What are the essential skills for insurance professionals working with predictive analytics?

由人工智能和领英社区提供技术支持

Predictive analytics is the use of data, statistical methods, and machine learning to forecast future outcomes and trends based on historical patterns. It can help insurance professionals improve their decision-making, optimize their pricing and underwriting, enhance their customer experience, and reduce their risks and losses. However, working with predictive analytics requires a set of essential skills that go beyond the traditional insurance knowledge and competencies. In this article, we will explore what these skills are and how you can develop them.

此文章中的业界达人

由社区从 4 条内容中精选。了解更多

Rajarshi Maitra

???????????????? & ???????????????? (??&??) ????/?????????????????? | ?????????????????? | ??????????????…
Pedro Ecija Serrano

Actuarial | Data Science | AI Ethics

1 Data literacy

Data literacy is the ability to understand, interpret, and communicate data effectively. It is not only about knowing how to use data tools and software, but also how to ask the right questions, identify the relevant data sources, assess the data quality, and apply the data insights to solve business problems. Data literacy is crucial for insurance professionals working with predictive analytics, as they need to be able to leverage data to generate value for their customers and stakeholders. To improve your data literacy, you can take online courses, read books and blogs, join data communities, and practice with real-world data sets.

添加您的观点

Rajarshi Maitra

???????????????? & ???????????????? (??&??) ????/?????????????????? | ?????????????????? | ?????????????? ???????????????????????????? | ???????????????????? | ???????????? ?????????????????? | ?????????????? ????????.
举报内容
I believe insurance professionals working with predictive analytics should possess strong analytical skills to interpret data and trends. Moreover, a solid understanding of statistical methods, programming languages (Python, SAS or R) and domain expertise in insurance is a must for the effective utilization of predictive analytics in risk assessment and decisioning.

已翻译

赞

2 Statistical thinking

Statistical thinking is the mindset of applying statistical principles and methods to analyze data and draw valid conclusions. It involves understanding the concepts of probability, distribution, sampling, hypothesis testing, confidence intervals, and error analysis. Statistical thinking is essential for insurance professionals working with predictive analytics, as they need to be able to evaluate the accuracy, reliability, and significance of their predictions and avoid common pitfalls such as overfitting, underfitting, bias, and confounding. To enhance your statistical thinking, you can review the basic statistics concepts, learn how to use statistical software and packages, and apply statistical techniques to your own data projects.

添加您的观点

3 Machine learning knowledge

Machine learning is the branch of artificial intelligence that enables computers to learn from data and perform tasks without explicit programming. It includes various techniques such as supervised learning, unsupervised learning, reinforcement learning, deep learning, and natural language processing. Machine learning knowledge is vital for insurance professionals working with predictive analytics, as they need to be able to select, implement, and optimize the appropriate machine learning models and algorithms for their specific data problems. To acquire machine learning knowledge, you can enroll in online courses, watch videos and podcasts, read books and papers, and experiment with machine learning frameworks and platforms.

添加您的观点

4 Business acumen

Business acumen is the understanding of how a business operates, what are its goals, strategies, processes, and challenges, and how it creates value for its customers and shareholders. It is not only about knowing the insurance industry and its products, but also how to align the data and analytics initiatives with the business objectives, how to measure and communicate the impact and value of the predictions, and how to identify and seize new opportunities and innovations. Business acumen is indispensable for insurance professionals working with predictive analytics, as they need to be able to translate the data insights into actionable recommendations and solutions. To develop your business acumen, you can read industry reports and news, network with other professionals, attend webinars and events, and participate in business case studies and simulations.

添加您的观点

Pedro Ecija Serrano

Actuarial | Data Science | AI Ethics
举报内容
Knowledge of how an insurance company operates, and its economic and regulatory environment, would set any data professional apart from the rest. It allows you to apply your skills in areas you know will add clear value. This extends also to actuarial; there are many synergies between data scientists and actuaries, and actuarial work is at the core of an insurance company. An understanding of what the actuaries do would facilitate collaboration and provide inspiration for projects.

已翻译

赞

5 Communication skills

Communication skills are the abilities to express, present, and share information and ideas clearly, concisely, and persuasively, using various formats and channels. They include verbal, written, visual, and listening skills, as well as the skills of storytelling, visualization, and collaboration. Communication skills are crucial for insurance professionals working with predictive analytics, as they need to be able to communicate the data findings and predictions to different audiences, such as customers, managers, regulators, and peers, and to convince them of the value and relevance of the analytics solutions. To improve your communication skills, you can practice with feedback, use simple and precise language, tailor your message to your audience, and use engaging and informative visuals and stories.

添加您的观点

Pedro Ecija Serrano

Actuarial | Data Science | AI Ethics
举报内容
Communicating complex technical issues in plain English to a non-technical audience is important. Similarly, translating the impact of your models from statistical metrics to business ones (eg, revenue, customers, etc) so that your stakeholders can better understand the implications. A good practice is to keep a log of completed projects with the recorded value added (preferably in measurable terms such as 'increased revenue by this much', 'reduced complains by that much', etc)

已翻译

赞

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

添加您的观点

Pedro Ecija Serrano

Actuarial | Data Science | AI Ethics
举报内容
All professionals involved in data work that requires AI or predictive analytics should focus on AI and data ethics. It will allow them to minimise the likelihood of ethical issues for their employers, as well as mitigate their effect when these arise (note it is 'when' rather than 'if', because they will come up from time to time). While ethics may seem optional, it is an important part of a credible ESG (Environmental, Social, and corporate Governance) strategy. Significant ethical mishaps will likely result in negative public and / or regulatory actions.

已翻译

赞

Insurance

+ 关注

给文章评分

我们借助人工智能创建了此文章。您认为这篇文章怎么样？

很棒不太好

举报此文章

What are the essential skills for insurance professionals working with predictive analytics?

1

2

3

4

5

6

1 Data literacy

2 Statistical thinking

3 Machine learning knowledge

4 Business acumen

5 Communication skills

6 Here’s what else to consider

Insurance

给文章评分

感谢您的反馈

更多Insurance相关文章

更多相关阅读内容