Sofie Quidenus-Wahlforss, CEO and founder of omni:us and a member of Forbes' Top 50 Women in Tech 2018 list, discusses the role AI in insurance in 2019
AI in insurance might not be something many consumers are aware about but it’s set to shake-up the sector in 2019.
Traditionally, insurance has had a reputation for being traditional, risk-averse and perhaps slower than other sectors to react to and adopt technical innovation and change
Although many opinion leaders are citing the fundamental nature of these advancements, it’s crucial to understand in what ways exactly they can be implemented and what the potential benefits are.
Sofie Quidenus-Wahlforss, CEO and founder of insurance AI firm omni:us and a member of Forbes’ Top 50 Women in Tech 2018 list, highlights five ways AI in insurance will transform the sector.
AI in insurance can help improve productivity and efficiency
Every day, insurance companies deal with vast amounts of highly variable documents, which they need to process with speed and accuracy.
AI and machine learning can help to revolutionise these workflows by reading, understanding and extracting relevant information and therefore automating tasks such as claims management - completing in minutes what may have previously taken hours or even days or even weeks to do.
As part of this, the AI also gets more intelligent and learns based on the amount of information and data it’s exposed to, so the system keeps improving.
It can help companies become more data driven and customer-focused
As well helping insurance companies deal with the vast amounts of documents they have to process, it is crucially the wealth of data that is extracted from these documents that will prove to be most fundamental to innovation long-term.
At omni:us, we predict that the insurance industry will move from process driven to data driven over the next few years, primarily because data holds the key to improved customer understanding.
By using AI to analyse customer data, insurers will be able to develop better products, make decisions and improve services where it matters most.
AI in insurance can decrease the risk of fraud
Claims settlement managed by employees alone comes with the risk of human error - it can be easy to overlook possibly fraudulent information and lose a payout that could otherwise be avoided.
AI can detect, to varying degrees, such misrepresentation of information by carrying out tests to validate a claim.
This process might involve cross-referencing public information against the metadata supplied by claims documents, a job that may prove intensive and painstaking to a human, but not for AI-powered technology trained specifically in such a task.
Automating tasks will deploy workforces to higher value work
As well as the positivity and interest surrounding AI, there are also concerns that it will mean business will have to make staff redundant, as tasks become fully automated.
Inevitably, the introduction of radical technology and innovation has an impact on business culture, teams and the way in which work is distributed and tasks are performed.
However, to look at the bright side, AI is in general predicted to make people’s lives easier rather than harder and will empower workforces by overcoming traditional limitations of manual workloads, ultimately helping to redeploy staff to higher-value work.
AI encourage industry-wide innovation, investment and competition
Today’s customers expect more from their insurance company - they want products tailored to suit their needs and don’t want to wait weeks or even months for claims to be resolved.
AI will make it possible for insurance companies to meet these changing expectations, in turn encouraging widespread interest, innovation and investment in the industry.
With regards to both current and future markets, adopting a data-driven approach is the fundamental means for insurance companies, whatever their size, to stay competitive and there’s little doubt that effective implementation of AI technology will be crucial to business success and survival into 2019 and beyond.