AI is a victim of its own hype in Insurance
The P&C insurance industry continues its march towards digital transformation in order to meet rising customer expectations, requiring incumbent insurers to vet myriad existing and emerging technologies to find ones that meet organisational needs. The sheer overwhelming choices make it a challenge to decide where to invest limited IT budget and resources. Artificial intelligence (AI) is a prime example of this challenge, a technology that is both fundamental to the future of the industry and difficult to execute properly at scale.
According to , banks and insurance companies are expecting to increase AI investments 86% by 2025. However, implementation steps will be the difference between success and a wasted investment. Many startups and companies proudly tout solutions as AI and machine learning within insurance, when really, they are often dabbling in analytics which involves much more linear data processing. This distinction is important because, according to , 84% of business executives believe they won’t achieve their business strategy without scaling AI. Below are the considerations insurers must address in order to operationalize true AI that can match the lofty expectations of the technology.
Level of AI maturity in insurance
Understanding true AI versus what certain companies believe to be AI is a crucial first step. Presently, most of the P&C industry today is investing in piecemeal predictive analytics projects, which can include machine learning and AI components, but are still siloed use cases often supporting a single business function (e.g. underwriting). Predictive modeling also has a more linear data pathway than true AI models. Current machine learning models are often trained in batches every few months or years, whereas true AI operationalization (where the model is ever-evolving) involves supporting a constant feedback loop comprising data ingestion from disparate systems and formats, training the model in real-time, evaluating decisions and then prediction/inference.
This process involves orders of magnitude more data and cloud resources than the most analytics projects. Using AI for underwriting is one thing, but ingesting drone imagery of every major freeway in California to help determine driving behaviors is another. These complex and data-intensive AI applications can only be achieved by the largest companies with the most capital and resources. If insurers try to use the same platforms and methodologies for today’s analytics projects to achieve larger AI projects, the infrastructure will eventually burst at the seams. To reach the next level of AI success, they must address common roadblocks that usually surface at the start of a project.
Overcoming roadblocks to AI
The common roadblocks to operationalising AI are data issues, a dearth of specialised talent, and operational expenditure outweighing the projected long-term ROI. While these may appear to be disparate challenges, they are all connected to the use of improper infrastructure. Most platforms today used for analytics, such as Apache Kafka, are not built to support the ever-increasing amounts of data needed for multiple AI models or advanced use cases. Consider that the current AI ecosystem requires many vendors including cloud providers, event streaming, pub/sub, message queuing. This creates many additional points of failure and requires more highly specialized, expensive and scarce MLops talent to overcompensate. Instead, insurers must use platforms that unify distributed messaging for pub/sub, event streaming and message queuing, such as Apache Pulsar, which is more conducive to scaling AI workloads.
Streamlining workloads also means less of a need for scarce data scientists, MLops talent, and developers to sustain a project. The other aspect of operationalizing true AI is that there is a point where humans simply can’t be the one to manage model training. Leveraging neural networks (similar to a computerized brain) will be crucial to handling the logistics, orchestrating and ensuring data is captured and distributed appropriately to-and-from the model.
The correct way to approach AI initiatives is not getting caught up in “How I can get this done now?”, but rather, “How can I sustain AI five years in the future?”. AI projects cannot be easily lifted and shifted to new platforms once they’ve begun, so making sure they can routinely capture new and existing data from the variety of insurance systems and process them in a scalable manner is the difference between failure and success. Staying ahead of burgeoning data growth is the key to realising AI initiatives.
SLK Software: Optimising performance in the digital economy
Established in 2000 in Bengaluru, India, SLK Software recognises that fast-paced digital transformation is creating an unprecedentedly fertile period of opportunity for global businesses.
As such, with a firm belief in the power of simplification and automation to yield new and exciting experiences, the company has been challenging the status quo for over 20 years through an approach that is:
- Relationship oriented
- Strategically focused on a desired outcome
- Reliant on automation tech
Believing in purposeful automation
SLK’s specialisation in automation tech is full spectrum: artificial intelligence (AI) and machine learning (ML), Computer Vision, Natural Language Processing (NLP), Robotic Process Automation (RPA), and more, are all part of its core competencies.
Citing 90% productivity improvements, 30% business growth through better customer experiences, and up to 20x faster go-to-market capabilities, the reasons for its focus are clear.
The company currently serves the banking, financial services, insurance, retirement services, M&A, manufacturing, and supply chain sectors. Solutions offered include:
- Intelligent Business Transformation
- Agile IT Automation
Accelerating workflow processes
The latter is a tool specifically calibrated to enable business users an easy method for capturing document processes. This can occur across any application, with these individual tasks then seamlessly combined for both improved compliance and governance.
Carol Castelloni, VP of Transformation at CNA Insurance, highlighted this as providing critical support in helping the company meet its business objectives:
“SLK’s Avo Discover tool accelerates how we can document workflow processes, measure impacts on enhancements, and identifies future automation opportunities.” Liberated from having to focus on these process-driven aspects of business, CNA Insurance has been able to refocus its attention on creative problem-solving instead.
Ultimately, this is the most important benefit that SLK brings: it optimises the back end so that clients can channel their energy towards what matters the most, customers.
Read more about SLK Software and CNA Insurance in the June 2021 edition of FinTech Magazine.
Pictured: SLK Software team (source)