Today’s economy is dominated by the use of data and digital technologies. The financial sector is no exception, as it largely deals with the management of information. Banks and insurance companies can leverage data and AI to enhance and personalize their product and service offerings. Often, the more customers considered in the process and analyzed with machine learning algorithms, the better the results. However, data is never sufficient. Sometimes certain phenomena are not well represented, such as specific customer categories. Or there may not be enough historical data, particularly when considering the propensity to purchase an entirely new product. Alternatively, the data in company databases may be of poor quality and limited variety. The possibilities are diverse. The solution to these challenges is called “data coopetition.”
When competitors decide to collaborate on certain aspects, such as sharing data, it is referred to as “coopetition.” To understand the power and significance of data coopetition, consider the field of medical and pharmaceutical research, where discoveries can save lives. Although less dramatic, the financial sector can also benefit from data coopetition.
Compelling reasons to embrace data coopetition:
It all sounds good, but the fear of competitors gaining access to customer data or other sensitive information is the primary deterrent to data coopetition. Losing control of data and sharing information that should remain within a company’s perimeter is a nightmare for many businesses, especially in a regulated sector like finance. However, solutions exist.
Cross-Silo Federated Learning is a decentralized machine learning paradigm, a form of data coopetition in which: • Participants do not share their input data (often anonymous) with full privacy protection. • They do share a global model that capitalizes on the information from cooperating participants, resulting in better service for all. The clever architecture involves each participant keeping their data, downloading an encrypted version of the algorithm, training it on their data, and then sharing the improved model. All of this is orchestrated by a central server that never sees participants’ data, only the progressively improved models. When combined with Differential Privacy, there are no security or privacy issues.
Those who adopt secure data analytics solutions based on a form of coopetition will achieve superior results compared to those who exclusively follow the “proprietary” route out of fear of competition.”
At Wealthype, this is precisely what we focus on, and we have already helped thousands of advisors improve their work. For a demo of our tools, you can contact us using the link below and discover how to apply Machine Learning principles to your business processes.