May 19, 2022
Thanks to recent technological advances, the insurance industry has been innovating itself, becoming more modern and efficient, offering simple, accessible and reliable products to customers. At the same time, customers have also evolved; their easy access to technology and social networks has made them savvy and internalised consumers, with greater options and freedom when selecting their insurance.
It is important to note that these new generations make up a big part of the new clientele. According to the World Economic Forum, Millennials now make up 23% of the worldwide population. These contemporary customers have a certain sensitivity to insurance companies' prices and types of service. They look for simplicity, immediacy, and an emotional connection that determines their decision. For this reason, modern insurers are seeking to innovate, customising their services to provide the user with a valuable, differential and reliable product tailored to their person, thus creating a positive association.
Faced with the need to personalise and connect emotionally, technology and alternative data began to be used. Alternative data is a source of information from social networks, sensors, transactions, emails, and others, whose objective is to collect more optimised and precise data. Thus, it is possible to internalise consumer information, obtaining behaviour data, habits and interests, which are integral for insurance companies to show greater transparency with the client, provide a more personalised experience, and improve the number of policy subscriptions. Thanks to a more accurate risk assessment.
At the same time, needless to say, insurance companies have to keep an eye on metrics that determine profitability levels and growth. Cost of customer acquisition, persistency rate, fraud occurrence and policy lapsation have traditionally been some of the main pain points for insurers. The difference being that nowadays, technology allows for these metrics to be resolved in a more efficient way with insightful access to customer data. Below are some examples of what alternative data has to offer:
With the entry of more insurance players, the insurance company ecosystem has become saturated, with more competitors driving higher acquisition costs, especially the cost of advertising and lead generation. The industry was behind in using digital technologies that meet the expectations of today's insurance buyers, and companies have understood that a more personalised approach is needed. Adopting a data-driven strategy for new customer acquisition is indispensable.
The good news is that the power of data enables new digital marketing strategies with tools and programs that attract new policyholders without breaking the bank. Insurers must successfully integrate these strategies to avoid competing in a highly saturated market. A study by LexisNexis Risk Solutions revealed that 43% of respondents think their current data sources fall short in helping them identify prospects and programs that represent the greatest profit opportunities. Insurance companies must have marketing powered by granular consumer insights.
Credolab helps lower the average acquisition cost by increasing the acceptance rate of applicants. Insurers today tend to turn away applicants (especially in emerging markets) because they lack the data to make an informed decisions. By using alternative data, credolab improves the allocation of leads by calculating their probability of accepting an insurance offer. If that isn't enough, credolab also provides a score for 100% of incoming requests, helping to reduce data asymmetry in making a decision and increasing approval rates.
By segmenting correctly, personalising the message, and directing leads to the insurance offered that has the highest chances of being accepted by the customer - the conversion rate increases. The power of data allows insurers to know their customers in-depth and thus optimise the customer acquisition budget.
The persistency ratio is the number of total policies that an insurer has to the policies that are renewed or in force. The persistency ratio shows the number of policyholders paying premiums for active plans. About 20% of all new customers do not pay the first time they are due, especially if those are not linked to a credit card payment. In such a case, incurred underwriting costs paid, brokerage fees, and other acquisition costs make the policy unprofitable unless it is paid for.
At credolab, we can predict the probability that a new applicant will not make the first premium payment. By leveraging alternative data sources, insurers can better understand their clients and make better decisions concerning pricing, coverage terms, conditions, and, most importantly, their risk levels.
The persistency ratio is an indicator of the insurer’s overall customer satisfaction. Thanks to artificial intelligence, machine learning, and predictive analytics offered by credolab, it is possible to understand customers better and offer an improved overall experience: services respond effectively, claims are processed instantly, and policy writing is done faster.
Healthier fraud controls are a key factor in ensuring insurance companies’ profitability and ROI. Fraud can occur from the seller or the buyer: the seller can sell policies from non-existent companies to generate a higher commission, and the buyer can make false claims, such as falsifying accidents or theft to collect insurance. For this reason, insurers generally pass this cost on to their clients with higher premiums.
Insurance companies are among the industries with the highest cases of digital fraud. According to a TransUnion report, across all industries, insurance saw the fifth-highest numbers for digital fraud attempts from 2020 to 2021, only being surpassed by the gaming, travel and leisure, telecommunications and financial services industries.
At credolab, we help mitigate this problem by detecting fraud at the origination stage using behavioural insights from alternative data. These insights help prevent malicious subscriptions thanks to their ability to know the type of device (its brand, operating system and model) and detect anomalous behaviour (such as the use of autofill forms or the presence of bots, VPNs, TOR). It is also possible to dig up how often applicants change their key information like address or income, discover if the phone is locked or open to a global network and predict the lifespan of the mobile device and check how active its owner is, based on the volume of media created and/or contacts saved over time. All of the aforementioned help safeguard a healthy onboarding process, securing effective user verification while detecting possible fraudulent behaviours.
A lapse ratio measures the percentage of an insurance company’s policies that customers have not renewed. This ratio reveals how efficient a company can retain its customers and earnings, making it a closely monitored indicator for insurers. For example, between months 6 and 8, about 15% of all customers miss a payment and become delinquent. As a result, while insurers may stop the premium coverage, they typically lose money on a policy that hasn't reached break-even.
Credolab helps reduce the number of delinquent customers approved at the origination/application stage. By detecting which are most likely to stop paying before months 10 and 11, the critical months to prevent losses, credolab helps insurance companies keep a healthy policy lapsation ratio. As stated before, the method is very similar to lending companies currently using to distinguish fraudsters from good consumers based on device recognition, context, and reputation. This is possible by using smartphone device ID and metadata to arrive at a fraud score for each applicant in a non-intrusive and secure manner and in real time.
Credolab uses alternative data from smartphones and the web to analyse user behaviour, assess risk, help insurance companies in the underwriting process, and offer opportunities to potential customers who were not on the radar. Thanks to artificial intelligence, machine learning, and predictive analytics, insurers can reach previously excluded markets due to a lack of credit history. As a result, they can offer a better experience to their clients. In addition, through alternative data, it will be possible to impact the right person, with the right message, without requiring much investment and a higher conversion rate.
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