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𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐨 𝐦𝐞 𝐏𝐫𝐢𝐜𝐢𝐧𝐠 𝐚𝐧𝐝 ğ‘ğžğ¬ğžğ«ğ¯ğ¢ğ§ğ  𝐨𝐟 ğ†ğžğ§ğžğ«ğšğ¥ ğˆğ§ğ¬ğ®ğ«ğšğ§ğœğž 𝐢𝐧 𝐀𝐜𝐭𝐮𝐚𝐫𝐢𝐚𝐥 𝐭𝐞𝐫𝐦𝐬 - Part 1?

𝐏𝐫𝐢𝐜𝐢𝐧𝐠 𝐚𝐧𝐝 ğ‘ğžğ¬ğžğ«ğ¯ğ¢ğ§ğ  𝐨𝐟 ğ†ğžğ§ğžğ«ğšğ¥ ğˆğ§ğ¬ğ®ğ«ğšğ§ğœğž 𝐢𝐧 𝐀𝐜𝐭𝐮𝐚𝐫𝐢𝐚𝐥 𝐭𝐞𝐫𝐦𝐬 - Part 1 💪🏻 Pricing and reserving of general insurance in actuarial terms involves using mathematical models and statistical analysis to estimate the expected future costs of insuring against specific risks. 💪🏻 Pricing involves determining the premium that should be charged to the policyholder in order to cover the expected cost of claims and expenses, while still allowing for a profit margin. This process involves analyzing data on past claims and expenses, as well as projecting future trends and changes in risk. 💪🏻 Reserving involves setting aside funds to pay for expected claims in the future. Actuaries use mathematical models to estimate the ultimate cost of claims that have been reported, but not yet paid. This involves considering factors such as the length of time it takes for claims to be paid, the expected value of claims, and the probability of payment. 💪

5 𝐦𝐨𝐬𝐭 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐏𝐲𝐭𝐡𝐨𝐧 ğ¥ğ¢ğ›ğ«ğšğ«ğ¢ğžğ¬ 𝐟𝐨𝐫 #ğšğœğ­ğ®ğšğ«ğ¢ğžğ¬ 𝐭𝐨 𝐮𝐧𝐥𝐨𝐜𝐤 𝐭𝐡𝐞 𝐩𝐨𝐰𝐞𝐫 𝐨𝐟 ğ¢ğ§ğ­ğžğ ğ«ğšğ­ğ¢ğ§ğ  ğžğ±ğœğžğ¥ ?

  5 𝐦𝐨𝐬𝐭 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐏𝐲𝐭𝐡𝐨𝐧 ğ¥ğ¢ğ›ğ«ğšğ«ğ¢ğžğ¬ 𝐟𝐨𝐫  #ğšğœğ­ğ®ğšğ«ğ¢ğžğ¬  ğ­ğ¨ 𝐮𝐧𝐥𝐨𝐜𝐤 𝐭𝐡𝐞 𝐩𝐨𝐰𝐞𝐫 𝐨𝐟 ğ¢ğ§ğ­ğžğ ğ«ğšğ­ğ¢ğ§ğ  ğžğ±ğœğžğ¥ #xlwings  - it is easy to call Python from Excel and vice versa #pandas  - pandas has rich tools for reading and writing Excel and CSV files. #XlsxWriter  - a Python module for writing files in the Excel 2007+ xlsx file format #openpyxl  - openpyxl is a Python library to read/write Excel 2010 xlsx/xlsm/xltx/xltm files. #vb2py  - vb2py is a toolkit to aid in the conversion of Visual Basic projects to Python. ğ’ğ®ğ›ğ¬ğœğ«ğ¢ğ›ğž 𝐭𝐨 𝐦𝐲 ğ˜ğ¨ğ®ğ“ğ®ğ›ğž ğ‚ğ¡ğšğ§ğ§ğžğ¥ 𝐭𝐨 ğ¥ğžğšğ«ğ§ 𝐏𝐲𝐭𝐡𝐨𝐧 𝐚𝐧𝐝 𝐒𝐐𝐋 𝐟𝐨𝐫 ğ€ğœğ­ğ®ğšğ«ğ¢ğžğ¬ YouTube - Actuary Sense Follow me on Linkedin:  Kamal Sardana

𝐋𝐢𝐬𝐭 𝐨𝐟 10 𝐬𝐭𝐞𝐩𝐬 𝐢𝐧𝐯𝐨𝐥𝐯𝐞𝐝 𝐢𝐧 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐚𝐧 𝐀𝐜𝐭𝐮𝐚𝐫𝐢𝐚𝐥 𝐦𝐨𝐝𝐞𝐥 𝐮𝐬𝐢𝐧𝐠 𝐎𝐩𝐞𝐧 ğ’ğ¨ğ®ğ«ğœğž 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 ?

  𝐋𝐢𝐬𝐭 𝐨𝐟 10 𝐬𝐭𝐞𝐩𝐬 𝐢𝐧𝐯𝐨𝐥𝐯𝐞𝐝 𝐢𝐧 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐚𝐧 𝐀𝐜𝐭𝐮𝐚𝐫𝐢𝐚𝐥 𝐦𝐨𝐝𝐞𝐥 𝐮𝐬𝐢𝐧𝐠 𝐎𝐩𝐞𝐧 ğ’ğ¨ğ®ğ«ğœğž 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠: via  #Python   #SQL   #PowerBI   #Git   #Github   #R  ğŸ‘‡ğŸ¼ğŸ‘‡ğŸ¼ğŸ‘‡ğŸ¼ ➊ Define the problem and objectives: 👇 Clearly define the problem and objectives of the model. ➋ Data collection: 👇 Gather and clean the necessary data for building the model. ➌ Data exploration: 👇 Explore the data to understand its characteristics, distributions, and relationships. ➍ Model design: 👇 Choose the appropriate actuarial method and design the model. ➎ Implementation: 👇 Implement the model using open source programming languages such as Python, SQL, etc. ➏ Model validation: 👇 Validate the model by comparing the results to actual data and checking the model's assumptions. ➐ Model testing: 👇 Test the model with various scenarios to ensure its accuracy and robustness. ➑ Visualization: 👇 Use data visualization tools such as PowerBI to represen

𝐓𝐨𝐩 10 𝐏𝐲𝐭𝐡𝐨𝐧 ğ‹ğ¢ğ›ğ«ğšğ«ğ¢ğžğ¬ 𝐭𝐡𝐚𝐭 𝐚𝐧 𝐀𝐜𝐭𝐮𝐚𝐫𝐲 𝐦𝐮𝐬𝐭 ğ¥ğžğšğ«ğ§ 👇🏻👇🏻👇🏻👇🏻

  𝐓𝐨𝐩 10 𝐏𝐲𝐭𝐡𝐨𝐧 ğ‹ğ¢ğ›ğ«ğšğ«ğ¢ğžğ¬ 𝐭𝐡𝐚𝐭 𝐚𝐧 𝐀𝐜𝐭𝐮𝐚𝐫𝐲 𝐦𝐮𝐬𝐭 ğ¥ğžğšğ«ğ§ 👇🏻👇🏻👇🏻👇🏻 ğ‘µğ’–ğ’Žğ’‘ğ’š: for numerical computations and data manipulation. 𝑷𝒂𝒏𝒅𝒂𝒔: for data manipulation and analysis. 𝑴𝒂𝒕𝒑𝒍𝒐𝒕𝒍𝒊𝒃: for data visualization. 𝑺𝒄𝒊𝒌𝒊𝒕-𝒍𝒆𝒂𝒓𝒏: for machine learning and statistical modeling. ğ‘ºğ’•ğ’‚ğ’•ğ’”ğ’Žğ’ğ’…ğ’†ğ’ğ’”: for statistical modeling and hypothesis testing. 𝑺𝒆𝒂𝒃𝒐𝒓𝒏: for data visualization, built on top of Matplotlib. 𝑿𝒍𝒘𝒊𝒏𝒈𝒔: for interaction between excel and Python interface. ğ‘ºğ’šğ’Žğ‘·ğ’š: for symbolic mathematics and symbolic computation. 𝑷𝒚𝑴𝑪3: for probabilistic programming and Bayesian modeling. 𝑳𝒊𝒇𝒆𝒍𝒊𝒏𝒆𝒔: for survival analysis and time-to-event modeling ğ’ğ®ğ›ğ¬ğœğ«ğ¢ğ›ğž 𝐭𝐨 𝐦𝐲 ğ˜ğ¨ğ®ğ“ğ®ğ›ğž ğ‚ğ¡ğšğ§ğ§ğžğ¥ 𝐭𝐨 ğ¥ğžğšğ«ğ§ 𝐏𝐲𝐭𝐡𝐨𝐧 𝐚𝐧𝐝 𝐒𝐐𝐋 𝐟𝐨𝐫 ğ€ğœğ­ğ®ğšğ«ğ¢ğžğ¬ YouTube - Actuary Sense Follow me on Linkedin:  Kamal Sardana

How Data Science and Data Engineering both is used in Actuarial work related to Insurance sector ?

  𝐃𝐚𝐭𝐚 ğ’ğœğ¢ğžğ§ğœğž 𝐚𝐧𝐝 𝐃𝐚𝐭𝐚 ğ„ğ§ğ ğ¢ğ§ğžğžğ«ğ¢ğ§ğ  𝐩𝐥𝐚𝐲 𝐚 𝐜𝐫𝐮𝐜𝐢𝐚𝐥 𝐫𝐨𝐥𝐞 𝐢𝐧 𝐭𝐡𝐞 ğŸğ¢ğžğ¥ğ 𝐨𝐟 𝐀𝐜𝐭𝐮𝐚𝐫𝐢𝐚𝐥 ğ’ğœğ¢ğžğ§ğœğž ğ«ğžğ¥ğšğ­ğžğ 𝐭𝐨 𝐭𝐡𝐞 ğˆğ§ğ¬ğ®ğ«ğšğ§ğœğž ğ¬ğžğœğ­ğ¨ğ«. 💪🏼 Data Science is used to analyze and extract insights from large amounts of data, which can then be used to make informed decisions. For example, data scientists might use statistical techniques to analyze claims data and identify patterns or trends that can inform pricing decisions. They may also use machine learning algorithms to predict the likelihood of certain events, such as policyholder churn or fraud. 💪🏼 Data Engineering, on the other hand, is responsible for the development and maintenance of data pipelines and systems. Data engineers work to ensure that data is stored securely, can be accessed quickly, and is formatted in a way that can be easily analyzed. They also develop systems to automate data collection and processing, freeing up data scientists

How python is used in Actuarial Science related work ?

  𝑷𝒚𝒕𝒉𝒐𝒏 𝒊𝒔 𝒘𝒊𝒅𝒆𝒍𝒚 𝒖𝒔𝒆𝒅 𝒊𝒏 𝒕𝒉𝒆 𝒇𝒊𝒆𝒍𝒅 𝒐𝒇 𝑨𝒄𝒕𝒖𝒂𝒓𝒊𝒂𝒍 𝑺𝒄𝒊𝒆𝒏𝒄𝒆 𝒇𝒐𝒓 𝒗𝒂𝒓𝒊𝒐𝒖𝒔 𝒕𝒂𝒔𝒌𝒔 𝒔𝒖𝒄𝒉 𝒂𝒔: ğŸ˜Ž 𝑫𝒂𝒕𝒂 𝒂𝒏𝒂𝒍𝒚𝒔𝒊𝒔 𝒂𝒏𝒅 𝒗𝒊𝒔𝒖𝒂𝒍𝒊𝒛𝒂𝒕𝒊𝒐𝒏 - Python's libraries such as Pandas, Numpy, and Matplotlib are used to process and analyze large amounts of data. ğŸ˜Ž 𝑭𝒊𝒏𝒂𝒏𝒄𝒊𝒂𝒍 ğ’Žğ’ğ’…ğ’†ğ’ğ’Šğ’ğ’ˆ - Actuaries often use Python to build financial models for pricing insurance products, calculating reserves, and projecting cash flows. ğŸ˜Ž 𝑴𝒐𝒏𝒕𝒆 𝑪𝒂𝒓𝒍𝒐 ğ’”ğ’Šğ’Žğ’–ğ’ğ’‚ğ’•ğ’Šğ’ğ’ğ’” - Python's libraries such as NumPy and SciPy provide tools for performing Monte Carlo simulations, which are commonly used in actuarial science to model uncertain events and estimate their impact on a financial system. ğŸ˜Ž 𝑴𝒂𝒄𝒉𝒊𝒏𝒆 𝒍𝒆𝒂𝒓𝒏𝒊𝒏𝒈 - Python's libraries such as scikit-learn and TensorFlow provide tools for building and training machine learning models, which can be used in actuarial science to predict outcomes a

10 𝐬𝐭𝐞𝐩𝐬 𝐟𝐨𝐫 𝐚𝐧 A𝐜𝐭𝐮𝐚𝐫𝐲 𝐭𝐨 ğ›ğžğœğ¨ğ¦ğž ğ­ğžğœğ¡ 𝐨𝐫𝐢𝐞𝐧𝐭𝐞𝐝 𝐚𝐜𝐭𝐮𝐚𝐫𝐲 in 2023

  10 𝐬𝐭𝐞𝐩𝐬 𝐟𝐨𝐫 𝐚𝐧  #𝐚𝐜𝐭𝐮𝐚𝐫𝐲  ğ­ğ¨ ğ›ğžğœğ¨ğ¦ğž ğ­ğžğœğ¡ 𝐨𝐫𝐢𝐞𝐧𝐭𝐞𝐝 𝐚𝐜𝐭𝐮𝐚𝐫𝐲 𝐢𝐧 2023 ⏩⏩⏩ 1️⃣ Gain technical skills: Learn programming languages, data analysis and visualization tools, machine learning algorithms. 2️⃣ Stay updated with current industry developments: Follow industry blogs and attend technology conferences. 3️⃣ Network with tech-savvy actuaries: Join professional organizations and attend events to meet other tech-oriented actuaries. 4️⃣ Take on tech-focused projects: Seek out projects that utilize technology, data analysis and modeling. 5️⃣ Collaborate with IT professionals: Work closely with technology teams to understand their processes and tools. 6️⃣ Participate in technology-focused workshops and training sessions: Regularly attend training sessions and workshops to deepen your tech knowledge. 7️⃣ Experiment with new technology: Explore new technology and apply it to actuarial problems to expand your understanding. 8️⃣ Publish articles or