Today’s rapid technological development is causing fundamental changes in the insurance industry. Organisations are looking for ways to adopt the likes of AI and Automation to increase operational efficiency and provide a unique customer experience. Implementing such solutions into an organisation doesn’t happen overnight, but the rise of innovative start-ups emphasise that change needs to happen soon, or risk losing your place in the market.

Most of you will be familiar with Fintech, which are organisations (mainly start-ups) that utilise the latest technology to disrupt the Finance industry. Well, Insurance has a name for disruptors too. Insurtech. As the name suggests, it is the meeting point between ‘Insurance’ and ‘Technology’.

The term has been growing in popularity with the continuous introduction of new companies that capitalise on the inefficiencies of the older providers. Technologies such as Internet of Things, Arial and Satellite imagery are giving insurers new ways to collect their data, whilst Artificial Intelligence (AI), Machine Learning and NLP are making it easier to analyse and generate insights from this data.

Whilst many insurers are progressing from fully manual processes to rule based automation, they are far from utilising cutting edge solutions to employ truly agile processes to optimize risk assessments, pricing generation and customer satisfaction.

Insurtech start-ups are very attractive to customers due to their tailored products and offerings, rather than one-shoe-fits-all.

Customers do not want to spend hours filling out an application form and then waiting weeks to receive a result. These new organisations are offering solutions to process everything in real time.

InsurTech companies simplify this process of buying business insurance online by blending Artificial Intelligence and Natural Language Processing.

Underwriting is a key function in the Insurance industry. This is where Insurers evaluate the risk of insuring something, such as a house, person or vehicle. The ability to automate this process have provided InsurTech’s a huge competitive advantage. AI solutions have enabled Insurers to:

Collect larger amounts of data: By utilizing artificial intelligence, underwriters are given access to a broader range of data sources.

Automation tools can be used to streamline backend processes to extract, transform and store data as needed for the end user. This is a key step before looking to apply any analytics.

By collecting data via IoT and geographical tools, insurers can leverage non-traditional data to help understand non-traditional risk.

AI-enabled insurance data capture solutions can process customer data far faster and with greater accuracy.

In addition, AI-based data capture transcends the current data silos that exist in underwriting to provide advanced analysis of each customer's risk profile by allowing data from multiple sources to be analysed together.

Risk: With big data analytics, insurance companies can gain a deeper understanding of customers' risk profiles, enabling them to tailor premiums to match each individual's actual risk profile.

This has positive consequences for overall profitability by generating a more attractive risk profile with broader segments, as a result of the increased dataset being analysed. Big data analytics also allows for more transparency in the decision-making process.

The more data that is fed into the algorithms, the more it can continue to learn a optimise the risk management process. Streamlining this process saves underwriters a lot of time having to go through the time-consuming due diligence process.

Reduced Human Error: Algorithms can reduce the time and number of errors as information is passed from one source to the next. AI can be used to conduct data collection and aggregation with pre-built rules, freeing up time for a human underwriter to make informed decision on the output.

On the whole, AI will make life a lot easier for underwriters and improve the quality of service and cover customers receive.

AI will revolutionize the underwriting process by leveraging data on a scale beyond practical human management and in a way that allows for continuous input.

This strongly tailored approach is based on the depth of data collected and leveraging this in the most effective way.

As one of the most critical aspects of Insurance, claim handling is one area where many insurers can improve their business model by streamlining the end-to-end claims process.

Visual Assessment: The latest development in claims analytics is Visual Assessment. By analysing images of properties or vehicles, algorithms utilise computer vision technology to conduct an impact assessment and generate a cost of damage and well as use this to accurately develop a new insurance premium.

Bdeo, a Series A start-up, utilises computer vision technologies, allowing customers to assess damage to a vehicle through taking a simple photo at First Notice Of Loss (FNOL).

Using visual intelligence, the algorithm produces the cost of damage within minutes.

Automated Claims Support: AI powered chatbots can reduce the number of FTE hours needed to process claims.

A touchless insurance claim process driven by Artificial Intelligence provides a manner in which insurance companies can process claims, capture damage, update systems, and communicate with customers without the need for excessive human intervention.

This claim process significantly reduces the effort required by clients.

Fraud Detection: Insurance fraud brings significant financial loss for the insurer. Data Science platforms are making it possible to detect fraudulent activity, detect subtle behaviour patterns and suspicious links.

A better view of this can be gained from incorporated third-party data sources on top of historical claims data to provide an overview of industry activity. Some InsurTech Start-Ups incorporate Fraud Detection algorithms this into their streamlined Claims process.

In anticipation of further technological advancements, Insurance companies are already experimenting with new ways to incorporate AI into their day-to-day operations.

Despite the infancy of AI adoption, companies should look to continue testing the waters of AI if there are to stay competitive.