How AI and Cognitive Computing can transform business decision making

2nd August 2017Blog

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Understanding the potential of AI and Cognitive Computing

While AI and cognitive computing are on the path to becoming mainstream technologies, they are not yet widely used in the Financial Services and Insurance (FSI) industry. Many FSI organisations are keen to understand the potential, but wary of acting as guinea pigs for untested solutions.

This post follows on from the second in our Cognitive Solutions series, and goes deeper to explore the problems, potential solutions and their benefits from the five key use-cases we previously highlighted.

1: Improving customer engagement

Building on the personalisation of policies, new technologies can increase the automation of customer sales and service interactions while simultaneously increasing customer engagement. Using advanced analytics to build a deep understanding of customers can transform relationships by accelerating interactions and enabling fully personalised services.

For example, customers and prospects can use natural language to get the information they need from an automated assistant that knows their full history of interactions with the company. And if those customers and prospects prefer to interact with a human, the same information and analysis can be made available to call centres, helping customer service representatives offer the best advice at high speed.

By helping insurers understand what their customers are looking for each time they make contact, advanced analytics and cognitive solutions strengthen the impression that insurers really know and care about their customers’ issues, enhancing loyalty.

2: Boosting competitiveness through automation

Processing and managing large – and growing – volumes of data is a significant contributor to high operational costs for insurance firms. Intelligent process-automation solutions can take the strain off human resources, applying adaptable business rules to process information automatically wherever possible, and involving human decision-makers for any exceptions. Improving the speed and efficiency of routine information processing in this way also frees up skilled human resources to focus on product, service and process innovation as key drivers of competitiveness.

Recognising that machines excel at routine tasks and that algorithms learn over time, insurers should focus their first steps towards automation on those business processes or assessments that are most widely understood. Once the capability is established, and as cognitive technologies improve over time, insurers can move to address more complex and higher-value processes.

3: Personalisation of insurance policies

Customers are keen to tailor insurance policies to their precise needs, which presents a new challenge to established FSI organisations. A key difficulty for organisations accustomed to selling a standard portfolio of products to large numbers of customers is to understand precisely what each individual wants. Naturally, this problem of scale is more easily addressed with technology than with human resources. Advanced analytics solutions enable insurers to gather data for analysis from multiple sources, helping them to build up a more detailed picture of their customers that can be used to model and anticipate their likely future requirements.

Cognitive technologies in the form of automated agents can empower customers to self-select the right policy options for their needs by typing or even speaking the answers to conversational questions. In this way, even complex, highly tailored policies can be sold to a mass market of consumers without requiring FSI organisations to employ armies of salespeople. In addition to making the process smoother and faster for customers, these new technologies boost efficiency and cut costs for organisations.

4: Fraud monitoring

Fraud contributes significantly to insurance losses, with estimates ranging from 5 to 18 percent of claims costs. But traditional efforts to detect and prevent fraud are an even greater drain on insurance businesses, with inefficient claim processing and associated human error representing a large part of the total operating costs.

Machine learning techniques empower small audit teams to extend their reach by embedding their knowledge in a predictive model that can be applied much earlier in the claims process. This enables insurers to make the breakthrough from reactive to proactive claims leakage management – potentially delivering enormous savings. Automation also speeds the processing of non-fraudulent claims, cutting operational costs, and improving customer service for greater competitive differentiation and better customer retention.

5: Improving compliance

As data volumes grow, and as regulations become stricter and more complex, the cost and difficulty of proving compliance are rising. With new regulations such as GDPR on the near horizon, existing manual approaches to compliance are unlikely to scale to future demands. The good news is that advanced analytics and machine learning algorithms are already available to help insurers review, analyse and assess compliance-related information, whether structured or unstructured.

By understanding the context around data, cognitive solutions can automatically classify data and highlight potential problem areas for deeper analysis by humans. The ability to analyse unstructured information opens up a number of interesting new possibilities for FSI organisations. For example, monitoring and understanding interactions between customers and sales agents can help improve controls over mis-selling of insurance products.

Northdoor can help FSI organisations understand the immediate benefits of advanced analytics and automation solutions, and the future potential of AI and cognitive computing. With more than 30 years of experience in the London Market, Northdoor offer proven, practical approaches to deploying analytics and cognitive solutions that deliver rapid return on investment.

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