According to the behavioral scientist Peter Hovard in his study for RGA UK, strategies that include predictive analytics allow insurers (and banks) to focus on reaching new potential customers. No more competing for crumbs, it’s time to reduce the cost of acquisition using artificial intelligence, machine learning and natural language processing.
New technologies are at your service. Here we tell you how to use them:
Finance and innovation. Insurers and banks on the crest of the wave
The tools we have mentioned (Artificial Intelligence, Machine Learning and Natural Language Program) allow companies to analyze large amounts of historical data in real time to identify patterns, predict risks/opportunities and improve decision making. New audience research techniques explore the beliefs of participants and are invaluable for better understanding human attitudes and purchasing behavior for financial products.
In fact, the return on investment has surprised skeptics. With the advent of generative AI, the value of technology is more tangible than ever. Company leaders are successfully integrating it into their operations and reaping great benefits, leading us into an accelerated adoption phase. And all this in corporate and highly regulated environments, where providing accurate financial information and ensuring regulatory compliance are critical.
Recently, the consulting firm Gartner predicted that by 2028, 50% of organizations will have replaced their current data analytics approaches with ones based on AI. No doubt many already know that so-called autonomous finance can improve business performance by minimizing the burden of data analysis.
“But how can innovation departments take advantage of this moment and boost the potential of artificial intelligence?“
A moment of joy: now you can invest in AI with all the guarantees
Taking these three issues into account, you can ensure the success of your entity:
Find the best interlocutor.
There are specialized technology providers in the world of finance that will offer you the developments that best suit your needs. Since flexibility is one of its premises, they guarantee that future adaptations and changes are covered.
Leverage industry partnerships and collaborations.
To stay up to date on the latest trends and developments in this field. You will gain valuable insights and perspectives on its evolution and different use cases. You will also discover emerging risks and opportunities.
Assess the feasibility of AI adoption.
In the context of existing infrastructure and operations within the organization. This implies identifying possible barriers and brakes. Sometimes the easiest option may be to incorporate white label modules that are installed through APIs.
Reviewing manual operations, in most cases repetitive and expensive, is crucial to start looking for solutions. You need to evaluate the benefits of new technologies (much more efficient and precise), assess the savings in staff and facilities, and calculate the ROI of the new tools. You will be able to swim with the big fishes of the industry!
Would you like to calculate the ROI you would get by applying an artificial intelligence module in your bank or insurance company? Send us a message!
Know the customer better to increase the turnover
Predictive analytics helps FinTechs better understand users and hyper-customize their products to meet their individual needs. By analyzing demographic data, transactions, online behavior and other sources of information, startups can segment consumers, anticipate their preferences and design more effective sales strategies.
This translates into greater customer retention, reduced acquisition costs and, ultimately, increased revenue for the companies we offer our services to. We all know very well how competitive the financial market is and the urgent need to differentiate yourself. The advantage of FinTechs is that we continually find new ways to differentiate the entities we work for.
Predictive analytics help identify unique opportunities and deliver great value propositions. By using internal and external data, we are able to gain insights with which to develop products and services that outperform the competition. From Coinscrap Finance we predict an acceleration in the adoption of these technologies throughout this year.
The use cases are diversified. We analyze the study of Peter Hovard
In other sectors, customer service chatbots, legal documentation review, energy and sustainability management systems or the diagnosis and triage of patients with Artificial Intelligece is causing a complete revolution. This new wave is really exciting in terms of its endless possibilities.
Now let’s take a closer look at Peter Hovard’s study
2,895 participants were recruited and attention was paid to various relevant events in their lives:
- If the participant had offspring and their age.
- If the participant had a property and when he bought it.
- Whether the participant had experienced bereavement in the last 12 months or knew a friend or relative who had.
The experiment presents what-if scenarios that contain the types of information that customers might be looking at online and that have the potential to create a predictive analytics. These situations vary in their potential to raise awareness of the need to purchase life insurance.
The participants were divided into six groups. First, each group was asked to remember or imagine online activities related to the three mentioned aspects. Then, they had to choose between three fictitious online products related to them. It was analyzed how the simulations affect the receptiveness of the participants to life insurance.
To do this, they used neuroscience techniques, in collaboration with the research agency Walnut Unlimited. They showed that the stronger an attitude is, the more accessible it is to memory and thus the more likely it is to guide behavior. It is clear that users still need to be persuaded to buy life insurance. But thanks to technology, we will know which people feel the strong need to buy it.
A well-known behavioral challenge is that people are influenced more by potential short-term gains and losses than by long-term gains and losses. It is one of the foundations of behavioral finance. If you want to know more, do not miss our next post.