Articles - Is AI overhyped or an essential business tool


AI is a popular buzzword for many different industries at the moment – and the insurance sector is no exception. 85% of insurers are hoping to invest over $5M in AI by the end of 2020 according to Deloitte’s Digital Disruption Index, showing the huge business value being attributed to this technology. But insurance has a long way to go when it comes to achieving these goals.

 By Lara Korz, Chief Data Officer, Azur
  
 Even though many insurers and brokers recognise the negative impact that traditional methods of data management can have on the customer experience, many firms are tied to legacy outdated systems. The first step is getting all customer data onto a single modern, core system.
  
 For today’s firms, this is an important area to get right. While other sectors make huge advancements when it comes to digital transformation, insurance firms are becoming increasingly vulnerable to more advanced competitors who are now able to collect and enrich their data before applying data science to unlock its true power.
  
 All about data
 The biggest issue most insurers face when it comes to organising their data is duplication across numerous disparate systems.
  
 Firms often store their data across several different databases, including multiple sets of data on individual customers. And whilst it’s true that different lines of insurance have different data requirements, there is no need for repetition of the most basic information across the company.
  
 For example, even if a client already has home and contents insurance with a particular insurer, many firms still need to re-key their data into an entirely new database for a car insurance quote. Unsurprisingly, inefficiencies like these have a negative impact on user experience, since customers start to feel undervalued and frustrated when they’re asked to repeat the same information time and time again. 
  
 But this isn’t the only problem. Each time the data is manually rekeyed, there’s also a possibility for mistakes to be made and the information to be altered slightly. This causes a huge problem; a recent study by Gartner found that up to 80% of a data scientist’s workload is spent cleansing data, normally because of the flawed or inefficient systems being used to manage it.
  
 Companies that decide to invest in their digital infrastructure will therefore have a clear advantage over their competitors. If more companies embrace technology to address this issue, data scientists will be able to focus on maximising their output and putting their talents to better use - working on areas which they were previously forced to overlook.
  
 Ahead of the pack
 By eliminating manual, error-prone data management, firms will also have huge opportunities for product development and innovation. Firms that store their data on a modern core system will not only be able to analyse and enrich this information, but can then use data science ranging from predictive analytics and machine learning AI technology to maximise its value. Not only do these technologies provide a better and more personalised customer experience by making straight through processing possible, but they also enable brokers and insurers to make smarter and more efficient underwriting decisions, manage and price risk more accurately, and detect fraud far more effectively.
  
 The Association of British Insurers estimated that there were more than half a million insurance frauds committed in the UK last year, with dishonest insurance claims amounting to £1.3 Billion. AI-driven fraud detection solutions can tackle this problem by analysing massive amounts of data from multiple sources in order to spot fraudulent claims.
  
 At the same time, these systems can also help insurers avoid falsely flagging legitimate claims as fraudulent. This is very important too, as making sure that only genuine fraudulent claims are investigated will not only minimise customer irritation, but also save on workload and costs across the industry, in turn reducing customer premiums.
  
 The role of the broker
 AI will never replace the value that brokers can provide, but firms will have to adapt to their new circumstances. Customers will always appreciate the advice of a trusted broker, but to maintain this relationship and their place in the value chain, brokers will need to harness the benefits that AI can provide.
  
 For example, with AI-based solutions, quotes that would have previously taken a couple of days can now be provided in a matter of minutes. When clients ask for a quote, AI technology can reduce the application to just five basic questions, including things like name and address, before enriching the data at the back end. This way, the broker and the customer enjoy a much smoother, slicker process without compromising on the data that firms needs to accurately price the risk.
  
 Ultimately, AI will only ever be as good as the data that is inputted. As such, to maximise the potential of this technology and improve its algorithms, all data has to be clean, reliable and easily accessible across all departments. Many insurers have historically struggled with organising their data effectively, but cleansing it should be a key priority, especially as the application of predictive analytics, machine learning and AI begins to play a key role in client retention and attracting new business.
  

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