Technology is increasingly becoming centerstage for all businesses across the world. In the last decade, we saw increased adoption of digital operating models and digital transformation of businesses featuring in board conversations. The next decade will surely belong to data and its effective use through AI and ML. AI & ML are by no means new buzzwords in the tech lexicon and we will continue to witness their increasing deployment at scale and becoming far more ubiquitous across organizations and industries. A report by PwC India indicates that the largest rise in the use of AI during COVID-19 has been observed in India. Among other sectors, 82 percent companies in financial services, have adopted it during the year.
Insurance has usually been considered a laggard in the adoption of new and emerging tech. However, it is a perception that the industry has already started to shed. In today’s age of ‘instant everything’, AI is critical in powering real-time decision making. Given the change in consumer buying behavior, there will be an accelerated pace of experimentation and adoption of emerging technologies. AI will lead and take an increasingly dominant position as a transformational tool, driving disruption in the insurance business for sure.
Embedding AI across the insurance value chain can deliver undeniable and compelling value to Insurance business through multiple use-case scenarios. Some of which are:
Claims Experience: Enabling hassle-free experiences for customers will need to be at the forefront of the change technology will drive. Smart use of AI will help smoothen claims experience and faster claim settlements. From data capture, authorization and approval, payment tracking, legal matter processing to communication management, AI can bring in efficiency in every aspect. We are already seeing Bots being used to review claims, verify policy details, check for fraud and process payments, thereby making the claims process faster and efficient.
Customers Engagement: AI can help mine and process the internal and external data towards creation of personalized product, service and experiences for customers. NLP is helping organizations understand human language, emotions, behaviour and expressions consistently and in real time. The applications of this capability in a business such as Insurance are limitless. With chatbots, customer queries related to their policy, complaints, registering grievances, etc. can be attended to 24×7. The bots can be integrated with various channels such as websites, social media, and others, allowing better engagement and conversation with customers in their preferred mode of communication, to create a more seamless, automated and personalized experience.
Operating Efficiency: Automating intelligent decision making at scale can be enabled by AI. This will help increase the scalability of all processes. Decisions involved in underwriting, claims adjudications, premium loading, etc. can be executed through a combination of AI and ML, which can lead to tremendous operating leverage for a business.
Product innovation: The millennial digital-savvy customer demands quick, convenient, pocket friendly solutions to their ever-changing needs. There is a huge opportunity in personalization and efficient use of analytics to bring relevant products to today’s consumer. The advent of AI, ML and Big Data will only help insurers to optimally leverage these and build products that are relevant. The availability of data helps better understanding of customers, in identifying behavioral patterns, insights, etc., that further enable designing and offering value-based solutions and engage with customers with personalized solutions.
Fraud Detection and Prevention– AI can help insurers spot unusual patterns and uncover hidden correlations and patterns missed by the human eye, making it useful for detecting suspicious activity or fraudulent behavior. AI can also be used to interpret behavioral cues and look for patterns indicating a potential fraud, to help insurers take steps to mitigate such risks. For eg: a customer filing unusual claims or a sudden spike in claims in a specific segment/ geography, etc. are potential red flags that can be detected with smart deployment of AI. Even video recording of customer’s claim narration can be translated using AI as an input towards the veracity of the claim.
To achieve successful implementation of AI with visible results, some of the imperatives that should be in place:
- Robust data architecture supported by strong governance to manage internal and external data
- Technical competence with a right mix of skill sets across data analysis, data engineering, statistical modelling and new-age visualisation tools
Choosing the right platform for AI implementation in order to allow for easy integration and scalability given the high amount of data handling. The platform will be able to drive AI into your processes but also allows you to leverage other technologies, remain competitive, breaking silos and ensuring process control and optimization.
How have we adopted and implemented AI and data
Edelweiss General Insurance is a cloud native insurer with a fully digital operating model. For us, AI is a strategic business tool that will help achieve our vision of helping people to lead happier, safer and healthier lives.
A robust data architecture is a key foundational element that powers all AI and ML initiatives. Our Data Lake architecture enables efficient capture of internal and external data across all categories including high velocity streaming data. The focus of our AI initiatives is on automation of claims adjudication because this will have the most meaningful impact for customers. Claims experience continues to be a source of huge dissonance for customers and this is the first area we want to transform using AI.
We will continue to invest in cloud-based technologies and a serverless architecture that can easily scale to meet our future growth plans. Going forward, we are looking at using AI for intelligent pricing and product innovation. Globally there is a growing interest in on-demand insurance, subscription-based models and wellness integrated health insurance. We aim to invest in developing these categories by introducing digital first products. We will also develop ML driven personalization model to support these new products.
We have a firm conviction in the ability of AI and ML to have a transformational impact on the Insurance business model. We have got the building blocks in place; apart from developing inhouse capability and assets, we will also be partnering with the external ecosystem to drive maximum business impact.
Authored by Shanai Ghosh, Executive Director & CEO Edelweiss General Insurance
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