What is deep learning?

25 August 2021 · By Irene Martínez

Digital transformation in the insurance industry is great news for insurers and policyholders alike. More and more, insurance companies are utilizing artificial intelligence, visual intelligence, and deep learning to fight fraud and increase their credibility.

AI and VI are forms of machine learning, but deep learning is something altogether different — and a little more complex for insurers to understand.

The basics of deep learning

Have you heard the saying, ‘Data is the new oil’?

It refers to the fact that, like raw oil, raw data on its own has little commercial value — you need the structures and capabilities to process and analyze that data, to draw insights and answers.

Big data from social media, eCommerce platforms, streaming services, wearable technology, and countless other sources has become more highly prized than currency in this digital age. However, despite being readily available through cloud networks, the sheer amount of data is unstructured and nearly impossible for humans to sort through. It could take decades of human interaction to find anything valuable, by which point the data is outdated and irrelevant.

The potential that big data presents is too valuable for companies to pass up — and that’s one of the biggest triggers for today’s artificial intelligence revolution. Insurers can utilize AI to sift through the mammoth piles of data while deep learning can take data and create patterns that can be used in decision making.

Deep learning describes algorithms that analyze data with a logic structure similar to how a human would draw conclusions — but on a far greater scale and at a way faster rate.

To achieve this, deep learning applications use a layered structure of algorithms called an artificial neural network (ANN). The ANN’s design is inspired by the biological neural network of the human brain, making it far more capable than standard machine learning.

Object recognition, speech recognition, and language translation are just some of the tasks performed through deep learning.

Deep learning and machine learning: what's the difference?

Machine learning is one of the most popular AI techniques — a self-adaptive algorithm that improves its abilities when fed fresh data. And while the names ‘machine learning’ and ‘deep learning’ are often used interchangeably, these are in fact two, separate functions.

Deep learning algorithms represent a sophisticated and mathematically complex evolution of machine learning algorithms. Deep learning requires far less human interaction than standard machine learning. It can identify, classify, and process data on its own. That said, deep learning requires a vast amount of data in order for it to hold true value.

Deep learning also utilizes a hierarchical level of artificial neural networks to carry out the process of machine learning. So, while machine learning builds data analysis in a linear fashion, the hierarchical and almost human processing approach of deep learning systems enables the AI to process data with a nonlinear approach.

This may sound like too much technical detail, but the tasks that deep learning can perform should be of interest to anyone working within the insurance sector...

The impact of deep learning for insurers and the insured

Deep learning is already being used in many different industries and in a wide variety of applications. Apps that use image recognition — like an iPhone’s in-built image gallery — and platforms offering personalized recommendations — like Amazon’s ‘Recommended for you’ — are both examples of deep learning in action.

Within the insurance industry, deep learning can improve the end-to-end process for insurers and the insured. Let’s take a look at some of the workflow optimizations deep learning is helping us achieve.

Fraud detection

We talk about AI and fraud a lot at Bdeo, and with good reason. Fraud is one of the most significant, growing threats to the insurance industry, while AI is one of the best weapons we have to regain control.

Deep learning can quickly identify signs of fraud as it compares each new data against previous claims data. Likewise, if a claim seems legitimate, deep learning has the potential to approve the claim within seconds.

The majority of insurance fraud lies in the claiming stage, as the claimant can fake or exaggerate key details. Deep learning can help identify these inconsistencies by comparing against similar claims and flagging something that doesn’t seem to fit with previous datasets.

Fraud reduction is vital to rebuild the relationship between insurers and their customers. Thanks to AI, we are already seeing a stronger sense of trust between the two.

Claims management

Claims management is one of the most burdensome and repetitive roles within the insurance field. Traditional methods involve claims assessors gathering data manually — a process that can take days or even weeks.

In the meantime, the claimant’s memory of an incident can become increasingly fuzzy, making it hard for the assessor to disseminate the facts from potential attempted fraud.

Deep learning speeds up the claims management process, minimizing the risk of misremembering and getting claimants the answers they need in less time.

Customer satisfaction

A reduction in claim handling time isn’t the only advantage for policyholders.

The era of personalized service is making a comeback in a big way, but it can be difficult to provide that en masse while still keeping costs down. This is where chatbots come into play.

Chatbots are increasingly taking over the customer service sector, offering fast and helpful experiences without the need for a human agent. Deep learning is continuously improving chatbots interactions, from simple keyword recognition to complex customer queries.

Deep learning can also be used to personalize plans — giving a policyholder the features they actually need rather than pre-packaged bundles designed for mainstream consumption. In this way, embracing deep learning within your customer service is a surefire way to boost customer satisfaction and drive new business.