Banking organisations and insurance companies, financial services in short, are an industry that is both fast-paced and highly competitive. It’s no exaggeration to state that there is more data in FS than most other industries can even contemplate, let alone use in any meaningful sense. The volume of data is growing on a daily basis and many organisations are failing to use even a fraction of the data available to them.
In FS, technology is often used for automation of tasks and interactions. Where the goal isn’t to make staff more valuable, it is to replace them. That approach doesn’t really work in the relationship-driven environments, where the focus for technology is to help staff be more efficient and manage lucrative client relationships much more effectively.
Despite a lot of hype and press attention, artificial intelligence (AI) and machine learning (ML) are yet to truly reach their potential in FS. But it has qualities that make it the right kind of technology for corporate FS, and one that can deliver ROI by imparting the required insight to those that work in the industry.
At a recent insurance conference, I had the chance to speak to these challenges. One of the key asks was how FS organizations can do initial steps. The following diagram shows our take on this:
An AI-Driven Insurance Roadmap
Gradually unlocking the potential of Smart Data & Analytics
1 — Start — Get low-hanging fruits
At start it is a good idea to get immediate benefits delivered to key people in marketing and sales: Get a full client engagement view. Sourced from internal and external data sources a relationship manager get everything they need to know about a client relationship
Example: For a large Insurance company Squirro ties together disparate data sources to show an underwriter a complete customer engagement view. In addition the underwriter gets to see automatically the full risk exposure. In many insurance and re-insurance companies this type of information is today still locked up in contracts or other accompanying documentation and not easily available in a structured format for consumption by an underwriter.
2 — Scale Up — Source new business
While step 1 is about producing insights to better understand a current customer relationship the next level is about a pro-active use of data assets to source new business. Deploying AI-driven technologies internal and external data sets are used to identify new business opportunities.
Example: For a large bank Squirro helps sourcing commercial real estate opportunities. The solution scans automatically bank internal and external sources for patterns that indicate a movement in the marketplace about e.g. an upcoming sales, re-development, or other. The result: A significant expansion of the deal pipeline worth >$100m within 30 days. The trick: None, simply a more comprehensive approach at doing more with available data assets.
3 — Future — Personalized data apps
A next step applies data and technologies to deliver a personalized experience built on data. In a world driven by data a personalized user experience makes the difference.
These are just brief descriptions of each step. We’ve seen companies deploying these steps successfully with significant top line impact. Get in touch for more details.
