This is the year in which a new mindset will take hold—and large financial services companies better be ready to embrace this new way of thinking.

We’ve seen the arrival of countless startups in the industry that have the capability to transform the sector. In response, some incumbents are launching innovation labs and partnerships, while others are taking a wait-and-see stance.

Whatever the approach, we believe that an “evolution-revolution” strategy is one that all incumbents should consider. With this strategy, financial services organizations must evolve to participate in the revolution taking place in the industry.

Large, established companies can’t act like small start-ups. Instead they need to deploy new technologies in ways that enhance their existing technology—in an evolutionary way—so they can benefit from the revolution. If they do, because of their size and market influence, they’ll be able to thrive as these new technologies become operationalized.

Four key trends are driving the need for this new approach in the industry.

  1. Consumers expectations about personalized service and responsiveness have changed, and all companies must reach this new level to compete effectively. All consumers have become accustom to outstanding service levels from the likes of Amazon, Uber, Airbnb, and others. Financial institutions must be able to reach these new service levels, which will require investment in technology and analytics, and in some cases, a new strategy.
  2. The emergence of new types of data and a rise in the volume of this data. This includes data generated by social media, telematics, connected cars, the Internet of Things, and other sources. All of it is relevant to financial institutions. There are also new ways to analyze this data—as well as traditional data—such as machine learning.
  3. Compliance with new regulations related to the protection of data. We are seeing very active regulators who, on the one hand, are introducing regulations like PSD2, that enable, or even encourage, disruption to the established industry, and on the other hand, regulators putting a “tighter collar” on the established industry with new regulations like GDPR and the likes.
  4. New Fintech/InsurTech disruptors. The emergence of thousands of startups that are disrupting the industry. They’re all using the latest and greatest technologies and data, and they represent a real competitive threat to established financial services firms.

Together these four forces are creating a revolution in the marketplace. The question is not whether the revolution will happen; it’s happening already. The question is what should companies be doing about it?

Some insist that this is all noise and hype, and say the proof is that none of these new disruptors are actually successful or creating a dent in the business of the established industry. That might be true today. But the same was said about Amazon and Uber when they first emerged and look what they’ve managed to achieve in their markets.

These naysayers think it makes sense to continue doing what they’re doing, but that’s not a good idea. Others are trying to do everything differently in order to change with the times—also not a good idea. Financial services companies need to ask themselves some key questions:

  • Can we really throw out our old models and replace them with models based only on telematics, IOT and other new data sources?
  • Can we afford to ignore the value that new forms of data can bring to our models and analytics?
  • Are linear regression models dead and from now on all our analytics should be based on machine learning?
  • Can we afford to ignore the value that machine learning can deliver?

The answer to all these questions should be “NO”. The issue isn’t whether to choose this approach or that approach. It’s about respecting the value you’ve been getting from traditional technologies, processes, data, and analytics, and at the same time embracing the new innovation that’s coming out.

This approach delivers the best of both words. It’s the essence of the evolution-revolution strategy: to weave the old and the new, the proven and the innovative, into a single coherent process.

A number of financial services use cases demonstrate how evolution-revolution works. One involves the problem of pricing in an aggregator market. Even if you have the best pricing analytics, unless you’re ranking one, two, or three, you probably will not be selling. Rank optimization is something that’s impossible to do using traditional statistical models, and is best done using machine learning.

So, do you drop your traditional pricing analytics, which works, and replace it with machine learning rank optimization? No, you should augment traditional pricing analytics with competitive positioning based on machine learning. Our customers that have taken this approach are seeing great results in the market.

Another example is pricing for small commercials. Your underwriting process and pricing analytics both work great. But wouldn’t it bring even more value if you include some relevant web information? For example, what are the clients of this small commercial saying about the small commercial? Wouldn’t it influence your decision making, and make you smarter in how you run your business? Absolutely! So, once again the traditional should be weaved with the new.

These examples show how companies can embed proven techniques and traditional data with new types of data and new analytical techniques. All of this is becoming reality, and this is what Earnix is about: providing advanced analytics, pricing, and product solutions that identify the right product, at the right price, to each and every customer, driving significant business results, and helping our customers merge the traditional with the new. The evolution-revolution way.

The revolution is coming; it is probably already here; evolution-revolution is the best way to ride this wave and make it a significant business opportunity.