Artificial Intelligence and Machine Learning are being hailed for spurring innovation in many industries, but there is one specific field that is a perfect fit for these technologies, a field that relies heavily on data and data processing — The insurance sector. The use cases for insurance cover a wide range, from faster processing to fraud detection. Join us as we take a closer look at how AI and ML could revolutionise the Indian insurance sector:

Better sales and customer support for a multilingual nation

With multiple languages in use across the nation, it becomes imperative for the insurance industry to cater to regional needs. Tools using the next generation of text recognition, chatbots, and interactive apps are a great way to provide the first level of support to prospective and existing customers. It is here that ML-based language tools can make a difference by adapting to region-specific language usage. New marketing tools also offer the prospect of greater customisation with lesser human involvement

Accurate risk assessment

Accurate assessment of risk is the foundation of the insurance industry. AI and ML can offer an advantage to early adopters here — in two disparate areas. Data (and actuarial tables) are used to come up with accurate models of risk, whether it’s health insurance, life insurance, or even motor insurance. Advanced processing that takes into account multiple factors, of the type only AI and ML systems can provide, will make this even more accurate, leading to a lowering of costs.

Newer, AI-based techniques, such as computer vision and IoT can also be used to map risk better. For example, with enough training, AI systems might be better at reading flood risk from satellite images than a human surveyor. It’s also possible to keep risk information up-to-date by allowing AI systems to factor in data from IoT sensors. As an example, consider the field of motor insurance. Traditionally, an insurance provider would have taken into account only a few variables (age, gender, history of claims) to personalise a quote. Today, it’s possible for a company to assess risk more accurately through active monitoring of driving habits — In the United States, the Root Insurance Co is using app-based monitoring to generate a driving score, which is then used as the main factor to calculate premium.

Claims processing

As with risk prevention. AI and ML based processes can make claims processing faster and more accurate by removing human bias and subjectivity. For example, computer vision could be used to analyse property damage, OCR could quickly transcribe surveyor reports, while ML systems could quickly analyse multiple claims to detect discrepancies or deviations from the norm.

Fraud detection and prevention

Fraud remains one of the biggest pain points for the insurance industry. As claims processing becomes more automated, it will also boost insurance companies’ ability to detect and prevent fraud. Shift Technologies is already offering a claims automation and fraud prevention platform that promises a 75 per cent hit rate for fraudulent claims. These next-generation tools not only reduce human involvement, but also use advanced processing to detect customer behaviour and other patterns that may suggest the likelihood of fraud.

And finally, all these techniques will eventually come together in a wave of mobile-centric, app-based services. Whether it’s IoT devices or phone apps that analyse your driving, or secure recording that uses multiple sensors to get an accurate view during a claims survey, enhanced mobility may one day help cut down operating expenses for insurers.

Source: INDIAai