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5 Deep Learning Use Cases for the Insurance Industry

Nidhi Agrawal
4 minutes, 9 seconds read

In 2010, with the launch of the Image Net Competition, a vast dataset of about 14 million labeled images was made open-source to inspire the development of cutting-edge image classifiers. This was when Deep Learning technology got its a real breakthrough and since then there’s been no looking back for advancements in this field.

Different industries are actively using Deep Learning for object detection, features tagging, image analysis, sentiment analysis, and processing data at extremely high speeds. The bigger benefit that differentiates Deep Learning from other AI and ML technologies is the ability to train vast amounts of unstructured data in near real-time. Organizations with a strong focus on data are already about 1.5 times more likely to invest in Deep Learning for actionable insights — Forrester Predicts.

What makes Deep Learning Technology so sought after?

Let’s take a look at 5 Deep Learning use cases from an insurance perspective.

5 Noteworthy Deep Learning Use Cases in Insurance

Deep Learning (DL) is a branch of Machine Learning, which is based on artificial neural networks. DL techniques are specifically useful for determining patterns in large unstructured data. It is highly beneficial for assessing damages during an accident, identifying anomalies in billing, etc. that can eventually help in fraud detection and better customer experiences.

The insurance industry can leverage Deep Learning technology to improve service, automation, and scale of operations. 

1. Property analysis

Typically, insurers analyze a property only once before quoting an insurance premium. However, a customer may remodel the property, for instance, install a swimming pool. 

Under such instances, Insurers can proactively modify the insurance coverage with the help of deep learning technology. In fact, with DL technology, Insurers can help their customers with predictive maintenance, fault analysis, and real-time support. 

For example, Enodo provides underwriting for multifamily properties. It allows users to analyze historical rent, concession data, and market values. Such data-driven tools are also a great aid for insurers.

2. Personalized offers

Insurers are seeking different ways to enhance the customer experience. Deep Learning can vividly improve interaction experiences at different customer touch-points. Take for instance — marketing outreach. Through personalized recommendations and dynamic remarketing strategies, insurers can achieve better conversions. McKinsey states that personalization can reduce customer acquisition costs by up to 50%

At the core of these strategies lies Deep Learning technology. DL technology can make logical classifications of unstructured data through unsupervised learning. We’ve already seen product recommendations based on our own preferences, browsing/search patterns, and peers’ interests. The same applies to the insurance industry, especially when insurers endeavor profits through bite-size and on-demand insurance products.  

3. Pricing/Actuarial analysis

Actuarial analysis and evaluation are both time-consuming and error-prone processes. Insurers can considerably improve policy pricing through automated reasoning. Deep Learning techniques combine statistics, finance, business, and case-based reasoning and can assist actuaries in better risk assessments. Accenture reports — Insurers are leveraging machine learning for underwriting in P&C (56%) and life (39%) insurance sectors

  1. Explainable AI (XAI) is capable of adopting and implementing AI across all capacities of the actuarial profession. 
  2. Pattern recognition from historical data can help assess the risk and understand the market better.
  3. Deep Learning can help in pragmatic actuarial solutions to make effective decisions on large actuarial data sets.

4. Deep Learning Use Cases in Fraud Detection

In Norway alone in 2019, there were 827 proven fraud cases, which could have caused a loss of over €11 million to insurers.

Insurance fraud usually occurs in the form of claims. A claimant can fake the identity, duplicate claims, overstate repair costs, and submit false medical receipts and bills. Mostly because of disconnected information sources, Insurers fall victim to fraudulent activities from customers. Now, here’s the challenge. How to unify different data sources, which, to date, even include offline receipts and manually scanned documents. 

Deep Learning can help in fraud detection by-

  • Finding hidden/implicit correlations in data.
  • Facial recognition, sentiment analysis on submitted claims application.
  • Supervised learning to train the fraud detection models using labeled historical data.
  • Eliminating the time lag in the verification of documents, which raises the potential for data breaching.

5. Claims

Deep Learning incorporates two-fold benefits to insurers in terms of claims. One — with a connected information ecosystem, it helps insurers with faster claims settlement (thus, customer experience as well). Two, deep learning predictive models can equip insurers with a better understanding of claims cost. 

For example, Tokio Marine — the largest P&C insurance group in Japan uses a cloud-based document processing system to process handwritten claims from the time of the first intimation. Many insurers are looking forward to end-to-end claims processing systems with deep learning and other AI capabilities. 

5 Challenges in AI implementation for Insurers - Featured Image


Are you facing challenges with AI implementation? Are you seeking advice on what to automate and when to augment?

Join our Webinar — AI for Data-driven Insurers: Challenges, Opportunities & the Way Forward hosted by our CEO, Parag Sharma as he addresses Insurance business leaders and decision-makers on Feb 13, 2020.

Register NOW!

The Crux

Today, Deep Learning technology is able to mimic an infant’s brain. The research is on for developing new neural network architectures (e.g. Siamese Network, OpenAI’s GPT-2 Model, etc.) that will be capable of performing complex functionalities of a mature human brain. Deep Learning technology, in the near future, will be leading the development of cognition-based insurance systems.

Also read — The Cognitive Cloud Insurer is Next!

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Insurance consumers around the globe are seeking convenience and expecting better customer experience. From millennials to Gen Z, with the agile connectivity, irrespective of the industry has numerous options to choose from. As the competition intensifies the insurance industry has to jump into the bandwagon of technovation in order to provide improved accuracy, cost-saving and excellent customer experience. 

Here is a list of the marketing trends in insurance that will prove to be a game-changer in the year 2020.

1. Robo Financial Advisors

According to a Business Insider Intelligence forecast, by the year 2020 Robo-advisers will manage investment products worth $1 trillion, which will spike up to $4.6 trillion by as early as 2022.

Robo advisors have been around for quite some time. In the year 2008, during the financial crisis, Jon Stein, a 30-year old entrepreneur launched “Betterment”, the first Robo-advisor. In recent years due to its low investment rates and data input based research results, it has increased in popularity. 

It is basically designed for the people who want to manage their finances with low management cost. Based on respective data inputs, the Robo-advisors offer any advisory services. 

The main purpose behind the making of the Robo-advisor is to bring the financial services to the wide range of population with lower investment cost as compared to the traditional human advisors. Upwardly.com, 5Paisa.com and Goalwise.com are some applications of Robo-advisors.

Behind the scenes of the software of Robo-advisors are actual human beings who track the market regularly and adjust the algorithms based on the current market condition. Robo-advisors are a boon to the end-users as they can invest in direct plans of mutual funds without shelling any commission. However lack of personalization and one-size-fits-all products are the areas of improvement.

2. Data Integration: The Future of Marketing

IDC estimates that, by the year 2020, the digital cosmos will reach 44 zettabytes, further complicating the lives of marketing professionals.

Integrating data sources is vital for any company, whether B2B or B2C to successfully meet Customer Experience expectations thereby drive accelerated sales revenue.

With an integrated source of information, retailers can administer and optimise marketing through KPI’s, metrics and dimensions that would not have been possible with the separate source system. In order to upscale marketing operations, a connected viewpoint is essential to evaluate the campaigns, audiences, events and channels, and drive the strategic goals.

From an operational viewpoint, CRM solution provides the organization with new business and the ERP system allows to manage and drive businesses around obstacles. A good place to start with the data integration is by Integrating these two systems shall provide marketers and the organizational sales-force with vital information, that can be shared with the stakeholders.

3. AI-driven Copywriting

Artificial intelligence can create cancer combating drugs, control self-driving cars, defeat the best brains at incredibly complex board games, but one realm it can’t perform flawlessly is communicating.

To help solve the issue, Google has been feeding it’s AI with more than 11,000 unpublished books, including 3,000 steamy romance titles. 

Autoencoder, a type of AI network, uses a data set to reproduce a result (in this case copywriting) using fewer steps. Insurers can harness this AI capability to create sentences and suggest the best-optimised language to approach the customers.

AI copywriting is evolving to a whole new level. Google granted  €706,000 (£621,000) to the Press Association, to run a news service with computers writing localised news stories. AI with the help of human journalists can write up to 30000 news stories a month and scale up the volume of the stories that would otherwise be impossible to produce manually.  

“Skilled human journalists will still be vital in the process, but Radar allows us to harness artificial intelligence to scale up to a volume of local stories that would be impossible to provide manually. It is a fantastic step forward for PA.”

  • PA’s editor-in-chief, Peter Clifton 

4. Gamification of Insurance

At the nexus of marketing trends ranging from social networking to the IoT to behavioural science and wearable tech;  gamification is a powerful lever for insurers and insurance agents. It creates an enriching digital experience and customer-centric business model.

Gamification offers great potential value to the insurance business process in the realm of consumer engagement and customer experience. From millennials to Gen Z, it has emerged as a useful practice and effective means to target early technology adopters by:

  • Transforming mundane tasks into interesting and fun experiences that keep users returning.
  • Increases brand awareness, brand penetration and affinity.
  • Increase sales by educating customers about product suitability and guide them to buying the product.
  • Motivating people to act in areas of healthcare and wellness, safe driving, financial planning and sustainability.

Ingress and AXA redefined the world of gaming and advertisement. December 5th, 2014, Niantic Labs the creator of ‘Ingress’ partnered with AXA. In the game, AXA Shield was initially only obtainable from AXA Portals, leading you to AXA business locations in person.

5. Advanced AI Capabilities in Insurance

Innovation and technology are the next frontiers in the insurance industry. While automation and IoT are already a reality for insurance, with the advent of AI there has been a holistic approach to Insurance automation. With insurance leveraging AI, it has expanded its reach to more ecosystems than ever before. Deploying AI capabilities in insurance can help make smarter underwriting decisions, fraud detections, risk assessment and create a better customer experience.

AI is driving significant change in business with insurance being no exception. It has the potential to enhance the insurance business model by-

  1. Improving the speed of the workflow: AI and RPA in insurance reduce redundancy of task. Automation of day to day tasks would reduce cost and time consumption thereby increasing accuracy, quality and competency.
  1. Customizing the services for better customer experience: One size no longer fits all, and the same goes for the insurance industry. With focus on individual markets, insurers can create niche usage-based products to sell the packages in a variety of ways.

Parag Sharma, CEO, Manta Labs and AI thought leader is going to speak about the Internet of Intelligent Experiences™: CX for the Digital Insurer at India Insurance Summit and Awards 2020 on March 12, 2020. Catch him live at IISA 2020.

Details

  1. Providing new insights: Insurance is no guessing game. Data in silos is the biggest drawback for any industry. AI in insurance can integrate this data and provide analytics to help actuaries have a better insight while making a decision about a product.

Marketing Trends in Insurance: The Bottom Line

Today, at the core of marketing in Insurance, lies AI, Machine Learning and advanced data analytics to foster better experiences for the end-user. We’ve listed 5 most important trends that have the potential to shape marketing business models for Insurance and InsurTech firms. Be it Robo financial advisors or gamification, impressing customers remains the prime goal for Insurers.

Have thoughts and queries regarding upcoming marketing trends in Insurance? Please feel free to drop us a word at hello@mantralabsglobal.com.

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Data Science is enormous. It brings forth a scientific approach to gather a massive amount of useful data from raw & disordered information (often collected from open sources). According to recent research, over 2.5 million terabytes of data appear daily. In 2020 every person produces 1.7 MB of data per second. Scientists, Analysts, and numerous other specialists use this data to derive decision-ready insights.

Using data science, marketers can get a clearer picture of their target audience. With this knowledge, any organization’s marketing department can formulate strategies to target customers who portray higher chances of conversion. Also, by delivering values, organizations can eventually maximize revenues. Going with the traditional methodologies, data processing can be a daunting task. Data Science offers a cost-effective solution to businesses seeking data-driven insights.

Let’s delve deeper into 5 most profitable and practical use cases of data science in marketing.

1. Budget Optimization

The primary goal of any marketer is to achieve the highest possible ROI from the allocated budget. This objective is undoubtedly difficult and time-consuming. On top of which, because of changing market dynamics and user preferences, strategies often go off the track leading to unanticipated outcomes.

Data science can be a saviour here. By analyzing the marketing department’s spending and acquisition ratio, organizations can build a model to distribute the budget in the smartest way possible. A clear picture will help marketers to invest money in the most relevant and surplus channels, thus optimizing key metrics.

2. Defining Audience Persona

While every marketer is familiar with the process of building the target audience portrait, determining the exact persona of the potential customer can still be a challenge. The lack of proper data insights might lead to ineffective advertiser decisions leading to a waste of resources.

Data science methods help marketers to understand the user persona and their preferred communication channels with data-driven insights. This means that the marketing budget will be spent on the right channels of influence, ignoring the irrelevant media, which a normal human being will think of covering for “just in case”. Such adjustment will inevitably increase the ROI and optimize the entire advertisement campaign. This will also retain brand relevance to the customers.

[Related: Your shopping cart just got a lot smarter!]

3. Brand New Social Media Marketing Strategy

Social media trends change faster than a human can track it. Facebook, LinkedIn, and Twitter define what is popular, and a marketer has to catch up with the trends.

Data science can keep you on track with the changing trends. Using the logic of Data Science in Marketing, one can get a bigger picture of what type of content people like interacting with. Data science allows us to gather and analyze data about people’s online behaviour. It provides the key metrics to adjust the SMM (Social Media Marketing) goals, which include – the time of posting, content type, amount, etc. These simple adjustments using data science insights can help increase the marketing ROI drastically.

4. Clearer Content Strategy

One of the biggest gaps between planning and execution that marketers face is knowing which channels will be affected and what kind of people will interact with their content and with what sentiment. Will be potential customers? Are interactors content gatherers? Are they the competition? Do they intend to ruin your reputation?

Knowing all this information will help streamline your content strategies.

As long as you know who your customers are; what are their perceptions about your brand; what information can attract/repel your customers; what social channels they are mostly active on; what are their sentiments with your content; what they usually do when they like or dislike a content; you’ll know what type of content you should produce.

For instance, some people hate emails, while others adore reading them. Some people want to resolve their queries publicly on social media, which some care about their online image. Data science can help achieve personalization to some extent, which can help humanize the conversations with your followers.

Let’s take another example of how data science in marketing can help stakeholders. It gives marketers insights about what phrases a customer would use while searching for a product/services online. Marketers can utilize this insight and prepare a content strategy that embeds these terms more often in your posts and articles.

Therefore, we can say that data science brings a variety of actionable insights about customer acquisition channels, their preferences, and engagement style, which can help plan content strategy accordingly.

5. Increasing Customer Loyalty

Your best customers are the ones who will not just purchase your product once but also will repeat buying and bring their friends and relatives to your store. Organizations realize that customer retention is easier than acquiring new customers.

But consolidating loyalty may be tricky. Data science can provide the marketing department with all the necessary information that can help boost customer loyalty. Based on purchase history and current search queries, analysts can predict their customer’s inclination towards a product. Accordingly, brands can create the most relevant offers for their customers. With personalized offers, existing customers feel special and will return to your brand and not go to the competitors.

The Essence of Data Science in Marketing

Using data science in marketing may ease the work of employees and uplift your strategies to new heights. We have to admit that the more structured information marketing teams have, the more effective their strategies become. At the core of any marketing efforts, data science can optimize cost for data processing and result in overwhelming conversion rates.

[Related: 5 Deep Learning Use Cases in Insurance]


About the Author: Marie Barnes is a writer for Bestforacar and an enthusiastic blogger interested in writing about technology, social media, work, travel, lifestyle, and current affairs. She shares her insights with the world through blogging. You can follow her on Medium.

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