#6 Pricing and Risk Management
Price optimization deploys data analysis techniques to understand customers' reactions to different pricing strategies for products & services and find the best prices for a given company, considering its goals. Insurance companies mostly use GLMs (Generalised Linear Models) for price optimization for sectors like car and life assurance. This technique allows insurance companies to better understand their customers and balance capacity with demand and drive better conversion rates.
Similarly, Risk assessment automation enhances operating efficiency. By combining RPA with machine learning & cognitive technologies to create intelligent operations, risk assessment automation boosts productivity. As the automated process significantly reduces time, insurers can deliver a better customer experience and reduce churn.
#7 Policy Servicing
Automated intake of policy details enables integration with the policy administration system to retrieve details related to each policy. This reduces the manual effort for finding and locating relevant fields required for policy endorsements. It also enables parallel processing to manage complex scenarios where multiple requests get initiated from individual customers, which results in reduced turnaround time for policy processing and servicing. RPA in insurance aids in accomplishing a plethora of operations efficiently without involving vast navigation across systems. It automates transactional and administrative activities such as accounting, settlements, risk capture, credit control, tax, and regulatory compliance.
AI and Machine Learning Use Cases in Insurance
#8 Insurance Distribution
Insurance customers would visit a local carrier or contact a financial planner to explore policy options in the pre-digital world. More often than not, there would be a leading carrier for a specific product in a localized market. Based on the information the customer provided, the carrier would perform underwriting activities and share a quote. Digitalized insurance distribution systems upended this picture.
Today, nearly every carrier has an online portal that allows customers to peruse their product and service catalog before deciding. This shift in consumer behavior prompted significant disruption in the insurance sector. The potential of AI goes beyond underwriting or claims approval; it could transform the sales and distribution phase of the insurance value chain, gaining from sophisticated AI algorithms available in the market today. Digital technologies such as optical character recognition (OCR), machine learning (ML), and natural language processing (NLP) can help insurers gain from a customer's digital behavior.
This payoff points to a massive opportunity – with so many prospects researching digital channels, there is a vast repository of customer data that the AI engine can leverage, empowering the distributors to make smarter decisions.
#9 Product Recommendation
The insurance companies generate a lot of transaction data each day. In such a scenario, automation can assist companies in recommending insurance products for customers accurately and efficiently, eventually improving the competitiveness of the insurance company. Connected devices and wearables offer deep insights into the customer's physical condition, like blood pressure, temperature, pulse.
Nowadays, the insurer has the opportunity to explore the client's lifestyle patterns and preferences. Social media data from Facebook, Twitter, or other networks also is a great aid. By capturing and analyzing a customer's requirements, the insurance companies are better equipped to offer them a customized offering, which saves time and money and, most importantly, enhances customer confidence.
#10 Property Damage Analysis
Inspection is the first step in a damage insurance claims process, be it any asset - a mobile phone, automotive, or property. Assessing the damages to calculate repair costs is a daunting task for insurance providers with manual intervention. AI-powered object detection analyzes data, compares the level of damage before and after the event. Machine learning models are equipped to recognize damaged vehicle parts and help estimate the cost of repairs.
A PwC report
highlighted the application of drone and AI technology to the insurance industry can result in savings of up to US$6.8 billion per year. Using the power of drones combined with automated object detection, the amount of time-solving a claim can be reduced between 25% and 50%.
#11 Automated Inspections
For a long, motor insurance claim estimation has been managed manually by claim adjusters and surveyors. Manual inspection requires the adjuster/surveyor to travel and interact with the policyholder, approximately costing $50 to $200 per inspection, making it a costly proposition. Claims settlement would also be slower as it takes about 1 to 7 days for report creation and estimation.
Through AI-based image processing, insurance companies can analyze the damage incurred to the vehicle. The system then generates an in-depth assessment report outlining the repairable and replaceable vehicle parts and their estimated costs. Insurers can cut down claim estimations costs and make the process highly efficient. Moreover, it also populates robust data to arrive at the final settlement amount.
#12 Customer Lifetime Value Prediction
Customer lifetime value (LTV) is one of the most critical tools that enable companies to trust customers and predict customer lifetime value through machine learning. A study conducted by Bain & Co
. found that a 5% increase in retention rate can lead to a rise in a company's profit between 25%–95%. Machine Learning algorithms use a customer's purchase history and match it to a large inventory of products to discover hidden patterns and similar group products together.
These products are then made accessible to customers, which eventually encourages the purchase of the product. Understanding the customer's lifetime value enables insurers to find the perfect balance between customer retention and acquisition.
#13 Speech Analytics
In early times, insurance companies possessed a limited amount of data to assess a customer's profile. Additionally, Call Center executives used to have limited ability with a small amount of manually audited and transcribed phone calls. In such a scenario, the advent of the Speech Recognition tool makes perfect business sense for companies.
Speech recognition is a powerful tool to analyze customer speech based on lead calls to improve personalization. It can identify customer pain points with products through speech analytics of feedback to improve future products and detect fraud based on voice analysis of customer calls to improve security measures.
Speech analytics software often combines the power of Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and Artificial intelligence (AI) technology. With the augment of such technology, companies are utilizing speech analytics tools to understand every aspect of their customer interactions and their contact center's performance and are applying those learnings in optimizing costs and generating higher revenue.
#14 Customer Segmentation
Customer segmentation is the first step towards enhancing personalization. It optimizes budgeting, product design, promotion, marketing, and customer satisfaction. Machine learning tools analyze customer data and find insights and patterns. AI-assisted tools precisely identify customer segments, a complex exercise to perform manually or with conventional analytical methods.
#15 Workstream Balancing for Agents
A study called AI at Work
was conducted by Oracle and Future Workplace, which stated that 50% of workers are already using some form of AI at work compared to only 32% in 2018. Today more and more insurance agents are finding it convenient to use AI-assisted models that help them gain access to customers and empower them to expand their markets. With simplicity as the defining factor, AI is sure to be the mainstay for building and enhancing customer satisfaction and, in turn, growing insurance agents' expanse.
#16 Self-Servicing For Policy Management
Self-service business intelligence (BI) is a data analytics tool that helps users who do not have a background in BI, data mining, or statistical analysis to access, analyze and explore data sets. Self-service BI tools filter, sort, analyze and visualize data without involving an organization's BI and IT teams. Such tools make it easier for employees to get valuable business insights from the data collected in BI systems. This approach ultimately drives more informed decision-making, resulting in higher profits, improved efficiency, and enhanced customer satisfaction.
#17 Claim Volume Forecasting
The essential gamut of an insurance practice is to set the premium at the beginning of the insurance contract. To arrive at a precise premium for next year in an insurance company, a precise and reliable estimate of the number of claims occurrences and the total claim amounts is extremely important. Machine learning significantly improves the speed and accuracy of the forecast for individual claims. This has a positive impact on the efficiency of an insurer's pricing.
For instance, claims that are more likely to be large and with more uncertainty in outcome can be given more attention, and claims that are more likely to be smaller and more certain in outcome can be settled faster. While improving business performance, such tools also enhance customers' experience.