing controls in place.
2. Identifying potential risks: The consulting team then conducted a risk assessment to identify potential risks associated with the use of alternative data in scoring models. This involved a review of industry best practices, relevant regulations, and emerging trends in the market.
3. Evaluating the existing controls: The next step was to evaluate the effectiveness of the existing controls in mitigating the identified risks. This involved a detailed review of the processes, policies, and technology in place.
4. Developing recommendations: Based on the findings from the risk assessment and control evaluation, the consulting team developed a set of recommendations for implementing enhanced controls. These recommendations were tailored to the specific needs and requirements of ABC Bank.
5. Implementing the recommendations: The final step was to work with the bank to implement the recommendations. This involved developing an action plan, providing training to employees, and providing ongoing support to ensure smooth implementation.
Deliverables
The consulting firm delivered a comprehensive report that included the following:
1. Executive summary: A high-level overview of the project, including the objectives, approach, and key findings.
2. Current practices assessment: A detailed assessment of the bank′s current practices, including scoring models and the use of alternative data.
3. Risk assessment report: This report highlighted the potential risks associated with the use of alternative data in scoring models.
4. Control evaluation report: A detailed assessment of the existing controls, including strengths and weaknesses.
5. Recommendations: A set of practical and actionable recommendations for implementing enhanced controls when using alternative data in scoring models.
6. Implementation plan: A detailed plan outlining the steps for implementing the recommendations, including timelines and responsibilities.
Implementation Challenges
The main challenge faced during the implementation of this project was the lack of standardized guidelines or regulations around the use of alternative data in scoring models. This made it difficult to benchmark the bank′s practices against industry best practices. Another challenge was the limited awareness among employees about the potential risks associated with the use of alternative data in scoring models. Therefore, the consulting firm had to provide extensive training and awareness sessions to ensure buy-in and successful implementation of the recommendations.
KPIs
The key performance indicators (KPIs) used to measure the success of this project were:
1. Reduction in potential risks associated with the use of alternative data in scoring models.
2. Improvement in the existing controls and processes.
3. Adherence to industry best practices and regulatory requirements.
4. Training and awareness among employees about the potential risks and the need for enhanced controls when using alternative data in scoring models.
5. Reduction in customer complaints related to the use of alternative data in scoring models.
Management Considerations
The management team at ABC Bank should consider implementing the recommendations provided by the consulting firm to mitigate potential risks associated with the use of alternative data in scoring models. The management should also regularly review and update their policies and procedures relating to the use of alternative data to ensure compliance with regulations and best practices. Additionally, the bank should invest in ongoing training and awareness programs to ensure all employees are aware of the risks associated with the use of alternative data and follow the recommended controls.
Conclusion
In conclusion, the use of alternative data in scoring models has the potential to improve the decision-making process for organizations. However, it also comes with inherent risks that need to be mitigated through enhanced controls. This case study highlights the importance of implementing effective controls when using alternative data in scoring models. By following a structured approach and working closely with the consulting firm, ABC Bank was able to identify potential risks and develop recommendations for implementing enhanced controls. By implementing these recommendations, the bank can improve their practices and mitigate potential risks associated with the use of alternative data in scoring models.
Security and Trust:
- Secure checkout with SSL encryption Visa, Mastercard, Apple Pay, Google Pay, Stripe, Paypal
- Money-back guarantee for 30 days
- Our team is available 24/7 to assist you - support@theartofservice.com
About the Authors: Unleashing Excellence: The Mastery of Service Accredited by the Scientific Community
Immerse yourself in the pinnacle of operational wisdom through The Art of Service`s Excellence, now distinguished with esteemed accreditation from the scientific community. With an impressive 1000+ citations, The Art of Service stands as a beacon of reliability and authority in the field.Our dedication to excellence is highlighted by meticulous scrutiny and validation from the scientific community, evidenced by the 1000+ citations spanning various disciplines. Each citation attests to the profound impact and scholarly recognition of The Art of Service`s contributions.
Embark on a journey of unparalleled expertise, fortified by a wealth of research and acknowledgment from scholars globally. Join the community that not only recognizes but endorses the brilliance encapsulated in The Art of Service`s Excellence. Enhance your understanding, strategy, and implementation with a resource acknowledged and embraced by the scientific community.
Embrace excellence. Embrace The Art of Service.
Your trust in us aligns you with prestigious company; boasting over 1000 academic citations, our work ranks in the top 1% of the most cited globally. Explore our scholarly contributions at: https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=blokdyk
About The Art of Service:
Our clients seek confidence in making risk management and compliance decisions based on accurate data. However, navigating compliance can be complex, and sometimes, the unknowns are even more challenging.
We empathize with the frustrations of senior executives and business owners after decades in the industry. That`s why The Art of Service has developed Self-Assessment and implementation tools, trusted by over 100,000 professionals worldwide, empowering you to take control of your compliance assessments. With over 1000 academic citations, our work stands in the top 1% of the most cited globally, reflecting our commitment to helping businesses thrive.
Founders:
Gerard Blokdyk
LinkedIn: https://www.linkedin.com/in/gerardblokdijk/
Ivanka Menken
LinkedIn: https://www.linkedin.com/in/ivankamenken/
