Deep Learning: 5 Ways How Insurance Companies can adopt it

New technologies often trigger unrealistic expectations in the market. It happened with the introduction of computers and yet again during the early years of the Internet. While adopting a new technology like Deep Learning, an organization may often overestimate its benefits and, at the same time, underestimate the pre-requisites for its success. Insurance companies are going through a similar experience with the adoption of AI. It might take a while for the new framework and technologies to mature before delivering a handsome Return on Investment (ROI). The only way to validate this premise is to put it to test.

Insurance companies have traditionally operated in silos, adopting proprietary software and following data secrecy practices. It will be a culture-shift to adapt to the new normal. However, insurers have to invest in technology today, and reinvent their business to remain relevant in future. Industry leaders must pursue strategic long-term growth over short-term maneuvers. Driven by this imperative, some insurers have opened up their data sets and partnered with startups to explore the benefits of AI.

1. Rapid Experimentation

Understanding the limitations of deep learning provides critical context to design use cases. The best way to understand the capability of a certain technology is to experiment. Many pilot projects make the mistake of spending too much time on setting up the experiment than on running and learning from it. The risk and cost of inaction is higher than that of pursuing a mediocre use case.

Experts recommend implementing multiple pilot-projects at once instead of rolling them out one after the
other. For example, a pilot-project on Customer Discovery can be implemented along with adoption of new Customer Support tools. The two use cases complement and yet do not interfere with each other.

An organization can attempt deep learning either at the task level (classification, recommendation etc.) and/or at functional level (underwriting, claims processing etc.). The actual application of deep learning depends on the end objectives — reduction in operating costs, and increase in revenue and efficiency.

2. Gathering / Generating Training Data Sets

The efficiency of an DL algorithm depends on the quality and size of the training data sets. A continuous stream of transactional data is often not enough to train the machine. The data needs to be indexed and labeled appropriately for the machine to make sense of it. Let’s take the example of credit card transactions. The raw data might not be enough; each transaction needs to be identified as either ‘genuine’ or ‘fraud’ so that the algorithm can identify trends that can distinguish the two types.

Sometimes, these data sets are not linear but relational – for example, to monitor the risk (fraud or compliance), it is important to understand context of the entities; this context would be gathered from third-party sources or from another dataset internally.

3. Strategic Growth Vs Long Time Benefits

There is always a trade-off while having to choose between complex use cases and simple ones. Complex use cases can take longer pilot times but can deliver higher ROI. On the other hand, simple use cases take lesser effort and resources but deliver short-term business outcomes. Hence, the product development roadmap becomes an important consideration while adopting deep learning technologies.

The best approach is to plan the product development with small upgrades such that the ROI/outcomes can be demonstrated at every product upgrade.

4. Creating a Man + Machine Eco-System

It is important to test the outcomes generated by AI algorithms through manual validation. The AI predictions must be compared with actual outcomes to understand the effectiveness of the algorithm. Comparing this outcome with current work flow results will help the AI system with continuous learning.

Alternative options of feedback must be created for the AI system to validate its outcome, particularly for use cases where reasoning is crucial. For example, while determining medical admissibility of claims application, it important to consider the mandatory documentation before processing the claims. A man+machine ecosystem can gather enough relational information to design a complex system to automate such high level tasks in future.

5. Creating Internal Competency and Resources

In any large enterprise, the adoption of deep learning is not limited to few use cases. Although insurers may embark the ML/AI journey with an external partner/vendor, it is important to create in-house resources and experts to extend the learning to other aspects of the business. This can reduce the customization/tuning and integration cycles. Additionally, it is also crucial to develop strong AI product development skills to better manage future ‘AI’ investments. Developing strong SMEs internally can accelerate adoption and deliver better business outcomes.

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