A Timetric report available at the Insurance Intelligence Center (IIC) reveals how insurers can use Big Data in areas including product development, underwriting and fraud detection to gain a competitive edge. At a time when Big Data is developing across financial services, insurers also need to be aware of the increasing regulatory focus whether big data fosters or constrains competition and its impact on consumers.
Data management software provider, SAS, defines Big Data as a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis.
But it’s not the amount of data that’s important. It’s what organizations do with the data that matters.
The Timetric insight report, Digital Innovation in Insurance, explains that Big Data allows access to high volumes of previously unavailable information, particularly unstructured and external data such as online forums, sensor data, blogs and social media.
Big Data technology can be used to design and accommo¬date various data platforms and data models, and provide insightful, actionable analysis on a diverse range of data sets. Data sets other than traditional data, such as demographics, can add to an insurer’s ability to assess underlying risk more effectively.
As well as enhancing core business functions, Big Data can therefore be used to achieve operational efficiency, and devise strategies to improve customer acquisition and retention through effective communication and cross-selling.
Big Data’s importance is such that according to research conducted by the Chartered Institute of Loss Adjusters in association with Ordnance Survey, 82% of respondents believed that insurers which do not capitalize on the benefits of big data might lose competitiveness.
The IIC report notes that South African health insurer Discovery runs a wellness and loyalty programme called Vitality, and uses Big Data analytics to build insights and calculate individual risk based on information col¬lected from customers. Discovery estimates that the instances of lapsed policies and mortality rate reduced by 52% and 34% respec¬tively for active participants in the Vitality programme.
Big data analytics
Big Data analytics is also capable of using high degrees of computing power and sophisticated algorithms to determine patterns from complex streams of unstructured data, according to the Timetric report.
Big Data analytics is enabling insurers to shift towards customer-centricity by analysing preferences, behaviour and attitudes. Score-based models can help insurers design customized products, using demographics, driving and health records, and credit information. The scores can then be employed to determine premiums for each individual customer.
Simultaneously, big data analytics also helps insurers to improve customer acquisition and retention through cross-selling. Data from sources such as customer records and feedback forums can be used to determine appropriate products or services.
Progressive Insurance, for example, uses big data analytics to create products and services based on the insurance needs and risk involved with a particular customer segment.
The Timetric report cites South Africa-based insurer AllLife as using big data to underwrite entirely new risk that could not previously be covered profitably. AllLife offers life and disability insurance at low premiums for manageable diseases such as HIV and diabetes.
Using Big Data analytics, AllLife assesses policyholders’ risk every three to six months, and clients who do not adhere to strict medical protocols will have their benefits or cover reduced. As a result, AllLife aims to insure 300,000 HIV patients by 2016.
Big Data analytics can be crucial in enabling insurers to identify fraudulent activity in the event of a claim.
In addition to conducting trend analysis from historical data in terms of claims made by a policyholder, insurers can also receive live data from GPS-enabled devices, blogs, tweets and social media platforms.
Insurers can therefore move beyond algorithmic fraud-detection models and use more person-centric techniques such as pattern and cohort analysis. By 2016, it is estimated that 25% of global insurers will use big data analytics to detect fraud cases, according to a survey by Deloitte.
FCA review into Big Data
With the use of Big Data developing across financial services, the UK’s Financial Conduct Authority (FCA) launched a call in November 2015 for examples and evidence where possible, of how big data is affecting, and is likely to affect, consumer outcomes and competition in the retail general insurance sector.
According to the FCA, it is seeking views on whether its regulatory framework affects developments in big data or unduly constrains innovation in the interest of consumers. The call for inputs will close on 8 January 2016.
Christopher Woolard, director of strategy and competition at the FCA, said: "Big Data is having an ever-growing social and commercial impact, and has the potential to transform practices and products across financial services. We are starting our work on Big Data by seeking to better understand how insurance firms are using data, and how this may evolve in the future."
Commenting on the FCA’s consultation on Big Data and how this might impact the life insurance sector, LII spoke to James Blake, CEO of financial technology company, Hello Soda.
Hello Soda’ s technology platform, PROFILE, analyses structured data such as the number of Facebook friends someone has, and generates predictions from unstructured data like blog posts, tweets and social interactions.
Using data properly
Speaking to LII, Blake says: "I think it is a difficult challenge for insurance companies, in particular, to try and get the right information to ensure they are dealing with their customers fairly, because that is what they are trying to do.
"The FCA investigation is around practices and rightly so. Data needs to be taken in the right way. "However, I would argue data taken needs to be used in analytics to ensure it is relevant and it is protected.
"There is a huge amount of power in positive data analytics, but it has to be used in the right way."