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The Journey of Machine Learning Through Banking & Insurance Industry

When we take into account the insurance and banking industry, data plays a central role in it. Insurance is a people-centric business. A business that heavily relies on an individual’s data to the maximum extent possible. And the data Insurance providers and carriers have access to more of such data than ever before. The amount of data, we as humans have created in the past two years is simply unprecedented. 

Insurers have been left flabbergasted by the explosion in data from a plethora of sources. They include telematics, online and social media activity, voice analytics, connected sensors and wearable devices. In order to steadily process this data, insurance companies and banking service providers need machines, the ones that are not only capable of handling this insurmountable amount of data but also can process, analyse, and provide insights considering that data.

What is the current scenario?

Even though the change has been rapid for many, the industry leaders have made sure that they do not remain at bay when the stakes are high. We are seeing a gradual and steady change. This change is propelled by increased competition, elastic marketplaces, complex claims and fraud behavior, higher customer expectations and tighter regulation.

With everything falling under one umbrella, insurers want to make sure not a single aspect gets exploited or should exploit their working environment. This has brought them to the crossroads of predictive modelling and machine learning where they can make sure that their competitive edge is maintained and the business operations get boosted.

Additionally, there have been recent advances in Artificial Intelligence and Machine Learning that have helped them ease business challenges across the insurance value-chain. Some of those challenges were underwriting and loss prevention, product pricing, claims handling, fraud detection, sales and customer experience etc.

How ML and AI are changing the Insurance paradigm 

In order to understand how Artificial Intelligence is changing the insurance sector, let us understand what AI is. In general terms, Artificial intelligence is a set of a computer system which has the ability to sense, comprehend, act and learn on its own. It comprises of the ability that lets it perceives the world around it, analyze and understand the information it receives. 

In return, AI can also take actions stemming out of that understanding, and improve its own performance by learning from the events that may have occurred in the timeline. In short, it learns from experience just like us humans. We learn and adapt to the experiences that we have by interacting with people and the environment around us. Similarly, by enabling machines to interact in the most natural way, with their environment, with people and with data—the technology gets the ability of both humans and machines. 

In terms of applied intelligence, this method goes a step above. It is the ‘application’ of intelligent technology and human ingeniousness working at the core of the business. This makes it possible for enterprises to address their most complex challenges, break into new markets or generate fresh revenue streams.

Interesting takeaway

How can insurance companies utilize the maximum potential of ML and AI? Here is one way they can do that. Imagine that you are driving a car on the highway and the journey has been long and tiring. The Artificial Intelligence system installed in your car senses that your eyes are droopy and that the overall speed, handling, braking, and the way you operate your car on most days is slightly off today.

The speakers inside your car come to life and you hear a voice telling you to park your car and get some rest. You resist and say you are good and don’t need a long break. 

The voice tells you that after analysing the number of hours you have been driving, the speed at which the car is running, the overall efficiency of the driving conditions, and calculating the restless position of your head, it feels that you must take a small break if not complete rest. The voice on the other end offers to buy a coffee for you from the next shop on the highway.

You think that’s a good option and agree conclusion for the coffee. With enough data and deep learning at its disposal, the AI, through the power of telematics (branch of information technology which deals with the long-distance transmission of computerized information) places the order and buys a coffee for you at the next station. 

For insurance companies, the price for a cup of coffee is infinitesimal compared to one huge accidental claim.   

How will the future look like?

  1. Smarter environment – Small and large enterprises are putting to use the advanced machine learning systems that drive smart, automated applications in fields like healthcare diagnosis, predictive maintenance, customer service, automated data centers, self-driving cars and smart homes.
  1. Omnipresent open source – Data is now becoming ubiquitous. There are open-source processes emerging in order to make sure that data gets shared and used across all paradigms. There are a range of different public and private entities who are creating an ecosystem to share data on multiple use cases under a common regulatory and cyber security framework.
  1. Internet of Things data – In the present scenario, volumes of data is being generated from IoT based devices and equipment. The speed at which this data is being generated has helped drive the overall need to automate the generation of actionable insight using advanced machine learning tools. 

A Gartner research says that, by 2020, 20 percent of enterprises will employ dedicated people to monitor and guide machine learning (such as neural networks).

  1. NLP– The field of Natural-language processing algorithms is growing. A number of programmers and researchers are interested towards it. With AI getting more dexterous with time, it now understands spoken language and facial expressions better than ever. And as surprising as it may sound, these algorithms are evolving in unprecedented manners. A fitting example is of Google when it found out that Google Translate invented its own language to help it translate more effectively.


When we talk about insurance companies, if you will see around the globe, there are still a lot of insurance companies who process less than 20 percent of data that they have. And a major chunk of this is structured data being housed in traditional databases. The problem with this is that it does not help them unlock significant value from their structured data and makes them overlook any valuable insights hidden in their unstructured data.

The power to analyse the unstructured, scattered data and utilizing the same to deliver results resides in advanced data science techniques. Advanced data analytics technologies which are engineered around machine learning aimed at bringing order and purpose to this unstructured data.