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The Algorithmic Insurer: Liberating and Leveraging Algorithms Ready for The Fourth Industrial Revolution

Blog: The Tibco Blog

As first appeared in Financial IT.


Having slipped almost unnoticed into the general consciousness, algorithms are being talked about everywhere and in relation to all manner of technologies and industries, from driverless cars to artificial intelligence. But just what are algorithms, why are they suddenly being hyped so much, and what are the implications for vertical markets like the insurance industry?

Same rules, different game

There’s nothing intrinsically complicated, mysterious, or even new about algorithms. Most of us will have learned about them at school and recognize an algorithm as a set of rules used to solve a problem and achieve a desired outcome in a consistent and repeatable manner.

A mathematical formula is a good example of an algorithm, as are recipes and computer programs, including those used to price and quote for new insurance business. In the case of motor insurance, for example, this would typically involve calculating risk, based on factors such as the customer’s age, address, and vehicle type, together with previous driving and claims history. Other algorithms would then be applied to give a price, according to the risk attitude of the insurers involved.

Originally designed for intermediary use, these applications have made a largely successful transition to the world of direct online selling. But that market continues to evolve and throw up new challenges, such as rapidly increasing transaction volumes which are slowing response times and putting applications under immense strain. At the same time, resources for calculating risk are proliferating and growing in complexity, with insurers increasingly looking to make use of private and public cloud services. Customer expectation is, similarly, changing as more channels for interaction open up.

The end result is an online insurance market powered by legacy applications which are no longer fit for purpose, with cracks already in evidence as insurers attempt to cope with this fast paced and rapidly evolving digital environment, who are calling for a fundamental re-think as to how the market can be better served.

Thinking inside the box

Historically, the insurance software market has been dominated by a handful of specialist vendors whose products can be both expensive to deploy and difficult to customize without the involvement of the IT department.

Operated by IT departments largely as “black box” solutions, leading insurance firms are effectively all using the same algorithms to assess and price risk, with little opportunity to fine tune the logic used or to do so quickly in order to, for example, react to market trends and legislative changes, or differentiate insurance products for competitive advantage.

Beyond that, the data analytics and algorithms applied by most legacy applications are, for the most part, basic and well behind the technology curve. The majority are designed to work purely with static data and are unable to handle multiple real-time data streams, or apply advanced predictive models to better understand and forecast possible risk. Added to which the black boxes are failing to add more advanced analytical capabilities, like those available through statistical languages such as R, which is rapidly becoming the tool of choice for data scientists.

Lastly, legacy insurance applications can be difficult to integrate, particularly into a modern IT fabric spanning a mix of public and private cloud, as well as on-premise platforms. That, in turn,  creates barriers when it comes to the automation and streamlining of business processes—a growing requirement as companies move towards wider digital transformation.

Of course the insurance sector isn’t the only industry experiencing these problems, with other sectors similarly reliant on applications that lock their algorithmic intelligence away inside a costly and mysterious black box. But change is coming; on two distinct fronts.

Thinking outside the ‘black’ box

The first change is all to do with unlocking the algorithms and making them available independently to the applications that use them. Driven partly by developers looking to monetize the intellectual property contained within their algorithms, this has led Gartner to describe what it calls the “Algorithm Economy”,[i] with developers increasingly packaging and trading algorithms, in much the same way they do mobile apps.

The second is the availability of what another analyst firm, Forrester, calls “Insight Platforms”[ii] able to use advanced data analytics to not just spot trends and deliver insight, but act on that insight and drive process execution. These platforms deliver the required tools in the form of simple building blocks which can be glued together using flexible ready-packaged or self-made algorithms, to build scalable, yet agile applications able to cope with the demands of modern digital business across a broad spectrum of industries.

Led by companies such as TIBCO, Insight Platforms are already enabling companies to take advantage of the growing algorithmic economy in order to build and manage applications themselves. Applications costing a fraction of legacy “black box” alternatives to not just solve business problems in the way the business wants and across the platforms it needs, but to do so under the full control of the business, at its own pace and in a format ready for the next move from algorithmic to autonomous trading.

Find out more by registering for TIBCO’s next webinar—The Algorithmic Insurer. Join TIBCO for this 40-minute webinar to discover how insurers can manage risk more accurately based on faster insights, increase agility by bringing more data into their pricing equations, and enhance the customer experience through real-time pricing. Register here.


[i] Gartner: Algorithm Economy
[ii] The Forrester Wave™: Enterprise Insight Platform Suites, Q4 2016

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