How data analytics and IoT are driving insurtech growth

Article | September 13, 2021

In this data-driven age, marketers have access to all the necessary information about their customers. There are different tools they can use to capture the exact data required for specific campaigns. We have come a long way from mass broadcasting campaigns. Growth in digital marketing has given rise to pinpointed targeting. The marketing industry is still accepting and learning data technology. However, there is more importance given to the content creation side of things when there should be a clear balance between data-driven efforts and content. It can be challenging to re-route pure content creator’s attention to data marketing, but it cannot be ignored for long. It is no longer enough to rely on gut instinct and ‘good content’. The rise in popularity in data-driven marketing has been lead by the revolution in big data. Big data has enabled massive amounts of data to be collected, analyzed, and organized, which helps in creating a personalized customer experience. Since the start of the Covid-19 pandemic, more and more people have started to spend time online. As a result, online user behavior has changed in just a matter of months rather than years. Data-driven marketing efforts can also help marketers to maximize their success as their results will now be data-backed with metrics that will change the way they conduct their business online. We have highlighted the steps you need for successful data-driven marketing. 5 Steps to Take for Successful Data-Driven Content Marketing Layout Objectives For any campaign to succeed, it is imperative to have a list of attainable objectives. You can set these objectives by studying historical data and know-how your marketing campaign will perform. For a successful content marketing strategy, make sure to concentrate on raising brand awareness, retaining current customers, and tracking sales. If you're not putting out relevant content in relevant places, you don't exist. Gary Vaynerchuk, -American entrepreneur, author, speaker, and Internet personality. Customize Campaigns for Target Audience Before you create any data-driven campaigns, know your customers well. Then, with the abundance of data at your fingertips, you can easily create personalized campaigns for them. Figure out and solve any problems they may be facing, if they need any solution, what they’re looking for and where. Creating user profiles will help you avoid targeting generalized strata rather than help you be more precise in your marketing efforts. Regular Content Optimization One of the best ways to ensure successful marketing results is through content optimization. Google algorithms are constantly changing. So what’s ranking on the first page today may not always rank the same next day. Set campaign-specific KPIs and work towards achieving those targets. Use different tools to track whether your campaign is working according to your goals or needs some serious upliftment. Keep running SEO audits on your pages regularly to keep your content in the best shape possible. Content Repurposing Repurposing content is the oldest trick in the book to gain a higher ROI on your existing content. For instance, if you have an article published on your company’s website, adapt that blog into an infographic and publish it on various social platforms. Content repurposing will help you boost your SEO, reach a broader and newer audience, help drive traffic to your website, and raise your brand’s awareness. Track Analytics Every platform has a different reach. Use your platforms according to the KPIs you have set for your business. For example, Twitter can help you raise your brand’s awareness, while LinkedIn will help you generate leads. Different platforms will have different metrics you will need to track. There are online tools available that help marketers track metrics. Each of these metrics will help you to achieve your marketing objectives. Final Thoughts In today’s competitive market, content marketing will have to be data-driven. The data-first approach will help you and your business in reaching the maximum number of people. In addition, a performance-oriented approach will ensure the success of your campaigns. Investing in high-quality marketing technologies will help you get balanced, data-driven, and goal-oriented results preparing you to become a content marketer ready to take on any challenges. Frequently Asked Questions What is the future of content marketing? Data-driven content marketing strategies can help marketers to maximize their success as their results will now be data-backed with metrics that will change the way they conduct their online business. What are the top content marketing trends for 2022? 1. Layout Objectives 2. Customize Campaigns for Target Audience 3. Regular Content Optimization 4. Content Repurposing 5. Track Analytics How is content-based marketing a proven strategy? Content-based marketing is a marketing strategy designed to attract, engage, and retain target audience. This works by creating and sharing relevant content such as articles, podcasts, infographics, videos, and other content marketing materials. This approach lays down expertise, helps brand awareness. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "What is the future of content marketing?", "acceptedAnswer": { "@type": "Answer", "text": "Data-driven content marketing strategies can help marketers to maximize their success as their results will now be data-backed with metrics that will change the way they conduct their online business." } },{ "@type": "Question", "name": "What are the top content marketing trends for 2022?", "acceptedAnswer": { "@type": "Answer", "text": " A. 1. Layout Objectives 2. Customize Campaigns for Target Audience 3. Regular Content Optimization 4. Content Repurposing 5. 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Article | January 21, 2021

The Internet of Things has been the hype in the past few years. It is set to play an important role in industries. Not only businesses but also consumers attempt to follow developments that come with the connected devices. Smart meters, sensors, and manufacturing equipment all can remodel the working system of companies. Based on the Statista reports, the IoT market value of 248 billion US dollars in 2020 is expected to reach a worth of 1.6 Trillion USD by 2025. The global market is in the support of IoT development and its power to bring economic growth. But, the success of IoT without the integration of data analytics is impossible. This major growth component of IoT is the blend of IoT and Big Data - together known as IoT Data Analytics. Understanding IoT Data Analytics IoT Data Analytics is the analysis of large volumes of data that has been gathered from connected devices. As IoT devices generate a lot of data even in the shortest period, it becomes complex to analyze the enormous data volumes. Besides, the IoT data is quite similar to big data but has a major difference in their size and number of sources. To overcome the difficulty in IoT data integration, IoT data analytics is the best solution. With this combination, the process of data analysis becomes cost-effective, easier, and rapid. Why Data Analytics and IoT Will Be Indispensable? Data analytics is an important part of the success of IoT investments or applications. IoT along with Data analytics will allow businesses to make efficient use of datasets. How? Let’s get into it! Impelling Revenue Using data analytics in IoT investments businesses will become able to gain insight into customer behavior. It will lead to the crafting offers and services accordingly. As a result, companies will see a hike in their profits and revenue. Volume The vast amount of data sets that are being used by IoT applications needs to be organized and analyzed to obtain patterns. It can easily be achieved by using IoT analytics software. Competitive Advantage In an era full of IoT devices and applications, the competition has also increased. You can gain a competitive advantage by hire developers that can help with the IoT analytics implementations. It will assist businesses in providing better services and stand out from the competition. Now the next question arises: Where is it being implemented? Companies like Amazon, Microsoft, Siemens, VMware, and Huawei are using IoT data analytics for product usage analysis, sensor data analysis, camera data analysis, improved equipment maintenance, and optimizing operations. The Rise of IoT Data Analytics With the help of IoT Data Analytics, companies are ready to achieve more information that can be used to improve their overall performance and revenue. Although it has not reached every corner of the market yet, it is still being used for making the workplace more efficient and safe. The ability to analyze and predict data in real-time is definitely a game-changer for companies that need all of their equipment to work efficiently all the time. It is continuously growing to provide insights that were never possible before.

Article | December 21, 2020

Introduction Machine Learning (ML) has taken strides over the past few years, establishing its place in data analytics. In particular, ML has become a cornerstone in data science, alongside data wrangling, and data visualization, among other facets of the field. Yet, we observe many organizations still hesitant when allocating a budget for it in their data pipelines. The data engineer role seems to attract lots of attention, but few companies leverage the machine learning expert/engineer. Could it be that ML can add value to other enterprises too? Let's find out by clarifying certain concepts. What Machine Learning is So that we are all on the same page, let's look at a down-to-earth definition of ML that you can include in a company meeting, a report, or even within an email to a colleague who isn't in this field. Investopedia defines ML as "the concept that a computer program can learn and adapt to new data without human intervention." In other words, if your machine (be it a computer, a smartphone, or even a smart device) can learn on its own, using some specialized software, then it's under the ML umbrella. It's important to note that ML is also a stand-alone field of research, predating most AI systems, even if the two are linked, as we'll see later on. How Machine Learning is different from Statistics It's also important to note that ML is different from Statistics, even if some people like to view the former as an extension of the latter. However, there is a fundamental difference that most people aren't aware of yet. Namely, ML is data-driven while Statistics is, for the most part, model-driven. This statement means that most Stats-based inferences are made by assuming a particular distribution in the data, or the interactions of different variables, and making predictions based on our mathematical models of these distributions. ML may employ distributions in some niche cases, but for the most part, it looks at data as-is, without making any assumptions about it. Machine Learning’s role in data science work Let’s now get to the crux of the matter and explore how ML can be a significant value-add to a data science pipeline. First of all, ML can potentially offer better predictions than most Stats models in terms of accuracy, F1 score, etc. Also, ML can work alongside existing models to form model ensembles that can tackle the problems more effectively. Additionally, if transparency is important to the project stakeholders, there are ML-based options for offering some insight as to what variables are important in the data at hand, for making predictions based on it. Moreover, ML is more parametrized, meaning that you can tweak an ML model more, adapting it to the data you have and ensuring more robustness (i.e., reliability). Finally, you can learn ML without needing a Math degree or any other formal training. The latter, however, may prove useful, if you wish to delve deeper into the topic and develop your own models. This innovation potential is a significant aspect of ML since it's not as easy to develop new models in Stats (unless you are an experienced Statistics researcher) or even in AI. Besides, there are a bunch of various "heuristics" that are part of the ML group of algorithms, facilitating your data science work, regardless of what predictive model you end up using. Machine Learning and AI Many people conflate ML with AI these days. This confusion is partly because many ML models involve artificial neural networks (ANNs) which are the most modern manifestation of AI. Also, many AI systems are employed in ML tasks, so they are referred to as ML systems since AI can be a bit generic as a term. However, not all ML algorithms are AI-related, nor are all AI algorithms under the ML umbrella. This distinction is of import because certain limitations of AI systems (e.g., the need for lots and lots of data) don't apply to most ML models, while AI systems tend to be more time-consuming and resource-heavy than the average ML one. There are several ML algorithms you can use without breaking the bank and derive value from your data through them. Then, if you find that you need something better, in terms of accuracy, you can explore AI-based ones. Keep in mind, however, that some ML models (e.g., Decision Trees, Random Forests, etc.) offer some transparency, while the vast majority of AI ones are black boxes. Learning more about the topic Naturally, it's hard to do this topic justice in a single article. It is so vast that someone can write a book on it! That's what I've done earlier this year, through the Technics Publications publishing house. You can learn more about this topic via this book, which is titled Julia for Machine Learning(Julia is a modern programming language used in data science, among other fields, and it's popular among various technical professionals). Feel free to check it out and explore how you can use ML in your work. Cheers!

Article | December 16, 2020

In this article, we will explore different techniques to detect money laundering activities. Notwithstanding, regardless of various expected applications inside the financial services sector, explicitly inside the Anti-Money Laundering (AML) appropriation of Artificial Intelligence and Machine Learning (ML) has been generally moderate. What is Money Laundering, Anti Money Laundering? Money Laundering is where someone unlawfully obtains money and moves it to cover up their crimes. Anti-Money Laundering can be characterized as an activity that forestalls or aims to forestall money laundering from occurring. It is assessed by UNO that, money-laundering exchanges account in one year is 2–5% of worldwide GDP or $800 billion — $3 trillion in USD. In 2019, regulators and governmental offices exacted fines of more than $8.14 billion. Indeed, even with these stunning numbers, gauges are that just about 1 % of unlawful worldwide money related streams are ever seized by the specialists. AML activities in banks expend an over the top measure of manpower, assets, and cash flow to deal with the process and comply with the guidelines. What are the punishments for money laundering? In 2019, Celent evaluated that spending came to $8.3 billion and $23.4 billion for technology and operations, individually. This speculation is designated toward guaranteeing anti-money laundering. As we have seen much of the time, reputational costs can likewise convey a hefty price. In 2012, HSBC laundering of an expected £5.57 billion over at least seven years.   What is the current situation of the banks applying ML to stop money laundering? Given the plenty of new instruments the banks have accessible, the potential feature risk, the measure of capital involved, and the gigantic expenses as a form of fines and punishments, this should not be the situation. A solid impact by nations to curb illicit cash movement has brought about a huge yet amazingly little part of money laundering being recognized — a triumph rate of about 2% average. Dutch banks — ABN Amro, Rabobank, ING, Triodos Bank, and Volksbank announced in September 2019 to work toward a joint transaction monitoring to stand-up fight against Money Laundering. A typical challenge in transaction monitoring, for instance, is the generation of a countless number of alerts, which thusly requires operation teams to triage and process the alarms. ML models can identify and perceive dubious conduct and besides they can classify alerts into different classes such as critical, high, medium, or low risk. Critical or High alerts may be directed to senior experts on a high need to quickly explore the issue. Today is the immense number of false positives, gauges show that the normal, of false positives being produced, is the range of 95 and 99%, and this puts extraordinary weight on banks. The examination of false positives is tedious and costs money. An ongoing report found that banks were spending near 3.01€ billion every year exploring false positives. Establishments are looking for increasing productive ways to deal with crime and, in this specific situation, Machine Learning can end up being a significant tool. Financial activities become productive, the gigantic sum and speed of money related exchanges require a viable monitoring framework that can process exchanges rapidly, ideally in real-time.   What are the types of machine learning algorithms which can identify money laundering transactions? Supervised Machine Learning, it is essential to have historical information with events precisely assigned and input variables appropriately captured. If biases or errors are left in the data without being dealt with, they will get passed on to the model, bringing about erroneous models. It is smarter to utilize Unsupervised Machine Learning to have historical data with events accurately assigned. It sees an obscure pattern and results. It recognizes suspicious activity without earlier information of exactly what a money-laundering scheme resembles. What are the different techniques to detect money laundering? K-means Sequence Miner algorithm: Entering banking transactions, at that point running frequent pattern mining algorithms and mining transactions to distinguish money laundering. Clustering transactions and dubious activities to money laundering lastly show them on a chart. Time Series Euclidean distance: Presenting a sequence matching algorithm to distinguish money laundering detection, utilizing sequential detection of suspicious transactions. This method exploits the two references to recognize dubious transactions: a history of every individual’s account and exchange data with different accounts. Bayesian networks: It makes a model of the user’s previous activities, and this model will be a measure of future customer activities. In the event that the exchange or user financial transactions have. Cluster-based local outlier factor algorithm: The money laundering detection utilizing clustering techniques combination and Outliers.   Conclusion For banks, now is the ideal opportunity to deploy ML models into their ecosystem. Despite this opportunity, increased knowledge and the number of ML implementations prompted a discussion about the feasibility of these solutions and the degree to which ML should be trusted and potentially replace human analysis and decision-making. In order to further exploit and achieve ML promise, banks need to continue to expand on its awareness of ML strengths, risks, and limitations and, most critically, to create an ethical system by which the production and use of ML can be controlled and the feasibility and effect of these emerging models proven and eventually trusted.