Challenge
One of our Swiss banking clients asked us to work with them to develop a prototype of a fully integrated end-to-end data-driven recommender platform. This should serve as a showcase to demonstrate the potential of data science and AI to potential internal end customers.
The bank’s management set expectations for data-driven innovation and new technologies. The task was to position the bank as the best digital bank in the country. In general, it is a challenge for all financial institutions today to address young generations due to their specific requirements for digitalisation. The goal of the use case in the prototype was to educate young people in the use of money through gamification and to create individual product recommendations based on peer group analyses.
Result
Every major bank that has BigData is already actively doing projects in the area of its analysis and use. This helps not only banks but also companies in general to better understand which customer journey end users go through and how best to accompany them on this journey. In the case of the customer, the aim was to pick up the end user who had just come of age, to identify his challenges and to select the appropriate content automatically on the basis of the different needs on the customer journey and to deal with them in a targeted manner.
The developed prototype, which is already available, is fully automatic. This means that personal recommendations are always made based on specific queries, without manual intervention. The logic is contained in complex algorithms, which had not yet been used in this form. It took about 5 months from the initial discussions to the development of the prototype. The project itself involved a local team in Switzerland as well as a UX team in Manila, which developed the entire frontend. Currently we are in discussions to provide another showcase and to further develop the platform for a productive use.
The potential of this platform lies in its scalability and agility. The prototype based on the gamification factor has the following modules:
- WebApp interface for interaction with the customer
- Recommender Engine for recommendations& comparisons
- Chatbot for consulting & communication
- Data models
What actually makes this prototype so unique? The customer can have complementary tools to the existing marketing (campaigns &lead management), test how customers react to “surprise recommendations”, try to do direct marketing via influencers (place the recommendation with the influencer and spread it through him to his network) and experience
many other functionalities.
Customer statement
“The cooperation was very constructive, solution-oriented and administratively uncomplicated. Also the integration of near– or off-shore employees was implemented very professionally and without unnecessary bureaucracy.”
Head of Financial & Analytics Services at a financial services provider
This article was featured in .experience magazine. If you want to be notified when the next issue is released, click on the banner below:
