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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.
- Explainable AI (XAI) is capable of adopting and implementing AI across all capacities of the actuarial profession.
- Pattern recognition from historical data can help assess the risk and understand the market better.
- 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.
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.
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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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