Current publications and recent interviews.
Priorities setting and decision making in uncertain times. Data Analytics, Big Data and AI promise banks and insurance companies a wealth of new answers and possibilities.
Sven Krämer, Partner, and Matthias Lehneis, Senior Manager, talk about the findings of their current publication "Data Analytics at Financial Service Providers" and why there is often a world of difference between inspiration and implementation.
If you would like to find out why Sleeping Beauty is not just a fairy tale, but part of the daily business of many banks and insurance companies, you´ll find out more in our second publication on DataAnalytics, where we explain how to reactivate customers who were thought lost.
At zeb, you - Mr. Krämer , Mr. Lehneis, caution your clients against blindly adopting ideas from other industries. Why?
Sven Krämer: There's nothing wrong with drawing inspiration from other industries or from technological advances. But we are observing two things in the financial services market: banks and insurers are leapfrogging from the inspiration stage directly to implementation. They’re investing in technology without identifying specific business problems that they want to solve with it. They’re leaving out important intermediate step – evaluation and prioritization. Tools like data analytics mean that every financial-services company can now address many of its own challenges and opportunities for the first time, or in ways better than before. As a first step, financial-services companies have to get a better understanding of their specific problems and, above all, what they expect in terms of solutions. They can then choose and implement one or several technological approaches that hit these marks.
That’s a clear warning against taking action for the sake of taking action. How to avoid this?
Matthias Lehneis: Banks and insurers must avoid making bad investments that are based on some sincere but diffuse enthusiasm for technology. We have developed criteria that allow companies to assess whether, say, a data-analytics application will more likely prove to be a success or a failure. Success is based on being able to precisely answer questions like: Does the tool under consideration address a concrete business problem? Do processes really need to be optimized, for example because existing tools are insufficient? Does the company have clear expectations about benefits and costs? In the course of advising many clients, zeb has developed an evaluation method that gauges the probability of success for any use-case.
Can you identify specific areas of the banking and insurance businesses in which data-driven applications are more likely to be successful? Are they the best place to start?
Sven Krämer: Yes, absolutely. Our experience shows that instruments that companies apply to internal processes are most readily and quickly successful – and they a have visible impact on the bottom line. Automation and optimization are the key terms. The advantage of focusing on internal processes is that the company can itself control most of the factors crucial for implementation. And everyone benefits from the resulting improvements – customers through faster processing speeds, employees through more manageable workloads, the companies through a more stable organization that is geared for growth.
Are there big data approaches being used in other industries that banks or insurance companies should avoid, or which should at least be treated with caution?
Matthias Lehneis: Solutions adopted in other industries do not always provide a one-to-one fir for the problems faced by banks or insurers. For example, the retail industry has for a long time been particularly focused on analyzing online shopping baskets. Retailers want to be able to make customers targeted product recommendations. The particular challenge here is to connect millions of different products with millions of different customers. But banks and insurance companies have a very different problem. Unlike retailers, they offer only a few products and instead have to tailor each one to each customer. They require very different analysis tools. Banks and insurers have to make sure they invest in the right solutions.