Artificial Intelligence

Author: Equinox AI Lab

Artificial Intelligence (AI) and Big Data are two of the most transformative technologies of the modern age. Individually, they hold tremendous potential, but when combined, they form a powerful duo capable of solving some of society’s most complex challenges. From healthcare to transportation, the integration of AI and Big Data is shaping industries and delivering unprecedented results.

AI in Healthcare: Improving Diagnostics with Big Data

One of the most significant examples of AI and Big Data working together comes from the healthcare sector. Diagnostic errors are a serious issue, with an alarming study by Johns Hopkins University estimating that over 40,000 patients in the United States die annually due to misdiagnosis. AI, powered by Big Data, is addressing this problem by improving diagnostic accuracy and suggesting more effective treatments.

A notable case occurred in Japan, where a woman suffered from a rare illness that eluded doctors for months. Despite numerous treatments and tests, her condition remained undiagnosed. The medical team sought help from IBM’s Watson, a supercomputer equipped with AI capabilities and access to vast amounts of medical data. In just 10 minutes, Watson identified her illness as a variant of leukemia and recommended an appropriate treatment. The breakthrough came from Watson’s ability to analyse the patient’s medical records and compare them to 20 million preloaded oncology cases. This synergy between AI’s analytical power and Big Data’s comprehensive information drastically reduced diagnosis time and improved treatment outcomes.

AI-Powered Health Applications: A Future in Your Hands

Imagine having access to personalised healthcare solutions through your smartphone. AI-driven apps are already revolutionising the way people approach health concerns. For instance, in the United Kingdom, a startup is developing an application that uses AI to assist doctors in diagnosing patients with higher accuracy. Users input their symptoms and answer a series of questions, allowing the app to analyse data and provide suggested tests or potential diagnoses within minutes.

This innovation addresses critical healthcare challenges, offering timely insights to both patients and medical professionals. By leveraging vast datasets of medical records, AI enhances diagnostic precision, helping reduce errors and save lives.

The Role of Big Data in AI’s Evolution

Big Data provides the foundation upon which AI operates. AI algorithms require vast quantities of data to learn, recognise patterns, and make informed decisions. Big Data offers access to these massive datasets, enabling AI systems to perform at their full potential.

Take the IBM Watson example: without access to millions of medical records, Watson could not have diagnosed the rare form of leukemia so efficiently. Similarly, other industries like transportation and finance depend on AI models trained on Big Data to optimise operations and deliver innovative solutions.

AI and Big Data in Transportation and Beyond

Leading technology companies like Google, Microsoft, IBM, Nvidia, Tesla, and Uber are investing heavily in AI and Big Data across industries. In transportation, AI combined with Big Data is improving autonomous driving systems by analysing real-time data from sensors, cameras, and GPS. These systems can make split-second decisions, ensuring safer and more efficient navigation.

In fields like logistics, AI algorithms powered by Big Data optimise delivery routes, reduce fuel consumption, and increase operational efficiency. These technologies are solving problems that were once considered too complex for traditional methods.

The Technological Revolution of the 21st Century

If the Internet, smartphones, and mobile applications transformed our lives in recent decades, the synergy of AI and Big Data is set to drive a big technological revolution. Together, they offer solutions to some of humanity’s most pressing challenges by combining advanced decision-making capabilities with vast, high-quality datasets.

In sectors such as healthcare, transportation, and finance, this combination has the potential to improve industries, quality of life, and pave the way for a more efficient, data-driven future. Far from being futuristic or threatening, AI and Big Data are tools that, when used responsibly, can unlock new possibilities and contribute significantly to societal progress.

Conclusion

The partnership between Artificial Intelligence and Big Data is a game-changer. From saving lives through accurate medical diagnostics to optimising industries and improving daily life, the integration of these technologies is already transforming the world. As research and development continue, we can expect even more groundbreaking solutions that will shape the future of society and technology in ways we are just beginning to imagine.

*Big data: Big data refers to extremely large and diverse collections of structured, unstructured, and semi-structured data that continues to grow exponentially over time. These datasets are so huge and complex in volume, velocity, and variety, that traditional data management systems cannot store, process, and analyze them. 1

REFERENCIAS

https://cloud.google.com/learn/what-is-big-data#:~:text=Big%20data%20refers%20to%20extremely,%2C%20process%2C%20and%20analyze%20them.
https://asesoftware.com/el-poder-la-de-inteligencia-artificial-con-big-data/

EQUINOX

Learn more about custom ai

Author: Carla Acosta – Visual Designer

Portfolios are traditionally associated with creative fields like design or photography, but in today’s evolving digital landscape, they have become equally important for data scientists, data engineers and AI professionals. A portfolio provides a clear, visual representation of your skills and expertise, giving you a competitive edge over others who may only present their work through resumes or academic credentials. Here’s why AI professionals need a portfolio and how to build one that stands out.

Beyond Resumes: The Power of a Portfolio

AI professionals need a portfolio for much more than just showcasing their technical ability. A portfolio is a platform that allows potential employers or collaborators to understand your problem-solving process and the real-world impact of your work. While a resume lists qualifications and experience, it often fails to capture the depth and scope of complex projects.

A portfolio, on the other hand, tells a comprehensive story. It showcases how you approach data problems, the methodologies and algorithms you employ, and how your solutions drive tangible outcomes. Esta gives hiring managers and potential clients a better sense of your practical expertise y your ability to apply theoretical knowledge to solve real-world issues.

For instance, if you’ve developed a machine learning model that improved customer retention for a company, a portfolio allows you a visually represent the process, from the initial data exploration to the final model implementation, and highlight the business impact it had through an User Interface.

Portfolios Shouldn’t Be Just About Code

One of the most common misconceptions is that a data science portfolio should only contain code or technical details. While showcasing your coding skills is important, making your portfolio visual and business-centric is equally crucial. Graphs, data visualisations, and interactive dashboards are essential in helping non-technical viewers understand the significance of your work.

*Even if you think you’re not talented at doing visuals, Gen-AI could help you develop your ideas or you could just ask to that designer friend!

For example, if you created a recommendation system, include charts that show how it improved user engagement over time. Display the interface changes, dashboards, or front-end elements that resulted from your work, and explain how these contributed to business goals. Esta makes your portfolio accessible to both technical and non-technical audiences, allowing decision-makers to grasp not just the “how” but the “why” behind your solutions.

Tools to Build and Showcase Your Portfolio

Thankfully, there are many tools available to help data scientists and AI professionals create stunning portfolios that combine code, visuals, and storytelling. Platforms like GitHub are great for hosting your code, but to make your portfolio truly shine, consider using a combination of tools for different purposes:

GitHub: Essential for showcasing the technical aspects of your projects. It allows you to host code, documentation, and project overviews.
Kaggle: Ideal for showcasing data science competitions and notebook work. You can share your solutions and receive feedback from the community.
Tableau Public: A powerful tool for building and sharing interactive data visualisations. It’s perfect for presenting the business insights derived from your analyses.
Notion: A versatile tool that allows you to organise and showcase your projects in an easily accessible format. It supports a combination of text, visuals, and even embedded code snippets.
Create a website: Personally, I think a website is the best way to consolidate a portfolio. You can add all the previous links so recruiters won’t adoptar lost el the sea of information.

AI Tools to Streamline Portfolio Creation

Creating a professional portfolio doesn’t have to be a daunting task. AI-powered tools can simplify the process and help you create content that looks polished and visually appealing. Some useful AI tools for building portfolios include:

ChatGPT or Jasper AI: These can assist with writing clear, concise project summaries or explanations, making it easier to describe complex technical concepts. Also chatgpt could be useful for image generation.
Canva or Visme: Use these AI-enhanced graphic design tools to create data visualisations, infographics, and presentation materials for your portfolio.
Plotly or Matplotlib: Use these to generate high-quality graphs and charts that can be embedded into your portfolio to communicate your data insights visually.

Focus on Business Impact

Another critical element in a strong portfolio is demonstrating how your work made an impact on the business. Employers and clients are not only interested in the technical details but also want to know how your work improved outcomes. Whether it’s driving efficiency, reducing costs, or increasing revenue, highlight specific metrics that showcase the success of your projects.

For example, if you built a predictive model for customer churn, don’t just describe the model’s accuracy—show how it reduced customer attrition by 15% or helped the company retain high-value clients. Display graphs and charts that outline these business improvements, making it easier for decision-makers to understand the value you bring.

Conclusion

In conclusion, AI professionals need a portfolio, it is not just for designers or artists anymore. For AI professionals, having el visual, interactive, and results-driven portfolio is essential to stand out in a competitive market. You can effectively communicate your technical and strategic capabilities by showcasing your skills through real-world projects, explaining your process in a business context, and using the right tools to enhance the presentation.

Make sure to tell the story of how your work not only solved data problems but also made a measurable difference to the organisations you worked with. This visual narrative will resonate with technical peers and business stakeholders alike, helping you advance in your career.

Carla Acosta – Visual Designer

EQUINOX

Learn more about custom ai

Author: Laura Mantilla – Data Engineer

SUMMARY

This article explores how insurance companies in Colombia can leverage Artificial Intelligence (AI) and Machine Learning (ML) to detect and prevent fraud in insurance claims. Given the growing economic impact of fraud in the sector, the most effective techniques for identifying suspicious patterns and improving detection accuracy are analyzed. Additionally, the article provides a methodological approach for applying these techniques in the insurance industry, highlighting how effective implementation can optimize fraud detection and offer sustainable long-term benefits.

Keywords: Artificial Intelligence, Machine Learning, Predictive Models, Classification Algorithms, Fraud Detection.

INTRODUCTION

Insurance fraud is a growing and concerning issue in Colombia, affecting not only the finances of insurance companies but also policyholders and the economy in general. According to data collected by the Colombian Federation of Insurers (Fasecolda), in the second half of 2021, 9,916 fraud cases were detected, totaling more than 67.95 billion pesos (approximately 16.8 million dollars), with insurers disbursing 8% of that amount. These frauds mainly impacted SOAT (Mandatory Traffic Accident Insurance), Occupational Risks, Health, and Automobile insurance (Fasecolda, 2022).

However, the situation has intensified in recent years. In 2023, the number of detected frauds exceeded 24,300 cases, a significant increase compared to 2021. Of these cases, 62% were related to SOAT, which continues to be the most affected area. Economically, these frauds represented around 242 billion pesos (approximately 59.98 million dollars), of which insurers paid about 12%, approximately 30 billion pesos, with the regions of Bogotá, Antioquia, Valle del Cauca, and Atlántico being the most impacted (El Tiempo, 2024).

This increase in both the number of cases and the economic impact highlights the urgent need to adopt more effective methods for the prevention and early detection of fraudulent claims. This article explores how Artificial Intelligence (AI) and Machine Learning (ML) techniques can be key tools in this effort, proposing approaches to optimize fraud identification before payments are made, thus protecting the interests of both insurers and policyholders.

Classification Methods for the Detection of Fraudulent Claims

In the field of fraud detection, classification methods play a crucial role in helping to identify patterns that indicate fraudulent activities. These methods are applied through two main phases: model training and testing.

First, the model is trained using historical data with claims labelled as fraudulent or non-fraudulent to learn to identify patterns. Then, its performance is evaluated with a new dataset to verify its ability to correctly classify previously unseen claims.

Figure 1. Logic of a Predictive Model.

Created by the author

The following explores the basic concepts of some of these methods and their application in the identification of fraud in insurance data:

LOGISTIC REGRESSION

Logistic Regression estimates the probability that a claim is fraudulent using a logistic function, which converts predictions into probabilities between 0 and 1. In the context of fraud detection, this method helps predict whether a claim is fraudulent based on key characteristics. It is useful when the data show a clear linear separation between fraud and non-fraud. However, it may have limitations if the data does not fit a linear model well.

DECISION TREES

The Decision Tree organises decisions in a tree structure. Each node represents a decision based on a characteristic, such as the amount of a claim, and the branches show the possible outcomes. To detect fraud, the tree classifies claims by dividing them according to criteria such as whether the amount is high or low. This methodology helps to identify patterns and provides an intuitive way to classify claims as fraudulent or non-fraudulent.

RANDOM FORESTS

The Random Forest uses multiple decision trees to improve classification accuracy. Each tree is trained on a different part of the dataset, and their results are combined to obtain a final prediction. This method is effective in detecting complex patterns and reducing the risk of overfitting, which is crucial in datasets where fraudulent claims are much less common than legitimate ones.

Figura 2. Random Forest structure

Taken from (Sarker, I. H.,2021)

(KNN)

K-Nearest Neighbours (KNN) ranks a claim based on similarity to the nearest claims in the dataset. The algorithm identifies the k nearest neighbours of a new claim and uses majority voting to determine its ranking. For example, if the majority of the k nearest neighbours are fraudulent, the claim will also be classified as fraudulent.

In fraud detection, KNN is useful when the data has complex and non-linear patterns. However, its accuracy depends on the quality of the dataset and the appropriate choice of the number of K neighbours.

Implementation and Training of Classification Algorithms

Effective implementation of classification algorithms for insurance fraud detection requires a structured approach that encompasses several key stages. We will build on the stages proposed in the methodology of (Ismail, A., 2022) which include:

Table 1. Phases for the implementation of ML algorithms.

Adapted from (Ismail, A., 2022)

Phase 1: Exploring the dataset

This stage is key to understanding what information we have and how it is presented. This helps us to understand the quality of the data and to identify potential problems early on. To approach this phase effectively, it is important to consider the following premises: overview of the dataset, exploration of characteristics and identification of potential problems.

In (Ismail, A., 2022) the study was conducted with a dataset taken from Kaggle, which was collected by Angoss Knowledge Seeker software between January 1994 and December 1996. This dataset consists of 15,420 insurance claims records and 33 features, such as:

Figure 3. Features in the dataset.

Taken from (Ismail, A., 2022)

In exploring the dataset they found that, of the 33 characteristics, 9 were numerical variables and the remaining 24 categorical. In addition, the dataset had no null values for any of the features.

Figure 4. Information from the dataset.

Taken from (Ismail, A., 2022)

Phase 2: Data pre-processing

Data pre-processing is crucial to ensure the quality and effectiveness of predictive models used in fraud detection. This process includes data cleaning to correct errors and remove duplicates, imputation of missing values to maintain the consistency of the dataset, and removal of irrelevant information to focus the analysis on the most relevant variables to identify fraud.

In the study of (Ismail, A., 2022), as shown in Figure 4, the dataset has no missing values. The main challenge in the preprocessing was the conversion of categorical variables to numerical variables, an essential step for machine learning algorithms to properly process and analyse the information and thus improve the detection of fraudulent activities.

Figura 5. Raw data set.

Taken from (Ismail, A., 2022)

Figure 6. Processed data set.

Taken from (Ismail, A., 2022)

Phase 3: Data exploration

Once the data has been pre-processed, data exploration focuses on more detailed analysis. This phase uses various techniques and tools to visualise and analyse the data, allowing for the identification of more subtle patterns and relationships between variables.

According to (Komorowski et al., 2016), Exploratory Data Analysis (EDA) has several key objectives: understanding the structure of the data, visualising relationships between variables, finding outliers and anomalies, and extracting important variables. For fraud detection, these practices are essential to uncover subtle patterns and anomalies in claims.

Table 2. Recommended EDA techniques depending on the type of data.

Adapted from (Komorowski et al., 2016)

Exploratory analysis of the dataset taken from (Ismail, A., 2022) reveals several important findings on fraudulent claims:

Seasonality in Fraudulent Claims: The months of March, May and January are observed to have a higher probability of fraudulent claims, possibly due to seasonal factors and a higher volume of claims in these periods.

Figure 7. Probability of fraudulent claims by month.

Taken from (Ismail, A., 2022)

Fraudulent Activity by Day of the Week: Monday and Friday, the days with the highest number of complaints, also show a higher percentage of fraud (see Figure 8). This could indicate that the increase in the number of complaints facilitates the commission of fraud.

Figure 8. Fraudulent activity on days of the week.

Taken from (Ismail, A., 2022)

Vehicle Type and Fraud: Sedans are the vehicles most commonly associated with fraudulent claims (5.15%), compared to utility vehicles (0.2%) and sports cars (0.5%) (see Figure 9). This finding may be useful for adjusting predictive models.

Figure 9. Percentage of fraud by vehicle type.

Taken from (Ismail, A., 2022)

In addition to the observed patterns, a problem of class imbalance within the dataset was identified. With 94% of claims being non-fraudulent and only 6% fraudulent, there is a significant skew in the distribution of classes.

This imbalance can affect the accuracy of the classification model, as most algorithms tend to be biased towards the majority class. It is therefore crucial to address this imbalance, possibly using adjustment techniques such as oversampling or undersampling to improve the model’s ability to detect fraud.

The oversampling technique consists of increasing the number of examples of the minority class (fraudulent claims) by replicating existing cases or generating new synthetic examples. This balances the proportion between the classes in the dataset and helps the model to pay more attention to the minority class, thus improving fraud detection by reducing the bias towards the majority class.

Figure 10. Oversampling technique.

Created by the author

On the other hand, the undersampling technique involves reducing the number of examples from the majority class (legitimate claims) to balance the proportion between classes in the dataset. By decreasing the number of cases from the majority class, the balance with the minority class (fraudulent claims) is improved, which helps to avoid bias towards the majority class and facilitates better fraud detection.

Figure 11. Sub-sampling technique.

Created by the author

Phase 4: Testing and modelling

Once we have identified patterns in the data and tuned the dataset, the next step is to build and train a model to detect fraud. Here are the key steps to build and train a machine learning model, following the principles of (Ismail, A., 2022):

Model Building: Based on the prepared data, selecting the appropriate algorithm for the problem. Here models are created and trained according to the previously identified patterns and features.
Evaluation and Validation: Dividing the data set into training and testing parts to validate model performance. Using techniques such as cross-validation to ensure that the model generalises well to new data and not just the training data.
Model Optimisation: Adjusting model parameters to improve model accuracy and performance. This may include fine tuning techniques and selection of relevant features.
Model Deployment: Once the model has been optimised, it is deployed to detect fraud in real time or on new data sets.

It is important to mention that there are several metrics to assess the accuracy and effectiveness of a model. According to (Hossin, M., & Sulaiman, M. N., 2015), these metrics provide a comprehensive view of model performance from different perspectives:

Adapted from (Hossin, M., & Sulaiman, M. N., 2015)

Phase 5: Results

Finally, the analysis of results is carried out, this includes the exploration of the insights obtained to interpret the findings and validate the proposed solution, ensuring that the model is not only effective in theory, but also meets the requirements and expectations in real situations.

In the following, we will analyse the results obtained in (Ismail, A., 2022) during the evaluation of the Logistic Regression, KNN, Random Forest and XGBoost models:

Table 4. Comparison of performance metrics

Adapted from (Hossin, M., & Sulaiman, M. N., 2015)

The results in Table 4 show that Random Forest and KNN are the best performing models in accuracy, precision, sensitivity and F1 score. Random Forest achieves the best accuracy (0.98) and high precision and sensitivity (0.98), while KNN also shows outstanding performance with an accuracy of 0.96 and similar metrics in precision and sensitivity.

Logistic Regression, on the other hand, underperforms with an accuracy of 0.75, and lower precision, sensitivity and F1 metrics compared to the other models. This indicates that Logistic Regression may not be the most appropriate choice for this particular problem, given the overall poor performance.

For fraud detection, the choice of the right metric is crucial due to the imbalance in the dataset. In this case, where only 6% of claims are fraudulent, the model needs to be sensitive to detecting these rare cases to be effective. In this context:

Accuracy measures the proportion of true positives among all positive predictions made by the model. Although important, high accuracy does not guarantee that the model will detect all fraud cases, especially if the model is biased towards the majority class.

Recovery measures the proportion of true positives correctly detected among all true positives. In fraud detection, high recovery is crucial because we want to ensure that we identify as many fraudulent claims as possible.

F1 Score is the harmonic mean between accuracy and sensitivity, providing a balance between the two. This metric is particularly useful in unbalanced datasets such as ours, as it combines the ability to detect (recovery) with the accuracy of positive predictions (precision).

Given the imbalance in the dataset, with a majority of non-fraudulent cases, recall is the most relevant metric to assess the model’s performance in fraud detection. We want to minimise the number of false negatives (fraudulent cases that are not detected), which is reflected in high recall.

CONCLUSIONES

This article highlights how Artificial Intelligence (AI) and Machine Learning (ML) can be very effective tools for detecting insurance claims fraud in Colombia. Among the models evaluated by (Ismail, A., 2022), the Random Forest and the KNN have proven to be the most effective. The Random Forest, in particular, achieved the highest accuracy (0.98) and showed excellent precision and sensitivity. The KNN also performed very robustly. In comparison, Logistic Regression was not as successful, suggesting that it is not the best choice for this type of problem.

Furthermore, it is crucial to address the imbalance in the dataset, given that most claims are legitimate and only a small fraction are fraudulent. Techniques such as oversampling and undersampling can help improve fraud detection by balancing class representation and preventing the model from biasing towards the majority class.

In our case, where effective fraud detection is a priority without compromising the quality of legitimate data, oversampling seems to be the most appropriate technique. By increasing the representation of fraudulent claims, this technique allows the model to better focus on identifying these rare cases without losing valuable information from legitimate claims.

These findings show that, in addition to properly choosing and fitting models, it is also crucial to manage data imbalance to optimise fraud detection. Implementing these techniques and methods can help insurers improve the identification of fraudulent claims, thereby reducing risks and associated costs.

REFERENCIAS

Fasecolda. (2022, 30 de marzo). La industria aseguradora detectó fraudes por más de $68 mil millones [Comunicado de prensa]. https://www.fasecolda.com/cms/wp-content/uploads/2023/06/La-industria-aseguradora-detecto-fraudes-por-mas-de-68-mil-millones-.pdf

El Tiempo. (2024, 6 de febrero). SOAT encabeza escalafón de pólizas con las que más se comete fraude en Colombia. El Tiempo. https://www.eltiempo.com/economia/sector-financiero/fraudes-esta-es-la-poliza-con-la-que-mas-estafan-a-las-aseguradoras-852030

Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2, 160. https://doi.org/10.1007/s42979-021-00592-x

Ismail, A. (2022). Fraudulent Insurance Claims Detection Using Machine Learning. Thesis. Rochester Institute of Technology. https://repository.rit.edu/cgi/viewcontent.cgi?article=12510&context=theses

Komorowski, M., Marshall, D., Salciccioli, J., & Crutain, Y. (2016). Exploratory Data Analysis. https://www.researchgate.net/publication/308007227_Exploratory_Data_Analysis.

Hossin, M., & Sulaiman, M. N. (2015). A Review on Evaluation Metrics for Data Classification Evaluations. International Journal of Data Mining & Knowledge Management Process, 5(2), 01-11. https://www.researchgate.net/publication/275224157_A_Review_on_Evaluation_Metrics_for_Data_Classification_Evaluations

Laura Mantilla – Data Engineer

Author: Paula Sanchez – Designer

Fashion, an industry known for its creativity and constant innovation, is being radically transformed by artificial intelligence (AI). What was once a field dominated solely by artistic vision and manual skills is now being integrated with cutting-edge technology that promises to redefine the process of design, production, and consumption of fashion. From creating personalised collections to predicting global trends, AI is positioning itself as an essential tool for designers and brands looking to stay ahead in an increasingly competitive market. Let’s check the revolution of AI in fashion!

The integration of AI in fashion is not only streamlining traditional processes but also opening new creative possibilities,73% of fashion executives said IA Generativa will be a priority for their businesses in 2024 (The State of Fashion ,2024). Advanced algorithms can analyse vast amounts of data to identify consumer patterns and preferences, allowing brands to anticipate market demands with unprecedented accuracy. Additionally, AI-assisted design tools are empowering designers to experiment with shapes, colours, and textures in ways that were previously impossible, pushing creativity to new heights.

In this article, we will explore how artificial intelligence is revolutionising every aspect of the fashion industry, from the supply chain to the user experience, and how it is forever changing the way we conceive and consume fashion.

Key Areas of Transformation

Artificial Intelligence (AI) is increasingly becoming a critical component in the fashion industry, driving innovation across various domains. Here, we will explore how the revolution of AI in fashion influences trend forecasting and research, virtual prototyping, designing spaces, fashion models, and trendy content creation.

1. Trend Forecasting & Research

AI has revolutionised trend forecasting by enhancing the speed and accuracy with which fashion trends are identified and predicted. Traditional trend forecasting methods relied heavily on human analysis and intuition, which could be slow and subjective. Today, AI algorithms analyse vast amounts of data from social media, online searches, and sales figures to detect emerging trends in real-time. This ability allows fashion brands to respond more dynamically to shifts in consumer preferences, reducing the time it takes to bring new styles to market (AI Model Agency, 2023).

2. Virtual Prototyping

Virtual prototyping is another area where AI is making significant strides. By using AI-powered tools, designers can create and modify 3D models of garments digitally, streamlining the design process. This approach reduces the need for physical samples, leading to cost savings and more sustainable practices. AI-driven virtual prototyping tools also allow designers to experiment with different fabrics, colours, and patterns in a virtual environment, making the design process more efficient and creative (Analytics India Magazine, 2023).

3. Designing Spaces

AI’s impact extends beyond garments to the very spaces where fashion is created and sold. For instance, AI can be used to design and optimise retail spaces, creating personalised shopping experiences that cater to individual preferences. AI-driven tools analyse customer behaviour, allowing retailers to adjust store layouts and product placements to maximise engagement and sales. Additionally, as the fashion industry ventures into the metaverse, AI is crucial in designing virtual stores and showrooms that offer immersive, interactive experiences (Immago, 2023).

4. Fashion Models

The rise of AI-generated fashion models is reshaping the way fashion is presented and marketed. Brands like Levi Strauss are collaborating with AI platforms to create hyper-realistic virtual models that reflect a diverse range of body types, ages, and ethnicities. These AI models are not just cost-effective but also allow brands to produce content around the clock, without the logistical challenges of traditional photoshoots. However, this trend also raises questions about authenticity and the representation of diversity, as the industry navigates the ethical implications of digital avatars replacing human models (AI Model Agency, 2023; Analytics India Magazine, 2023).

*Check out Aitana’s Instagram profile: a Spanish AI generated model here.

5. Trendy Content Creation

Tomado de: https://vitaldesign.com/buyer-persona/

In content creation, AI is enabling fashion brands to generate tailored content at scale. Tools like Shopify Magic use AI to create personalised marketing materials, from product descriptions to social media posts. This automation helps brands maintain a constant stream of fresh, relevant content, increasing engagement with their audience. Furthermore, AI-powered chatbots and recommendation engines enhance the shopping experience by providing personalised suggestions based on individual preferences and past behaviours, thus driving sales and customer satisfaction (Analytics India Magazine, 2023; Immago, 2023).

Conclusion

AI is transforming the fashion industry in profound ways, from speeding up trend forecasting to revolutionising the design and retail spaces. While these advancements offer numerous benefits, such as increased efficiency and personalised experiences; they also present new challenges, particularly around diversity, ethics, and authenticity concerns. As the revolution of AI in fashion continues, it will be essential for the fashion industry to balance technological innovation with the need to maintain human creativity and ethical standards.

REFERENCIAS

– AI Model Agency. (2023). The Next Wave in Fashion: How AI-Generated Fashion Models Are Changing the Industry. Retrieved from https://www.aimodelagency.com

– Immago. (2023). How Artificial Intelligence is changing the fashion industry. Retrieved from https://www.immago.com

– Analytics India Magazine. (2023). Top 7 Noteworthy AI Innovations in Fashion in 2023. Retrieved from https://www.analyticsindiamag.com

-Balchandani,A.,Barrelet,D.,Berg,A.,D’Auria,G.,Rölkens,F.,& Starznska,E.(2023) The State of Fashion 2024: Finding pockets of growth as uncertainty reigns. En McKinsey & Company. https://www.mckinsey.com/industries/retail/our-insights/state-of-fashion

Paula Sanchez – Designer

1 EFICIENCIA OPERACIONAL

Es allí donde RPA y su principal función que es la automatización de tareas repetitivas juega un papel importante ya que no solo puede mejorar la eficiencia operativa en las organizaciones, sino que también puede generar un impacto indirecto en el uso de los recursos, permitiendo así reducir el desperdicio de recursos como el papel, energía y otros insumos, donde el agua es un factor importante para la producción de estos insumos, bienes o servicios.

2 Monitoreo y gestión Ambiental

RPA en ayuda de integraciones de sensores o sistemas de monitoreo puede recopilar información que, en conjunto con un análisis de datos enfocados a lo ambiental, pueden identificar y mitigar cambios abruptos en cuanto al uso y la gestión de recursos naturales.

3 Gestión y seguimiento de consumos

Con RPA podemos automatizar los procesos de facturación del servicio de agua, donde se busca obtener un mejor cálculo de los consumos, y facilitar el seguimiento por parte de los usuarios finales y fomentando entre ellos usos más racionales, eficientes y responsables del recurso.

4 Gestión de infraestructuras

Se pueden hacer programaciones avanzadas para que Robots puedan hacer inspecciones regulares de infraestructura relacionadas con el consumo del recurso e identificando las áreas que puedan requerir un mantenimiento correctivo o preventivo.

5 Análisis de información y predicciones de demanda

RPA puede recopilar información la cual, con un debido procesamiento y análisis a estos grandes volúmenes de datos relacionados con los consumos, permite a las autoridades y empresas anticipar la demanda futura y tomar medidas proactivas para gestionar el suministro de manera eficiente.

Tomado de: https://vitaldesign.com/buyer-persona/

Estas formas de regulación se pueden complementar con válvulas inteligentes que no solo regulen el recurso hídrico como el llenado de tanques de reserva, sistemas de regado en jardines, entre otros, sino que también sistemas de interruptores los cuales hagan un uso óptimo de la electricidad, recordemos que estos son impactos indirectos pero que influyen a la hora de hacer un uso racional del agua.

Si bien hemos observado las formas consideramos que el manejo de estos recursos como lo puede hacer mediante RPA, esta no constituye una solución completa por si sola, pero si complementaria en los impactos de la regulación del mismo.

Finalmente, podemos concluir que los avances tecnológicos donde se evidencia una solida gama de productos para casas inteligentes que forman parte del internet de las cosas (IOT) acompañadas con Smart Automation, pueden influir no solo en tareas empresariales, sino también en la regulación del consumo de recursos desde casa, contribuyendo a resolver problemas ambientales.

David Rivera – RPA Engineer

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Author: Carla Acosta – Lead Designer

It’s the third year we’ve attended the UK’s most prominent technology event, and we have some tips to help you get the most out of the London Tech Week.

The London Tech Week is a week-long celebration of tech and talent in a world-class hub of innovation. It is held in a central venue, but there are many side events that are also worth exploring. This year, it was at Olympia’s London, where there are several stages where you can listen to industry leads’ keynotes and stands to meet and discover startups and growing companies.

London Tech Week is the perfect place to exchange ideas with people from around the globe. It has 45,000 participants, 5,000 startups, 350+ speakers, and 1,000+ investors.

But how can you get the most out of this experience?

CONNECT WITH PEOPLE BEFORE THE EVENT

First, be prepared to connect and speak with other people.

Once you have booked your ticket, download the LTW app, create your profile, and build the persona of the person you want to talk to. Ask yourself if you’re looking for partners, investors, or colleagues from a specific country to do business.

After creating the persona, look for the LTW participants on the app and check who matches your requirements. Contact them through the app 3 to 1 week before the event to schedule an appointment. Remember that the best way to approach someone is by connecting and creating sincere relationships rather than selling or promoting a product or a service in the first instance.

Tomado de: https://vitaldesign.com/buyer-persona/

ASK QUESTIONS TO START CONVERSATIONS

Don’t be shy! We understand it’s easier said than done, but asking questions is perfectly fine. There will be a lot of information in the environment, from the keynotes to the pitches and vouchers you’ll receive at the stands. So, if you find anything that catches your attention, don’t hesitate to go deeper into the conversation. Expositors will be glad to begin a discussion with attendees.

DOODLES

Write, draw or sketch. At some keynotes, we found enlightening insights that work for our business solutions; writing them down allows us to remember the idea clearly, understand it better, and deepen it after the London Tech Week.

It is also a valuable way to remember all the information coming to your head this week.

Taken from scribbleincltd instagram: https://www.instagram.com/scribbleincltd/

EXPLORE EVERYTHING

As we said in previous paragraphs, London Tech Week also has side events that could be valuable to you; a great place to find them is on Eventbrite, where they post constantly paid and free events this week. In addition, if you’re not from the UK, you can find other event information with your embassy or delegation.

Another way to enjoy the week is to discover the city! London has more than 170 museums and a living history. We’re sure you’ll find inspiration and awaken your creativity, no matter your industry.

Picture taken by the author

BOND WITH PEOPLE

It doesn’t matter which social network you use; generally, in a corporate environment, we use LinkedIn, but it is crucial to connect with the people you meet to keep everyone in one place and remember names and faces. Have an easy access QR with your social networks or a business card (we recommend the QR; after all, it is a tech event, and it is always good to take care of the environment).

After the event, it is common to lose the inertia, but this is the most important moment to write short messages and keep the most critical people on the radar for your business.

NETWORKING EVENTS

Definitely, we recommend attending the networking spaces; this is the most exciting space to start conversations with leaders from around the world because at other events, you won’t be able to interrupt exhibitors.

Starting a conversation with a stranger can feel intimidating, but here are three effective ways to break the ice:

Comment on your surroundings: Speak about London’s weather (that’s something locals do all the time), ask if it is someone’s first time in London, or just go to the point saying something about tech.
Ask for advice or recommendations: Ask questions about the keynotes, the city or the venue.
Compliment or acknowledge them: If you recognise someone from any company or talk, express why you know them or what you like about their work.

Remember, it is the responsibility of all attendees to create a friendly and engaging atmosphere, encouraging strangers to bond positively and keeping the conversations going.

CONCLUSION

In conclusion, attending London Tech Week offers an incredible opportunity to immerse yourself in a vibrant ecosystem of innovation, networking, and knowledge-sharing. By preparing in advance, actively engaging with fellow attendees, asking insightful questions, and taking detailed notes, you can maximise the benefits of this event. Don’t forget to explore the side events and the city itself to enhance your experience and foster creativity. Finally, maintain your new connections through LinkedIn or other social platforms to build lasting professional relationships. Embrace the full potential of London Tech Week to propel your tech career and business forward, and of course, contact us if you want to chat, discuss AI ideas or receive some of our London restaurants suggestions.

Carla Acosta – Lead Designer

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Inteligencia Artificial

Conclusion

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Beyond Resumes: The Power of a Portfolio

Portfolios Shouldn’t Be Just About Code

Tools to Build and Showcase Your Portfolio

AI Tools to Streamline Portfolio Creation

Focus on Business Impact

Conclusion

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SUMMARY

INTRODUCTION

Classification Methods for the Detection of Fraudulent Claims

LOGISTIC REGRESSION

DECISION TREES

RANDOM FORESTS

(KNN)

Implementation and Training of Classification Algorithms

Phase 1: Exploring the dataset

Phase 2: Data pre-processing

Phase 3: Data exploration

Phase 4: Testing and modelling

Phase 5: Results

CONCLUSIONES

REFERENCIAS

Key Areas of Transformation

1. Trend Forecasting & Research

2. Virtual Prototyping

3. Designing Spaces

4. Fashion Models

5. Trendy Content Creation

Conclusion

REFERENCIAS

ARTÍCULOS RELACIONADOS

1 EFICIENCIA OPERACIONAL

2 Monitoreo y gestión Ambiental

3 Gestión y seguimiento de consumos

4 Gestión de infraestructuras

5 Análisis de información y predicciones de demanda

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CONNECT WITH PEOPLE BEFORE THE EVENT

ASK QUESTIONS TO START CONVERSATIONS

DOODLES

EXPLORE EVERYTHING

BOND WITH PEOPLE

NETWORKING EVENTS

CONCLUSION

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