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Netflix movie becomes 'unexpected' Christmas classic: 'The best holiday film I have seen in years' According to Rotten Tomatoes, 2019's Klause is the best Christmas film It's been declared an 'unexpected' Christmas classic by fans READ MORE: How Netflix has thrilled fans with its raunchiest festive films to date By EMILY LEFROY FOR DAILYMAIL.COM Published: 22:14 GMT, 9 December 2024 | Updated: 22:39 GMT, 9 December 2024 e-mail 3 View comments There's no shortage of festive films as the holiday season approaches, but Christmas fans have already crowned their favorite. According to Rotten Tomatoes, which ranked Netflix 's festive films, the 2019 animated film Klause is a near-perfect film. Rated an impressive 95 per cent on its Tomatometer scoring system and 96 per cent on the Popcornmeter, the beautifully animated creation has been catapulted into 'classic' territory. Fans have declared the movie - which stars the voices of J.K. Simmons, Rashida Jones, and Norm McDonald - an 'unexpected' Christmas classic. 'Klaus might be one of the best and unexcepted holiday films that I have enjoyed in years,' one user commented on Rotten Tomatoes. 'This will satisfy the creepy and the jolly in your life with a beautiful story, strange visuals, hopeful moments, plenty of laughs and a few tears,' they finished. And plenty of others agreed with the assessment. 'Klaus is the surprise of this awards season,' one fan gushed. According to Rotten Tomatoes, who ranked Netflix 's festive films, the 2019 animated film Klause is a near-perfect film (Pictured is a still from Klause) 'An engaging revisionist tale, destined to become another Christmas classic,' agreed someone else. 'Klaus is destined to be a new holiday classic,' another declared. READ MORE: REVEALED: The top 10 most popular Christmas movies of all time - so did you favorite festive film make the cut? The animated film takes place in the fictional island village of Smeerensburg, sharing the 'origin' story of Santa. It told the story of a spoiled young postman called Jesper who strikes up an unlikely friendship with a local toymaker and recluse named Klaus before they team up to help bring joy to a town torn apart by a family feud. The film even outranked Christmas classic Love Actually, which was rated 21 out of 61. Netflix has become known for its variety of Christmas movies, including the popular Princess Switch tribology, and the 2024 addition, Hot Frosty. Following Klause with 91 per cent was 2020's Jingle Jangle: A Christmas Journey. In number three was Alien Christmas (2020), followed by Who Killed Santa? A Murderville Murder Mystery (2022) which starred Jason Bateman, Will Arnett and Maya Rudolph, and Feast of the Seven Fishes (2019). 'Klaus is destined to be a new holiday classic,' a fan declared. (Pictured is a still from Klause) The animated film takes place in the fictional island village of Smeerensburg, sharing the 'origin' story of Santa According to fans, the movie - which stars the voices of J.K. Simmons, Rashida Jones, and Norm McDonald, is an 'unexpected' Christmas classic Number six was Let It Snow (2019), followed by A Boy Called Christmas (2021), then Hot Frosty (2024) and rounding out the top 10 was A Castle for Christmas (2021) and A Castle for Christmas (2021). This season saw Netflix choosing to swap wholesome family cheer for sizzling passion this season, with a string of noticeably racier festive films being released in the lead up to Christmas. Viewers are already big fans of the daring choice, with several of movies already debuting to number one on Netflix, while setting social media ablaze. Last year, IMBD rounded up the best Christmas film of all time, with Home Alone (1990) coming out on top. It was followed by Love Actually (2003), National Lampoon's Christmas Vacation (1989), Elf (2003), and How The Grinch Stole Christmas (2000). Netflix Love Actually Share or comment on this article: Netflix movie becomes 'unexpected' Christmas classic: 'The best holiday film I have seen in years' e-mail Add commentOne of the key challenges in addressing sexual harassment is the ambiguity surrounding its definition. While most people can agree on the most egregious forms of sexual harassment, such as unwanted physical contact and explicit language, there are many subtler forms of harassment that often go unrecognized or unaddressed. This lack of clarity can make it difficult for victims to come forward and for authorities to take appropriate action.The news of "Deadly Arrival" comes as a pleasant surprise to fans who have been eagerly anticipating a new chapter in the "Final Destination" saga. The last installment, "Final Destination 5," was released in 2011, leaving audiences hungry for more of the franchise's signature blend of suspense, supernatural elements, and creative deaths.

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Chance of direct attack by Russia ‘remote’, says UK armed forces chiefShare Tweet Share Share Email Managing expenses effectively is crucial to maintaining financial integrity. However, the rise in fraudulent expense claims poses a significant challenge for organizations worldwide. Fraudulent activities not only drain resources but also damage trust within the workplace. Detecting such fraud manually can be tedious, time-consuming, and error-prone. This is where machine learning comes into play, offering innovative ways to combat expense fraud efficiently and proactively . What Is Expense Fraud? Expense fraud refers to the act of submitting false or exaggerated claims for reimbursement. Employees may manipulate receipts, inflate mileage claims, or create fake invoices to gain undeserved compensation. Common examples of expense fraud include: Submitting personal expenses as business-related costs. Altering the amounts on genuine receipts. Creating entirely fictitious receipts or invoices. Reimbursing duplicate claims for the same expense. These fraudulent activities can cost businesses thousands, if not millions, of dollars annually. While traditional audits can uncover some discrepancies, they often miss more sophisticated schemes. This highlights the need for advanced technologies like machine learning to tackle fraud more effectively. The Role of Machine Learning in Fraud Detection Machine learning (ML) leverages algorithms that analyze data, learn patterns, and make predictions. Unlike rule-based systems, ML models continuously improve over time as they process new data. In the context of expense fraud detection, machine learning offers the following advantages: Automated Data Analysis: ML systems can process vast amounts of expense data in real time, identifying anomalies that may signal fraudulent behavior. Pattern Recognition: By analyzing historical data, machine learning models can detect unusual patterns that deviate from normal expense behaviors. Risk Scoring: ML algorithms assign risk scores to expense claims, flagging those with high likelihoods of fraud for further review. Adaptive Learning: As fraud tactics evolve, machine learning models adapt by learning new patterns and identifying emerging threats. Key Machine Learning Techniques for Expense Fraud Detection Machine learning employs various techniques to identify fraudulent expense claims. Below are some of the most commonly used approaches: Supervised Learning Supervised learning involves training a model on labeled data, where the outcomes (fraudulent or non-fraudulent) are already known. The model learns to classify new expense claims based on the patterns it observes in the training data. Algorithms like decision trees, support vector machines (SVM), and neural networks are commonly used in supervised learning for fraud detection . Unsupervised Learning Unlike supervised learning, unsupervised learning works with unlabeled data. This approach is ideal for detecting unknown types of fraud. Techniques like clustering and anomaly detection help group similar data points and identify outliers. For instance, if an employee’s expense claim is significantly higher than their peers’, the system flags it for investigation. Natural Language Processing (NLP) Natural language processing is used to analyze text-based data, such as descriptions in expense claims. NLP can identify inconsistencies, suspicious keywords, or unusual phrasing that may indicate fraudulent intent. Reinforcement Learning Reinforcement learning involves training models through a reward-based system. In fraud detection, the algorithm receives rewards for accurately identifying fraudulent claims and penalties for false positives or negatives. This iterative process improves the model’s accuracy over time. Building an Effective Machine Learning Framework for Fraud Detection Implementing a machine learning-based expense fraud detection system requires careful planning and execution. Below are the critical steps: Data Collection and Preprocessing The first step is gathering expense-related data, such as receipts, invoices, and transaction records. Preprocessing this data is crucial to ensure accuracy and consistency. Steps include: Cleaning and removing duplicate entries. Converting unstructured data into structured formats. Normalizing numerical data for better model performance. Feature Engineering Feature engineering involves selecting and creating relevant variables (features) that help the model identify fraud. Examples include: Frequency of claims per employee. Average claim amount by department. Expense categories with unusually high costs. Model Training and Testing Once features are defined, the next step is training machine learning models. A portion of the data is used for training, while the rest is reserved for testing. This ensures that the model performs well on unseen data. Deployment and Monitoring After successful testing, the model is deployed to monitor expense claims in real-time. Continuous monitoring and periodic retraining are essential to maintain effectiveness and adapt to new fraud patterns. Challenges in Machine Learning-Based Fraud Detection While machine learning offers powerful tools for expense fraud detection, it is not without challenges. Some common obstacles include: Data Quality Issues: Inaccurate or incomplete data can lead to unreliable model predictions. High False Positives: Over-sensitive models may flag legitimate claims as fraudulent, leading to inefficiencies. Evolving Fraud Tactics: Fraudsters continuously adapt, requiring models to be updated regularly. Privacy Concerns: Handling sensitive financial data necessitates strict adherence to data protection regulations. Addressing these challenges requires a collaborative approach involving robust data governance, regular model audits, and input from domain experts. Benefits of Machine Learning in Expense Fraud Detection Despite the challenges, machine learning brings numerous benefits to expense fraud detection: Efficiency: Automating fraud detection reduces the manual workload, allowing finance teams to focus on high-priority tasks. Accuracy: ML models often outperform traditional methods, identifying subtle patterns that humans might miss. Scalability: Machine learning systems can handle large volumes of data, making them suitable for organizations of all sizes. Cost Savings: By preventing fraud, businesses can save significant amounts of money that would otherwise be lost. Real-World Applications of Machine Learning in Expense Fraud Detection Many organizations have successfully implemented machine learning to combat expense fraud. For instance: Corporate Finance Departments: Large corporations use ML tools to monitor employee expense reports, flagging anomalies in real-time. Expense Management Software: Companies like Expensify and Concur integrate machine learning algorithms to provide fraud detection features for their clients. Financial Institutions: Banks and credit card companies employ ML models to detect suspicious transactions that could indicate fraudulent expense claims. Conclusion Expense fraud is a pressing issue that requires proactive measures to mitigate its impact on businesses. Machine learning offers a transformative solution, enabling organizations to detect and prevent fraud with unprecedented efficiency.Furthermore, By leveraging techniques like supervised and unsupervised learning, natural language processing, and reinforcement learning, companies can build robust fraud detection systems that evolve alongside emerging threats. Although challenges remain, the benefits of machine learning far outweigh the drawbacks, making it an indispensable tool in the fight against expense fraud. Organizations that invest in this technology not only safeguard their finances but also foster a culture of transparency and trust . Related Items: Catching Fraud Before It Strikes , Expense Fraud Detection , machine learning Share Tweet Share Share Email Recommended for you Can Machine Learning Models Truly Revolutionize Retail Sales Forecasting? AI and Machine Learning Reshape Modern Data Center Operations How Machine Learning is Driving a New Era in Healthcare? 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When did Beyoncé, Jay-Z start dating? A relationship timeline, from courtship to marriageAnother crucial factor driving the success of China's ICH projects is the active participation and engagement of local communities and practitioners. Many of these cultural expressions are deeply rooted in the daily lives and traditions of Chinese people, who take great pride in preserving and transmitting their heritage to future generations. By involving community members in the documentation, revitalization, and transmission of ICH projects, China has been able to ensure their sustainability and vitality over time.

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