Driving Opportunities for Insurers through Artificial Intelligence
The rising impact of digital technologies is providing organizations with a huge amount of data, thus opening up various avenues to Big Data and Analytics. Analytical models can predict outcomes, identify patterns and highlight outliers among other umpteen number of business cases.
This progress in Data and Analytics has led to this revolutionary concept known as Artificial Intelligence (AI).
AI, simply put, is the concept of enabling machines to perform cognitive functions that are associated with human brains. In AI, machines are enabled to think, learn, and solve problems on the run in real-world scenarios
Applications of AI
The concept of AI has existed for quite some time, and its application can be seen in our daily lives. One of the best evident examples is the visual effects in Hollywood sci-fi movies like Transformers, Matrix etc. AI has also found real-world applications like airplane autopilots, email spam filters, and autonomous cars. Technology has evolved and matured since its early utilization, making it even more powerful.
Today, AI has become the buzzword in the world of technology and is the latest disruptor that is presenting the world with endless possibilities across industries!
Benefits of AI in the Insurance Domain
Application of Artificial Intelligence in the insurance industry will change the way companies carry their business. Each of AI’s multi-facets, i.e., Machine Learning, Text Analytics and Natural Language Processing, Audio, Image and Video Analysis, Robotic Process Automation and Decision Management has an impact in the insurance field.
Let us see each of them in detail.
The gift of being human is the power to learn from experiences. Similarly, machine learning algorithms apply statistical techniques and enable software to predict outcomes accurately and automatically. These algorithms are self-coded by constantly learning from previous ‘experiences’. In other words - automating the automation. Machine learning effectively forms the backbone of AI implementations.
Insurance companies source data from multiple sources ranging from traditional external systems like DMV, MIB, etc., to data from latest technology trends like digital (social media integration), telematics, IoT, drone data, etc. Machine learning algorithms play a key role in all business areas from product design, sales to services and settling claims. Machine learning in the insurance industry is also useful in fraud detection, risk evaluation and identifying cross-selling opportunities.
Text Analytics and Natural Language Processing (NLP)
Customer service and customer engagement have been the key business trends in all domains, and insurance companies are no different. Digital technologies have offered a variety of options for customer engagement (gamification) and customer service (customer portals and mobile apps for self-service). Text analytics and Natural Language Processing (NLP) have redefined self-service capabilities and are taking customer engagement to the next level.
Customer apps are now equipped with AI-powered chatbots that can hold meaningful conversations with consumers to understand their needs and address them. NLP has allowed insurance companies to offer a robotic conversational agent to be at the service of customers any time of the day. These robotic agents are capable enough to identify the customer need, generate meaningful responses and perform the required actions.
One shortcoming of the robotic agents was that they were not able to recognize the tone and emotions. Deep learning technique overcomes this shortcoming as well, making the conversational agents very similar to humans. IBM’s Watson APIs is an example of a platform that provides a complete package required for a true conversational agent.
Audio, Image and Video Analysis
Insurers can target faster and accurate claims processing to enhance customer satisfaction. For instance, in auto insurance, customers can initiate claims through a mobile app, click pictures of the accident and submit the claims instantly. Algorithms that have been trained through pictures from past claims can accurately estimate the extent of damage and automate the claims evaluation process.
Image and video analysis could also form a key application in commercial property insurance inspection. Agents generally upload pictures or videos of the properties as a part of their risk inspection report. Image and video analysis could analyze these supporting evidence and automatically evaluate the risk. This enables insurers to provide accurate rates to cover the risk.
Audio analysis or voice recognition has become a part of our daily lives through Siri, Alexa, and Google. Identifying the tone and emotions is also part of the audio analysis. In the insurance industry, audio analysis has found its use in customer service and fraud detection.
Conversation agents in customer service are given a new dimension through audio and video analysis, making the experience realistic and personal.
Robotic Process Automation (RPA)
Combining business processes with AI concepts is a new weapon in the technology arsenal. RPA provides an end-to-end automated solution for business problems. Insurance companies too are finding RPA to be a powerful tool and are reaping benefits. Automated underwriting and customer service are a great case study for RPA. For instance, customer requests for an address change through chatbots. Chatbots invoke robots that update the new address in the policy record.
RPA also finds great application in IT support activities in any organization.
AI powers decision management system that uses analytics to provide recommendations based on identified patterns. One of the many insurance case studies is the “Next Best Action” for a producer or an organization. Based on customer data, analytics can analyze the pattern and assign a probability of customer churning. AI analyzes past patterns and actions taken in the past to suggest the best action to be taken to retain a customer.
The ai-powered decision management system could be the digital sales advisor of the future. Based on conversations and data collected through bots, the next-gen digital advisor can suggest products to the buyer. Digital advisors can also suggest the next best product to existing customers to increase cross-sell opportunities.
Challenges of AI Adoption
- An advantage like reduced auto insurance premium could result in decreased written premiums for the insurer and it could challenge their profitability.
- Since the technology is still maturing and not many platforms are available, the cost of implementation is high. Insurers need to do an enterprise level evaluation to determine the application of AI across the company to balance the high cost.
- Newer technologies demand the availability of skilled professionals. Companies are reskilling their staff to cater to the growing demand for skilled personnel.
Read our report to know how Mindtree is putting a humane touch to AI.
USAA has invested in a cognitive technology company to modernize the customer service, and Allstate has launched ABIe, an agent servicing AI application. These are some of the early adopters of AI. Gradually, many other insurance companies are investing in AI and are betting big on it.
While challenges remain, insurers are seeing a huge potential in AI, extending far beyond customer service and engagement. Insurers, therefore, need to carefully evaluate and devise an enterprise-level implementation strategy to reap AI’s full benefits.
Future of AI in Customer Engagement
A Gartner report predicts that by 2020, 85% of customer interactions will be managed without a human and that by 2018, customer digital assistants will recognize the customer by face and voice. With the increasing smartphone user base and growing digital trends, AI is poised for greater growth. Intelligent bots are replacing humans, resulting in FTE savings for insurance companies, especially in the sales and service business areas. Insurance companies are now able to appropriately price the risks, reduce fraud and increase customer satisfaction through accurate predictions and faster processing. These benefits are passed on to the customers as reduced premiums. For example, autonomous cars reduce auto insurance premiums of the customer.
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